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Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies

Open AccessPublished:November 01, 2022DOI:https://doi.org/10.3168/jds.2022-21923

      ABSTRACT

      Mastitis, the most frequent disease in dairy cattle. Resistance to mastitis is a complex, polygenic trait controlled by several genes, each with small effects. Genome-wide association studies have been widely used to identify genomic variants associated with complex traits, including resistance to mastitis, to elucidate the underlying genetic architecture of the trait. However, no systematic review and gene prioritization analysis have been conducted to date on GWAS results for resistance to mastitis in dairy cattle. Hence, the objective was to perform a systematic review and gene prioritization analysis of GWAS studies to identify potential functional candidate genes associated with resistance to mastitis-related traits in dairy cattle. Four electronic databases were searched from inception to December 2020, supplemented with multiple sources of gray literature, to identify eligible articles. Annotation for genes and quantitative trait loci (QTL), and QTL enrichment analysis were conducted using GALLO. Gene prioritization analysis was performed by a guilty-by-association approach using GUILDify and ToppGene. From 52 articles included within this systematic review, 30 articles were used for further functional analyses. Gene and QTL annotation resulted in 9,125 and 43,646 unique genes and QTL, respectively, from 39 studies. In general, overlapping of genes across studies was very low (mean ± SD = 0.02% ± 0.07%). Most annotated genes were associated with somatic cell count-related traits and the Holstein breed. Within all annotated genes, 74 genes were shared among Holstein, Jersey, and Ayrshire breeds. Approximately 7.5% of annotated QTL were related to QTL class “health.” Within the health QTL class, 2.6 and 2.2% of QTL were associated with clinical mastitis and somatic cell count-related traits. Enrichment analysis of QTL demonstrated that many enriched QTL were associated with somatic cell score located in Bos taurus autosomes 5, 6, 16, and 20. The prioritization analysis resulted in 427 significant genes after multiple test correction (false discovery rate of 5%) from 26 studies. Most prioritized genes were located in Bos taurus autosomes 19 and 7, and most top-ranked genes were from the cytokine superfamily (e.g., chemokines, interleukins, transforming growth factors, and tumor necrosis factor genes). Although most prioritized genes (397) were associated with somatic cell count-related traits, only 54 genes were associated with clinical mastitis-related traits. Twenty-four genes (ABCC9, ACHE, ADCYAP1, ARC, BCL2L1, CDKN1A, EPO, GABBR2, GDNF, GNRHR, IKBKE, JAG1, KCNJ8, KCNQ1, LIFR, MC3R, MYOZ3, NFKB1, OSMR, PPP3CA, PRLR, SHARPIN, SLC1A3, and TNFRSF25) were reported for both somatic cell count and clinical mastitis-related traits. Prioritized genes were mainly associated with immune response, regulation of secretion, locomotion, cell proliferation, and development. In conclusion, this study provided a fine-mapping of previously identified genomic regions associated with resistance to mastitis and identified key functional candidate genes for resistance to mastitis, which can be used to develop enhanced genomic strategies to combat mastitis by increasing mastitis resistance through genetic selection.

      Key words

      INTRODUCTION

      Mastitis is the most common and expensive infectious disease in dairy cattle (
      • Halasa T.
      • Huijps K.
      • Østerås O.
      • Hogeveen H.
      Economic effects of bovine mastitis and mastitis management: A review.
      ). It is caused by IMI with pathogenic bacteria in the udder which, if not cleared, can manifest as subclinical mastitis (SCM) or clinical mastitis (CM). The prevalence of IMI ranges from 17 to 76% depending on lactation stage and parity (
      • Oliver S.P.
      • Almeida R.A.
      • Gillespie B.E.
      • Ivey S.J.
      • Moorehead H.
      • Lunn P.
      • Dowlen H.H.
      • Johnson D.L.
      • Lamar K.C.
      Efficacy of extended pirlimycin therapy for treatment of experimentally induced Streptococcus uberis intramammary infections in lactating dairy cattle.
      ;
      • Piepers S.
      • De Meulemeester L.
      • de Kruif A.
      • Opsomer G.
      • Barkema H.W.
      • De Vliegher S.
      Prevalence and distribution of mastitis pathogens in subclinically infected dairy cows in Flanders, Belgium.
      ;
      • Narayana S.G.
      • Schenkel F.
      • Miglior F.
      • Chud T.
      • Abdalla E.A.
      • Naqvi S.A.
      • Malchiodi F.
      • Barkema H.W.
      Genetic analysis of pathogen-specific intramammary infection in dairy cows.
      ), whereas the prevalence of SCM in first-parity Canadian Holstein heifers in early lactation ranges from 13 to 24% (
      • Narayana S.G.
      • Miglior F.
      • Naqvi S.A.
      • Malchiodi F.
      • Martin P.
      • Barkema H.W.
      Genetic analysis of subclinical mastitis in early lactation of heifers using both linear and threshold models.
      ). Incidence rate of CM ranges from 23.7 cases per 100 cows per year in Canada (
      • Levison L.J.
      • Miller-Cushon E.K.
      • Tucker A.L.
      • Bergeron R.
      • Leslie K.E.
      • Barkema H.W.
      • DeVries T.J.
      Incidence rate of pathogen-specific clinical mastitis on conventional and organic Canadian dairy farms.
      ), to 32.5 cases per 100 cows per year in the Netherlands (
      • Santman-Berends I.M.G.A.
      • Lam T.J.G.M.
      • Keurentjes J.
      • van Schaik G.
      An estimation of the clinical mastitis incidence per 100 cows per year based on routinely collected herd data.
      ), and 39.6 cases per 100 cows per year on large dairy farms in China (
      • Gao J.
      • Barkema H.W.
      • Zhang L.
      • Liu G.
      • Deng Z.
      • Cai L.
      • Shan R.
      • Zhang S.
      • Zou J.
      • Kastelic J.P.
      • Han B.
      Incidence of clinical mastitis and distribution of pathogens on large Chinese dairy farms.
      ). Mastitis can affect animal welfare and farm profitability through reduced milk yield, milk quality, and treatment costs (
      • Halasa T.
      • Huijps K.
      • Østerås O.
      • Hogeveen H.
      Economic effects of bovine mastitis and mastitis management: A review.
      ). Costs associated with mastitis have been estimated at Can$66,178/100 cows per year for a typical Canadian dairy farm (
      • Aghamohammadi M.
      • Haine D.
      • Kelton D.F.
      • Barkema H.W.
      • Hogeveen H.
      • Keefe G.P.
      • Dufour S.
      Herd-level mastitis-associated costs on Canadian dairy farms.
      ).
      Effectiveness of traditional methods to control mastitis (e.g., antimicrobial therapy and disinfectants) is relatively low (
      • Pyörälä S.
      New strategies to prevent mastitis.
      ). Therefore, selective genetic breeding for enhanced mastitis resistance is a potential strategy to control mastitis, together with better management practices. Genetic improvement is cumulative and results in permanent and cost-efficient changes. However, the success of genetic selection of a trait depends on its heritability and additive genetic variation. Because the heritability of resistance to mastitis is low (≤0.05;
      • Koeck A.
      • Miglior F.
      • Kelton D.F.
      • Schenkel F.S.
      Health recording in Canadian Holsteins: Data and genetic parameters.
      ;
      • Narayana S.G.
      • Miglior F.
      • Naqvi S.A.
      • Malchiodi F.
      • Martin P.
      • Barkema H.W.
      Genetic analysis of subclinical mastitis in early lactation of heifers using both linear and threshold models.
      ,
      • Narayana S.G.
      • Schenkel F.
      • Miglior F.
      • Chud T.
      • Abdalla E.A.
      • Naqvi S.A.
      • Malchiodi F.
      • Barkema H.W.
      Genetic analysis of pathogen-specific intramammary infection in dairy cows.
      ), genomics provides exceptional opportunities to increase the frequency of favorable alleles in the population, compared with conventional pedigree-based breeding. This was demonstrated by an increase in genetic gain of 0.46 (standard units) when comparing the past 5 years to the 5 years before introduction of genomic selection for mastitis resistance (
      • Canadian Dairy Network
      10 Years of genomic selection: What's next?.
      ). In addition, identifying genomic variants associated with mastitis resistance aids in understanding its genetic architecture (
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      ).
      Genome-wide association studies are advanced techniques commonly used to detect significant genetic markers (SNPs, haplotypes) associated with complex traits, resulting in identification of QTL and candidate genes. Resistance to mastitis is a complex and polygenic trait controlled by several genes, each with small effects (
      • Rupp R.
      • Boichard D.
      Genetics of resistance to mastitis in dairy cattle.
      ;
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      ). Therefore, numerous studies have performed GWAS of CM (
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      ;
      • Welderufael B.G.
      • Løvendahl P.
      • de Koning D.J.
      • Janss L.L.G.
      • Fikse W.F.
      Genome-wide association study for susceptibility to and recoverability from mastitis in Danish Holstein cows.
      ;
      • Kurz J.P.
      • Yang Z.
      • Weiss R.B.
      • Wilson D.J.
      • Rood K.A.
      • Liu G.E.
      • Wang Z.D.
      A genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.
      ) and SCC, or its log-transformation SCS, an indicator trait of SCM (
      • Durán Aguilar M.
      • Roman Ponce S.I.
      • Ruiz Lopez F.J.
      • Gonzalez Padilla E.
      • Vasquez Pelaez C.G.
      • Bagnato A.
      • Strillacci M.G.
      Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers.
      ; Oliveira et al., 2019) to identify genetic variants associated with resistance to mastitis and to understand its architecture. Several different as well as a few similar genetic variants associated with CM and SCC-related traits have been reported. However, these genetic variants depended on the phenotype, population, breed, genotyping, and GWAS approach used for the analysis. This has resulted in a long list of positional candidate genes associated with resistance to mastitis, although only a few genes have been validated (
      • Leyva-Baca I.
      • Schenkel F.
      • Sharma B.S.
      • Jansen G.B.
      • Karrow N.A.
      Identification of single nucleotide polymorphisms in the bovine CCL2, IL8, CCR2 and IL8RA genes and their association with health and production in Canadian Holsteins.
      ;
      • Chen X.
      • Cheng Z.
      • Zhang S.
      • Werling D.
      • Wathes D.C.
      Combining genome wide association studies and differential gene expression data analyses identifies candidate genes affecting mastitis caused by two different pathogens in the dairy cow.
      ). In the absence of appropriate prioritization analysis, a long list of candidate genes causes pitfalls in confirming potential functional candidate genes. A review of reported SNPs associated with SCC, using 7 published GWAS studies (
      • Chen X.
      • Cheng Z.
      • Zhang S.
      • Werling D.
      • Wathes D.C.
      Combining genome wide association studies and differential gene expression data analyses identifies candidate genes affecting mastitis caused by two different pathogens in the dairy cow.
      ), did not use a systematic approach to select relevant literature and there were no formal comparisons between reported genes. More recently, several other studies have conducted GWAS and reported novel and similar genomic variants associated with resistance to mastitis. Therefore, there is a need for a systematic review of GWAS on resistance to mastitis and prioritization analyses to pinpoint potential functional candidate genes to improve mastitis resistance through genetic selection.
      To our knowledge, there are no formal systematic reviews nor gene prioritization analyses on this topic. Given this knowledge gap, our objectives were to: (1) conduct a systematic review of GWAS on resistance to mastitis; and (2) perform gene prioritization analysis of GWAS results of resistance to mastitis to identify potential functional candidate genes.

      MATERIALS AND METHODS

      No human or animal subjects were used, so this analysis did not require approval by an Institutional Animal Care and Use Committee or Institutional Review Board.

      Systematic Review Search Strategy

      This systematic review followed a pre-specified study protocol and the standards of preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement (
      • Page M.J.
      • McKenzie J.E.
      • Bossuyt P.M.
      • Boutron I.
      • Hoffmann T.C.
      • Mulrow C.D.
      • Shamseer L.
      • Tetzlaff J.M.
      • Akl E.A.
      • Brennan S.E.
      • Chou R.
      • Glanville J.
      • Grimshaw J.M.
      • Hróbjartsson A.
      • Lalu M.M.
      • Li T.
      • Loder E. W
      • Mayo-Wilson E.
      • McDonald S.
      • McGuinness L.A.
      • Stewart L.A.
      • Thomas J.
      • Tricco A.C.
      • Welch V.A.
      • Whiting P.
      • Moher D.
      The PRISMA 2020 statement: An updated guideline for reporting systematic reviews.
      ). A combination of online databases and conference proceedings were searched to identify relevant literature. Online databases searched included CAB Abstracts (EBSCO), MEDLINE (OVID), Web of Science, and BIOSIS (Web of Science) from the database inception to December, 2020, with no language restrictions. Search terms consisted of both keywords and database-specific subject headings for the elements of the PPO framework: population – dairy cattle; prognostic tool – GWAS; and outcome – mastitis or SCC-related traits.
      Identifiers and synonyms for each framework element were combined using the Boolean operator “OR.” Thereafter, the elements of the framework were combined using the Boolean operator “AND.” The specific search terms used for CAB Abstracts, MEDLINE, Web of Science, and BIOSIS are displayed in Supplementary Material 1 ( https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ). Twenty-seven relevant articles identified using Google Scholar were used as a test set to check the recall of the search strategy. In addition, hand searches were conducted (from 2005 onward) of the following conference proceedings: Plant and Animal Genome Conference (PAG); World Congress on Genetics Applied to Livestock Production (WGALP); and International Conference of Quantitative Genetics (ICQG). Searches were also conducted (from 2005 onward) for proceedings of the following meetings: American Dairy Science Association (ADSA); National Mastitis Council (NMC); International Society for Animal Genetics (ISAG); and European Federation of Animal Science (EAAP). All identified articles were imported into Covidence (Covidence systematic review software, Veritas Health Innovation) and duplicates removed. Each abstract was independently screened by Saranya Narayana and Ellen de Jong. Abstracts were selected based on the following criteria: (1) original work (review articles, editorials, and commentaries were excluded); (2) population of interest: dairy cattle; and (3) mentioning of variants such as SNPs, sequences, copy number variation, and haplotypes used to conduct GWAS. During full-text review, articles were excluded if: (4) a GWAS technique was not used for the analysis; or (5) phenotypes defined did not include resistance to mastitis or SCC-related traits. Any disagreement between the 2 reviewers (Saranya Narayana and Ellen de Jong) regarding inclusion of an abstract or full-text was resolved by consensus after discussion. The included articles were further screened for relevant references and a citation check was performed. If the full-text article was missing, or there was uncertainty regarding duplicates (results presented in both a conference and published work), the lead author was contacted. For each included full-text study, Microsoft Excel was used to organize the following data: phenotype, genotype, SNP selection method, GWAS approach, identified significant (as reported by study) genomic variants (SNPs or copy number variations or haplotype) and their coordinates, SNP effects, standard errors, and genes.

      Annotation for Genes and QTL

      Conversion of Genomic Coordinates to ARS-UCD1.2

      Genomic coordinates of associated markers and windows extracted from the included articles were converted to new bovine genome assembly ARS-UCD1.2 (
      • Medrano J.F.
      The new bovine reference assembly and its value for genomic research.
      ;
      • Rosen B.D.
      • Bickhart D.M.
      • Schnabel R.D.
      • Koren S.
      • Elsik C.G.
      • Tseng E.
      • Rowan T.N.
      • Low W.Y.
      • Zimin A.
      • Couldrey C.
      • Hall R.
      • Li W.
      • Rhie A.
      • Ghurye J.
      • McKay S.D.
      • Thibaud-Nissen F.
      • Hoffman J.
      • Murdoch B.M.
      • Snelling W.M.
      • McDaneld T.G.
      • Hammond J.A.
      • Schwartz J.C.
      • Nandolo W.
      • Hagen D.E.
      • Dreischer C.
      • Schultheiss S.J.
      • Schroeder S.G.
      • M Phillippy A.
      • Cole J.B.
      • Tassell C.P.V.
      • Liu G.
      • Smith T.P.L.
      • Medrano J.F.
      De novo assembly of the cattle reference genome with single-molecule sequencing.
      ) using Lift Genome Annotations, animalgenome.org (ARS-UCD1.2 Cow Genome Assembly: Mapping of all existing variants) and European Variation Archive, depending on available information. Because most studies did not report significant markers or windows per parity related to resistance to mastitis traits, duplicate markers or windows per parity per trait were deleted. Significant markers or windows with unknown chromosome and with negative positions were also deleted. Following conversion, if the start position of a SNP was higher than the end position, the orientation of coordinates was reversed for windows. If a study had used UMD 3.1 Bos taurus assembly, it was converted to UMD 3.1.1 Bos taurus assembly before converting to ARS-UCD1.2 in Lift Genome Annotations.

      Annotation of Markers and Windows

      After all genomic coordinates were retrieved, genes and QTL mapped within 0.1 Mb interval of markers and windows were annotated using GALLO (
      • Fonseca P.A.S.
      • Suárez-Vega A.
      • Marras G.
      • Cánovas A.
      GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci.
      ). Markers and windows were annotated for genes and QTL separately. Subsequently, annotated genes and QTL from markers and windows were combined to obtain unique genes and QTL that were used for further analysis. Gene and QTL coordinates of the new bovine assembly ARS-UCD1.2 were obtained from Ensembl.org and Animal QTLdb, respectively. Following this step, lists of unique genes identified in each article were compared, and proportions of shared unique genes between studies and traits estimated. Annotated QTL information were used to estimate the percentage of QTL type in respective QTL classes. Moreover, QTL enrichment analysis was performed for each trait individually and at a chromosome-wise level in the GALLO package using a hypergeometric test.

      Gene Prioritization Analysis

      A “guilt by association” based gene prioritization analysis (
      • Walker M.G.
      • Volkmuth W.
      • Sprinzak E.
      • Hodgson D.
      • Klingler T.
      Prediction of gene function by genome-scale expression analysis: Prostate cancer-associated genes.
      ) was conducted using “trained” (genes obtained from GUILDify software;
      • Guney E.
      • Garcia-Garcia J.
      • Oliva B.
      GUILDify: A web server for phenotypic characterization of genes through biological data integration and network-based prioritization algorithms.
      ;
      • Aguirre-Plans J.
      • Piñero J.
      • Sanz F.
      • Furlong L.I.
      • Fernandez-Fuentes N.
      • Oliva B.
      • Guney E.
      GUILDify v2.0: A tool to identify molecular networks underlying human diseases, their comorbidities and their druggable targets.
      ) and “test” genes' list (positional candidate genes derived from annotation of chosen articles from the systematic review) using GUILDify and ToppGene. The top 100 ranked trained genes (after removing the 36 common genes with test gene list, GUILD score ≥0.7322) were obtained from GUILDify v2.0 Web Server. GUILDify uses a Biologic Interaction and Network Analysis (BIANA) knowledge database to retrieve genes associated with the outcome (mastitis or SCC-related traits), using given keywords related to the outcome. Keywords used in the present study were; “mastitis,” “subclinical mastitis,” “somatic cell count,” “somatic cell score,” “SCC,” “SCS,” “intramammary infection,” “lactation,” “milk,” and “mammary gland.” Subsequently, GUILDify uses selected genes and species-specific (Homo sapiens) protein interaction network and applies graph theory algorithms to prioritize genes.
      Only genes annotated from studies with ≥400 samples were used for the prioritization analysis to minimize a potential sample size effect. To obtain the test gene list, first, for the positional candidate genes without gene name, human orthologs in Ensembl database 106 were used to derive gene names. If the percentage of id. query gene identical to target human was ≥70%, the human gene name was used for further analyses. Second, final sets of test and trained gene lists were simultaneously used in ToppGene (
      • Chen J.
      • Bardes E.E.
      • Aronow B.J.
      • Jegga A.G.
      ToppGene Suite for gene list enrichment analysis and candidate gene prioritization.
      ), which performs functional annotation-based prioritization analysis using a fuzzy-based multivariate approach to estimate the relationship between any 2 genes, based on semantic annotations. Multivariate analyses were performed using functional information shared between test and trained gene sets derived from various sources including: Gene Ontology (GO) terms for molecular function (MF), biological process (BP), and cellular component (CC); human and mouse phenotypes; metabolic pathways; PubMed publications; co-expression pattern; and diseases. An overall P-value per functional candidate was estimated using a statistical meta-analysis where P-values for each functional annotation of each gene were combined into a final P-value. Finally, prioritized genes were selected after false discovery rate (FDR) multiple correction of 5% from significant genes.

      Gene Ontology, Metabolic Pathway, and Gene Network Analyses

      Gene ontology and metabolic pathway analyses for Bos taurus were performed using Over-Representation analysis in WebGestalt (
      • Liao Y.
      • Wang J.
      • Jaehnig E.J.
      • Shi Z.
      • Zhang B.
      WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs.
      ) for prioritized genes. Gene ontology analyses were performed for 3 GO term categories: BP, MF, and CC using a nonredundant function database. Additionally, metabolic pathway analyses were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway within the WebGestalt database. Gene network analyses were conducted for the list of prioritized genes using STRING database (
      • Szklarczyk D.
      • Franceschini A.
      • Wyder S.
      • Forslund K.
      • Heller D.
      • Huerta-Cepas J.
      • Simonovic M.
      • Roth A.
      • Santos A.
      • Tsafou K.P.
      • Kuhn M.
      • Bork P.
      • Jensen L.J.
      • von Mering C.
      STRING v10: protein-protein interaction networks, integrated over the tree of life.
      ) using co-expression as an interaction source with a medium confidence (0.400) interaction score. The resulting network was imported into Cytoscape 3.9.0 (
      • Shannon P.
      • Markiel A.
      • Ozier O.
      • Baliga N.S.
      • Wang J.T.
      • Ramage D.
      • Amin N.
      • Schwikowski B.
      • Ideker T.
      Cytoscape: a software environment for integrated models of biomolecular interaction networks.
      ), and the Cytoscape network analyzer plugin with undirected network criteria was used for network analyses. Key genes (nodes) were selected from proteins with degree ≥8, betweenness centrality ≥0.28, and closeness centrality ≥0.62.

      RESULTS

      Systematic Review

      A summary of the search strategy is displayed in Figure 1. A total of 4,877 studies were identified from the databases (CAB Abstracts, MEDLINE, Web of Science and BIOSIS) and gray literature (115) search. After exclusion of duplicates (n = 2,828), 2,164 abstracts were screened separately by Saranya Narayana and Ellen de Jong from which 1,471 studies were excluded. A total of 693 studies were included in the full-text review, where 641 were excluded due to facts such as: not original study, duplication, analyzed population was not dairy cattle, outcome was not resistance to mastitis, did not use GWAS, or did not have full-text available. In total, 52 unique publications met our inclusion criteria. Descriptive details of these 52 articles, including country, breed and phenotype (Table 1), were extracted. Most of the studies were from Europe (34 studies) and North America (11 studies). Of the 52 manuscripts, 38 and 40 reported on studies using SCC-related traits as phenotype and Holstein breed, respectively, for GWAS analysis. We also extracted other details regarding genotyping (SNP chip, assembly), quality control summary (call rate, minor allele frequency and Hardy-Weinberg threshold; Table 2), and GWAS summary(e.g., approach used for GWAS, number of animals, and SNPs used for GWAS, and significant SNPs per windows identified; Table 3). Most of the studies used 50K SNP chip (31 studies) with 3 different versions. Of 52 manuscripts, 27 and 7 studies used assembly UMD 3.1 and UMD 3.1.1, respectively, whereas only 2 studies used the new bovine assembly ARS-UCD 1.2. Number of animals used for GWAS ranged from 34 to 293,467.
      Figure thumbnail gr1
      Figure 1Preferred reporting items for systematic reviews and meta-analyses (PRISMA) flow diagram of search strategy.
      Table 1Origin of data, breed, and phenotype for 52 articles used for the analyses
      ManuscriptCountry
      Nordic = Denmark, Sweden, Finland.
      BreedPhenotype
      CM = clinical mastitis; DYD = daughter-yield deviations; YD = yield deviations; DEBV = deregressed EBV; and DPTA = deregressed PTA.
      • Abdel-Shafy H.
      • Bortfeldt R.H.
      • Tetens J.
      • Brockmann G.A.
      Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle.
      GermanyHolsteinDYD of SCS, calculated across first 3 lactations (
      • Liu Z.
      • Reinhardt F.
      • Bünger A.
      • Reents R.
      Derivation and calculation of approximate reliabilities and daughter yield-deviations of a random regression test-day model for genetic evaluation of dairy cattle.
      )
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.
      Nordic countriesHolsteinDEBV of CM resistance, calculated as a weighted score over first 3 lactations
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle.
      Nordic countriesHolsteinDEBV of udder health (combination of CM occurrence, SCC and udder conformation), calculated as a weighted score over first 3 lactations
      • Cecchinato A.
      • Macciotta N.P.P.
      • Mele M.
      • Tagliapietra F.
      • Schiavon S.
      • Bittante G.
      • Pegolo S.
      Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle.
      ItalyBrown SwissUdder health, a combination of lactose content and SCS (
      • Mele M.
      • Macciotta N.P.P.
      • Cecchinato A.
      • Conte G.
      • Schiavon S.
      • Bittante G.
      Multivariate factor analysis of detailed milk fatty acid profile: Effects of dairy system, feeding, herd, parity, and stage of lactation.
      )
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      United StatesHolsteinPTA of SCS
      • Durán Aguilar M.
      • Roman Ponce S.I.
      • Ruiz Lopez F.J.
      • Gonzalez Padilla E.
      • Vasquez Pelaez C.G.
      • Bagnato A.
      • Strillacci M.G.
      Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers.
      MexicoHolsteinEBV of SCS, from phenotypic extreme animals only (2 SD above and below mean)
      • Fang L.
      • Sahana G.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      integrating sequence-based GWAS and RNA-seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle.
      Nordic countriesHolstein, Jersey, Norwegian RedDEBV of CM resistance, calculated as a weighted score over first 3 lactations
      • Fang L.
      • Sahana G.
      • Ma P.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.
      Nordic countriesHolstein, JerseyDEBV of CM resistance, calculated as a weighted score over first 3 lactations
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      Nordic countriesHolstein, Jersey, Norwegian RedDEBV of CM resistance, calculated as a weighted score over first 3 lactations
      Fang et al., 2019Austria, Germany, SwitzerlandBrown SwissEBV of SCS
      • Freebern E.
      • Santos D.J.A.
      • Fang L.
      • Jiang J.
      • Parker Gaddis K.L.
      • Liu G.E.
      • VanRaden P.M.
      • Maltecca C.
      • Cole J.B.
      • Ma L.
      GWAS and fine-mapping of livability and six disease traits in Holstein cattle.
      United StatesHolsteinDPTA of CM, calculated according to
      • Garrick D.J.
      • Taylor J.F.
      • Fernando R.L.
      Deregressing estimated breeding values and weighting information for genomic regression analyses.
      • Guo J.
      • Jorjani H.
      • Carlborg Ö.
      A genome-wide association study using international breeding-evaluation data identifies major loci affecting production traits and stature in the Brown Swiss cattle breed.
      Austria, Switzerland, Germany, France, Italy, Slovenia, United StatesBrown SwissDEBV of SCS
      • Iung L.H.S.
      • Petrini J.
      • Ramírez-Díaz J.
      • Salvian M.
      • Rovadoscki G.A.
      • Pilonetto F.
      • Dauria B.D.
      • Machado P.F.
      • Coutinho L.L.
      • Wiggans G.R.
      • Mourão G.B.
      Genome-wide association study for milk production traits in a Brazilian Holstein population.
      BrazilHolsteinSCS of cows ranging from first to sixth parity
      • Jiang J.C.
      • Ma L.
      • Prakapenka D.
      • VanRaden P.M.
      • Cole J.B.
      • Da Y.
      A large-scale genome-wide association study in US Holstein cattle.
      United StatesHolsteinSCS of first lactation cows
      • Kirsanova E.
      • Heringstad B.
      • Lewandowska-Sabat A.
      • Olsaker I.
      Identification of candidate genes affecting chronic subclinical mastitis in Norwegian Red cattle: Combining genome-wide association study, topologically associated domains and pathway enrichment analysis.
      NorwayNorwegian RedDYD of lactation average SCS and 9 SCM traits as defined by
      • Kirsanova E.
      • Heringstad B.
      • Lewandowska-Sabat A.
      • Olsaker I.
      Alternative subclinical mastitis traits for genetic evaluation in dairy cattle.
      , calculated across first 3 lactations
      • Kurz J.P.
      • Yang Z.
      • Weiss R.B.
      • Wilson D.J.
      • Rood K.A.
      • Liu G.E.
      • Wang Z.D.
      A genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.
      United StatesHolsteinMastitis resistant (absence of CM and SCM) and susceptible (at least 4 CM cases), cows were monitored for 8 mo
      • Lu H.
      • Wang Y.
      • Bovenhuis H.
      Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle.
      The NetherlandsHolsteinSCS of first lactation cows
      • Ma P.
      • Lund M.S.
      • Aamand G.P.
      • Su G.
      Use of a Bayesian model including QTL markers increases prediction reliability when tests animals are distant from the reference population.
      Germany, France, the Netherlands, NordicHolsteinDEBV of mastitis
      • Macciotta N.P.P.
      • Gaspa G.
      • Bomba L.
      • Vicario D.
      • Dimauro C.
      • Cellesi M.
      • Ajmone-Marsan P.
      Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel.
      ItalySimmentalSCS, indicated by 2 variables per lactation (via principal component analysis) to reflect lactation pattern
      • Marete A.
      • Lund M.S.
      • Boichard D.
      • Ramayo-Caldas Y.
      A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds.
      FranceHolstein (SCS only), Normande, MontbéliardeYD of SCS and CM, calculated over the first 3 lactations
      • Marete A.
      • Sahana G.
      • Fritz S.
      • Lefebvre R.
      • Barbat A.
      • Lund M.S.
      • Guldbrandtsen B.
      • Boichard D.
      Genome-wide association study for milking speed in French Holstein cows.
      FranceHolsteinYD of SCS and CM, calculated over the first 3 lactations
      • Meier S.
      • Arends D.
      • Korkuc P.
      • Neumann G.B.
      • Brockmann G.A.
      A genome-wide association study for clinical mastitis in the dual-purpose German Black Pied cattle breed.
      GermanyGerman Black PiedCM present or absent, during first 3 lactations
      • Meredith B.K.
      • Kearney F.J.
      • Finlay E.K.
      • Bradley D.G.
      • Fahey A.G.
      • Berry D.P.
      • Lynn D.J.
      Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland.
      IrelandHolsteinDYD and YD of SCS, calculated across first 5 lactations (
      • Berry D.P.
      • Shalloo L.
      • Cromie A.R.
      • Veerkamp R.F.
      • Dillon D.
      • Amer P.R.
      • Kearney J.F.
      • Evans R.D.
      • Wickham B.
      The economic breeding index: A generation on.
      )
      • Meredith B.K.
      • Berry D.P.
      • Kearney F.
      • Finlay E.K.
      • Fahey A.G.
      • Bradley D.G.
      • Lynn D.J.
      A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility.
      IrelandHolsteinDYD of SCS, calculated across first 5 lactations (
      • Berry D.P.
      • Shalloo L.
      • Cromie A.R.
      • Veerkamp R.F.
      • Dillon D.
      • Amer P.R.
      • Kearney J.F.
      • Evans R.D.
      • Wickham B.
      The economic breeding index: A generation on.
      )
      Miles et al., 2020United StatesHolsteinUdder inflammation status (average, healthy, chronic), determined using SCS and CMT results
      • Mulder H.A.
      • Crump R.E.
      • Calus M.P.L.
      • Veerkamp R.F.
      Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms.
      Ireland, Scotland, the Netherlands, SwedenHolsteinEBV of SCS of first-lactation heifers
      • Naderi S.
      • Bohlouli M.
      • Yin T.
      • Konig S.
      Genomic breeding values, SNP effects and gene identification for disease traits in cow training sets.
      GermanyHolsteinCM present or absent, during first lactation
      • Nani J.P.
      • Raschia M.A.
      • Poli M.A.
      • Calvinho L.F.
      • Amadio A.F.
      Genome-wide association study for somatic cell score in Argentinean dairy cattle.
      ArgentinaHolstein and Holstein × JerseySCS for 1 lactation, indicated by 3 variables (arithmetic mean, maximum value, arithmetic mean of the top 3 values)
      • Oliveira H.R.
      • Cant J.P.
      • Brito L.F.
      • Feitosa F.L.B.
      • Chud T.C.S.
      • Fonseca P.A.S.
      • Jamrozik J.
      • Silva F.F.
      • Lourenco D.A.L.
      • Schenkel F.S.
      Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.
      CanadaAyrshire, Holstein, JerseyEBV of SCS, calculated across first 3 lactations (
      • Schaeffer L.R.
      • Jamrozik J.
      • Kistemaker G.J.
      • Van Doormaal B.J.
      Experience with a test-day model.
      )
      • Oliveira H.R.
      • Lourenco D.A.L.
      • Masuda Y.
      • Misztal I.
      • Tsuruta S.
      • Jamrozik J.
      • Brito L.F.
      • Silva F.F.
      • Cant J.P.
      • Schenkel F.S.
      Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.
      CanadaAyrshire, Holstein, JerseyEBV of SCS, calculated across first 3 lactations (
      • Schaeffer L.R.
      • Jamrozik J.
      • Kistemaker G.J.
      • Van Doormaal B.J.
      Experience with a test-day model.
      )
      • Pegolo S.
      • Momen M.
      • Morota G.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle.
      ItalyBrown SwissSCS, ranging from first to fifth parity
      • Prinsen R.
      • Rossoni A.
      • Gredler B.
      • Bieber A.
      • Bagnato A.
      • Strillacci M.G.
      A genome wide association study between CNVs and quantitative traits in Brown Swiss cattle.
      ItalyBrown SwissDEBV of SCS
      • Qanbari S.
      • Pausch H.
      • Jansen S.
      • Somel M.
      • Strom T.M.
      • Fries R.
      • Nielsen R.
      • Simianer H.
      Classic selective sweeps revealed by massive sequencing in cattle.
      GermanyFleckviehEBV of SCS
      • Rincón Flórez J.C.
      • López Herrera A.
      • Echeverri Zuluaga J.J.
      Genome-wide association study using the Bayes C method for important traits in dairy yield in Colombian Holstein cattle.
      ColombiaHolsteinEBV of SCS
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Lund M.S.
      Confirmation and fine-mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle.
      Nordic countriesHolsteinEBVs of CM resistance and SCS, each calculated as a weighted score over first 3 lactations (

      Johansson, K., S. Eriksson, J. Pösö, U. S. Nielsen, and G. P. Aamand. 2007. Predictive ability of different models for clinical mastitis in joint genetic evaluation for Sweden, Denmark and Finland. Proc. 58th EAAP, Dublin, Ireland.

      )
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Holm L.E.
      • Panitz F.
      • Brondum R.F.
      • Bendixen C.
      • Lund M.S.
      Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle.
      Nordic countriesHolstein, Jersey (CM only), Norwegian Red (CM only)EBVs of CM resistance and SCS, each calculated as a weighted score over first 3 lactations (

      Johansson, K., S. Eriksson, J. Pösö, U. S. Nielsen, and G. P. Aamand. 2007. Predictive ability of different models for clinical mastitis in joint genetic evaluation for Sweden, Denmark and Finland. Proc. 58th EAAP, Dublin, Ireland.

      )
      • Seroussi E.
      • Blum S.E.
      • Krifucks O.
      • Shirak A.
      • Jacoby S.
      • Leitner G.
      Basal levels of CD18 antigen presenting cells in cow milk associate with copy number variation of Fc gamma receptors.
      IsraelHolsteinUdder inflammation status (healthy, subclinical, clinical) determined using SCC, CMT and bacterial testing, as well as differential SCC, based on flow cytometry of CD antigens in milk. First-lactation heifers only
      • Siebert L.
      • Staton M.E.
      • Headrick S.
      • Lewis M.
      • Gillespie B.
      • Young C.
      • Almeida R.A.
      • Oliver S.P.
      • Pighetti G.M.
      Genome-wide association study identifies loci associated with milk leukocyte phenotypes following experimental challenge with Streptococcus uberis..
      United StatesHolsteinTime to SCC “cure” after Streptococcus uberis challenge, SCC 0–7 d after challenge and SCC 0–28 d after challenge
      • Silva A.A.
      • Silva D.A.
      • Silva F.F.
      • Costa C.N.
      • Silva H.T.
      • Lopes P.S.
      • Veroneze R.
      • Thompson G.
      • Carvalheira J.
      GWAS and gene networks for milk-related traits from test-day multiple lactations in Portuguese Holstein cattle.
      PortugalHolsteinEBV of SCS, calculated across first 3 lactations
      • Sodeland M.
      • Kent M.P.
      • Olsen H.G.
      • Opsal M.A.
      • Svendsen M.
      • Sehested E.
      • Hayes B.J.
      • Lien S.
      Quantitative trait loci for clinical mastitis on chromosomes 2, 6, 14 and 20 in Norwegian Red cattle.
      NorwayNorwegian RedDYD of lactation average SCS and 7 CM traits, calculated across first 3 lactations
      • Strillacci M.G.
      • Frigo E.
      • Schiavini F.
      • Samore A.B.
      • Canavesi F.
      • Vevey M.
      • Cozzi M.C.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.
      ItalyValdostana Red PiedEBV of SCS of first lactation cows, from phenotypic extreme animals only (top and bottom 20%)
      • Strucken E.M.
      • Bortfeldt R.H.
      • de Koning D.J.
      • Brockmann G.A.
      Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle.
      GermanyHolsteinLactation curve parameter (Wilmink curve;
      • Wilmink J.B.M.
      Adjustment of test-day milk, fat and protein yield for age, season and stage of lactation.
      ) and YD of SCS, calculated over the first 4 lactations
      • Su G.
      • Christensen O.F.
      • Janss L.
      • Lund M.S.
      Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.
      Nordic countriesHolsteinDEBVs of mastitis
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      United StatesHolsteinPTA of CM incidence rate, first lactation only
      • Tribout T.
      • Croiseau P.
      • Lefebvre R.
      • Barbat A.
      • Boussaha M.
      • Fritz S.
      • Boichard D.
      • Hoze C.
      • Sanchez M.P.
      Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle.
      FranceHolstein, Montbéliarde, NormandeDYD and YD of SCS and CM, calculated across lactations
      • Veerkamp R.F.
      • Bouwman A.C.
      • Schrooten C.
      • Calus M.P.L.
      Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle.
      The NetherlandsHolsteinDEBV of SCS
      • Wang X.
      • Ma P.P.
      • Liu J.F.
      • Zhang Q.
      • Zhang Y.
      • Ding X.D.
      • Jiang L.
      • Wang Y.C.
      • Zhang Y.
      • Sun D.X.
      • Zhang S.L.
      • Su G.S.
      • Yu Y.
      Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility.
      ChinaHolsteinEBV of SCS
      • Welderufael B.G.
      • Løvendahl P.
      • de Koning D.J.
      • Janss L.L.G.
      • Fikse W.F.
      Genome-wide association study for susceptibility to and recoverability from mastitis in Danish Holstein cows.
      DenmarkHolsteinTwo health events: transition from healthy to clinical mastitis and vice versa, during first 3 lactations
      • Wijga S.
      • Bastiaansen J.W.M.
      • Wall E.
      • Strandberg E.
      • de Haas Y.
      • Giblin L.
      • Bovenhuis H.
      Genomic associations with somatic cell score in first-lactation Holstein cows.
      Ireland, Scotland, the Netherlands, SwedenHolsteinSCS of first-lactation heifers
      • Yang F.
      • Chen F.H.
      • Li L.L.
      • Yan L.
      • Badri T.
      • Lv C.L.
      • Yu D.L.
      • Zhang M.L.
      • Jang X.J.
      • Li J.
      • Yuan L.
      • Wang G.L.
      • Li H.
      • Li J.
      • Cai Y.F.
      Three novel players: PTK2B, SYK, and TNFRSF21 were identified to be involved in the regulation of bovine mastitis susceptibility via GWAS and post-transcriptional analysis.
      ChinaHolsteinMastitis resistant (no mastitis across 3 lactations and low SCC) and susceptible (CM in each of 3 lactations)
      • Zhou Y.
      • Connor E.E.
      • Wiggans G.R.
      • Lu Y.
      • Tempelman R.J.
      • Schroeder S.G.
      • Chen H.
      • Liu G.E.
      Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle.
      United StatesHolsteinDPTA of SCS, calculated according to
      • VanRaden P.M.
      • Wiggans G.R.
      Derivation, calculation, and use of national animal model information.
      • Zhou J.
      • Liu L.
      • Chen C.J.
      • Zhang M.
      • Lu X.
      • Zhang Z.
      • Huang X.
      • Shi Y.
      Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle.
      ChinaXinjiang BrownEBV of SCS
      1 Nordic = Denmark, Sweden, Finland.
      2 CM = clinical mastitis; DYD = daughter-yield deviations; YD = yield deviations; DEBV = deregressed EBV; and DPTA = deregressed PTA.
      Table 2Genotyping and quality control summary of 52 included manuscripts
      ManuscriptSNP chipAssemblyCall rate (SNPs)MAF
      MAF = minor allele frequency.
      Hardy-Weinberg threshold
      • Abdel-Shafy H.
      • Bortfeldt R.H.
      • Tetens J.
      • Brockmann G.A.
      Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle.
      Illumina BovineSNP50 BeadChip v1UMD 3.1<0.90<0.01P < 0.001
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.
      Illumina BovineSNP50 BeadChip (54K) v1 or v2UMD 3.1.1<0.01P < 10−6
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle.
      Illumina BovineSNP50 BeadChip (54K) v1 or v2UMD 3.1.1<0.01P < 10−6
      • Cecchinato A.
      • Macciotta N.P.P.
      • Mele M.
      • Tagliapietra F.
      • Schiavon S.
      • Bittante G.
      • Pegolo S.
      Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle.
      Illumina BovineSNP50 BeadChip v2UMD 3.1<0.95<0.01P ≤ 0.001
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.0≤0.02
      Durán et al., 2017Illumina BovineHD BeadChipUMD 3.1<0.980.02
      • Fang L.
      • Sahana G.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      integrating sequence-based GWAS and RNA-seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle.
      Illumina HiSeq 2000 systemUMD 3.1≤0.01P ≤ 10−6
      • Fang L.
      • Sahana G.
      • Ma P.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.
      Illumina BovineHD BeadChip, Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.01P < 10−6
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      Illumina BovineHD BeadChip, Illumina Bovine BeadChip (50K)
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1≤0.01P ≤ 10−6
      Fang et al., 2019Illumina BovineHD BeadChip, Illumina BovineSNP50 BeadChip v1 and v2UMD 3.1<0.80<0.01P < 10−5
      • Freebern E.
      • Santos D.J.A.
      • Fang L.
      • Jiang J.
      • Parker Gaddis K.L.
      • Liu G.E.
      • VanRaden P.M.
      • Maltecca C.
      • Cole J.B.
      • Ma L.
      GWAS and fine-mapping of livability and six disease traits in Holstein cattle.
      BovineHD BeadChip (777K)UMD 3.1<0.01P ≤ 10−6
      • Guo J.
      • Jorjani H.
      • Carlborg Ö.
      A genome-wide association study using international breeding-evaluation data identifies major loci affecting production traits and stature in the Brown Swiss cattle breed.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      <0.90<0.02P < 0.01
      • Iung L.H.S.
      • Petrini J.
      • Ramírez-Díaz J.
      • Salvian M.
      • Rovadoscki G.A.
      • Pilonetto F.
      • Dauria B.D.
      • Machado P.F.
      • Coutinho L.L.
      • Wiggans G.R.
      • Mourão G.B.
      Genome-wide association study for milk production traits in a Brazilian Holstein population.
      Illumina BovineLD BeadChip, GeneSeek Genomic Profiler Bovine (50k)<0.90<0.02
      • Jiang J.C.
      • Ma L.
      • Prakapenka D.
      • VanRaden P.M.
      • Cole J.B.
      • Da Y.
      A large-scale genome-wide association study in US Holstein cattle.
      UMD 3.1<0.05
      • Kirsanova E.
      • Heringstad B.
      • Lewandowska-Sabat A.
      • Olsaker I.
      Identification of candidate genes affecting chronic subclinical mastitis in Norwegian Red cattle: Combining genome-wide association study, topologically associated domains and pathway enrichment analysis.
      Affymetrix SNP array (25K), Illumina BovineSNP50 BeadChip (54K),
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Illumina BovineHD BeadChip (777K)
      UMD 3.1.1<0.95 (minimal) and <0.85 (individual)<0.01
      • Kurz J.P.
      • Yang Z.
      • Weiss R.B.
      • Wilson D.J.
      • Rood K.A.
      • Liu G.E.
      • Wang Z.D.
      A genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.
      Illumina BovineHD BeadChipUMD 3.1.1/bosTau8<0.95<0.05
      • Lu H.
      • Wang Y.
      • Bovenhuis H.
      Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle.
      Customized Illumina SNP chip (CRV, 50K) and Illumina Infinium assayBtau 4.0<0.80
      • Ma P.
      • Lund M.S.
      • Aamand G.P.
      • Su G.
      Use of a Bayesian model including QTL markers increases prediction reliability when tests animals are distant from the reference population.
      Illumina BovineSNP50 BeadChip (54K),
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      ≤0.005P ≤ 10−7
      • Macciotta N.P.P.
      • Gaspa G.
      • Bomba L.
      • Vicario D.
      • Dimauro C.
      • Cellesi M.
      • Ajmone-Marsan P.
      Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel.
      Illumina BeadChip (7K)UMD 3.1≤0.99≤0.01
      • Marete A.
      • Lund M.S.
      • Boichard D.
      • Ramayo-Caldas Y.
      A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds.
      Illumina BovineSNP50 BeadChip (50K),
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Illumina BovineLD BeadChip v.2
      UMD 3.1≤0.99≤0.02P < 10−4
      • Marete A.
      • Sahana G.
      • Fritz S.
      • Lefebvre R.
      • Barbat A.
      • Lund M.S.
      • Guldbrandtsen B.
      • Boichard D.
      Genome-wide association study for milking speed in French Holstein cows.
      Illumina BovineSNP50 Beadchip (50k),
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      customized EuroGenomics LD SNP chip
      UMD 3.1<0.95<0.005P < 10−4
      • Meier S.
      • Arends D.
      • Korkuc P.
      • Neumann G.B.
      • Brockmann G.A.
      A genome-wide association study for clinical mastitis in the dual-purpose German Black Pied cattle breed.
      Illumina BovineSNP50 BeadChip v.3UMD 3.1<0.90<0.01
      • Meredith B.K.
      • Kearney F.J.
      • Finlay E.K.
      • Bradley D.G.
      • Fahey A.G.
      • Berry D.P.
      • Lynn D.J.
      Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Btau 4.0<0.95≤0.05
      • Meredith B.K.
      • Berry D.P.
      • Kearney F.
      • Finlay E.K.
      • Fahey A.G.
      • Bradley D.G.
      • Lynn D.J.
      A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility.
      Illumina BovineHD BeadChipUMD 3.1≤0.90<0.05P < 10−6
      Miles et al., 2020Illumina BovineHD BeadChip (777K)ARS-UCD 1.2<0.90<0.05
      • Mulder H.A.
      • Crump R.E.
      • Calus M.P.L.
      • Veerkamp R.F.
      Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.0≤0.95≤0.01x
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      < 600; P = 1.7 × 10−132
      • Naderi S.
      • Bohlouli M.
      • Yin T.
      • Konig S.
      Genomic breeding values, SNP effects and gene identification for disease traits in cow training sets.
      Illumina Bovine Eurogenomics SNP chip (10K), Illumina Bovine SNP BeadChip (50K)
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      <0.95<0.01P < 10−6
      • Nani J.P.
      • Raschia M.A.
      • Poli M.A.
      • Calvinho L.F.
      • Amadio A.F.
      Genome-wide association study for somatic cell score in Argentinean dairy cattle.
      Illumina BovineSNP50 BeadChip v.2UMD 3.1≤0.95<0.05P < 0.001
      • Oliveira H.R.
      • Cant J.P.
      • Brito L.F.
      • Feitosa F.L.B.
      • Chud T.C.S.
      • Fonseca P.A.S.
      • Jamrozik J.
      • Silva F.F.
      • Lourenco D.A.L.
      • Schenkel F.S.
      Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.
      Illumina BovineSNP50 BeadChip (50K)
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.95<0.01
      • Oliveira H.R.
      • Lourenco D.A.L.
      • Masuda Y.
      • Misztal I.
      • Tsuruta S.
      • Jamrozik J.
      • Brito L.F.
      • Silva F.F.
      • Cant J.P.
      • Schenkel F.S.
      Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.
      Illumina BovineSNP50 BeadChip (50K)
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.95<0.01
      • Pegolo S.
      • Momen M.
      • Morota G.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle.
      Illumina BovineSNP50 BeadChip v.2UMD 3.1<0.95<0.01P < 0.001
      • Prinsen R.
      • Rossoni A.
      • Gredler B.
      • Bieber A.
      • Bagnato A.
      • Strillacci M.G.
      A genome wide association study between CNVs and quantitative traits in Brown Swiss cattle.
      Illumina BovineHD BeadChip (777K)UMD 3.1
      • Qanbari S.
      • Pausch H.
      • Jansen S.
      • Somel M.
      • Strom T.M.
      • Fries R.
      • Nielsen R.
      • Simianer H.
      Classic selective sweeps revealed by massive sequencing in cattle.
      Illumina Bovine SNP array (50K2 and 700K)NCBI 6.1≤0.95≤0.005P ≤ 10−6
      • Rincón Flórez J.C.
      • López Herrera A.
      • Echeverri Zuluaga J.J.
      Genome-wide association study using the Bayes C method for important traits in dairy yield in Colombian Holstein cattle.
      Illumina BovineLD BeadChipUMD 3.1<0.001<0.03P ≤ 0.05
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Lund M.S.
      Confirmation and fine-mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.05
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Holm L.E.
      • Panitz F.
      • Brondum R.F.
      • Bendixen C.
      • Lund M.S.
      Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.95<0.05P < 10−5
      • Seroussi E.
      • Blum S.E.
      • Krifucks O.
      • Shirak A.
      • Jacoby S.
      • Leitner G.
      Basal levels of CD18 antigen presenting cells in cow milk associate with copy number variation of Fc gamma receptors.
      Illumina BovineSNP BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      ARS-UCD 1.2<0.05P < 10−4
      • Siebert L.
      • Staton M.E.
      • Headrick S.
      • Lewis M.
      • Gillespie B.
      • Young C.
      • Almeida R.A.
      • Oliver S.P.
      • Pighetti G.M.
      Genome-wide association study identifies loci associated with milk leukocyte phenotypes following experimental challenge with Streptococcus uberis..
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1.10
      • Silva A.A.
      • Silva D.A.
      • Silva F.F.
      • Costa C.N.
      • Silva H.T.
      • Lopes P.S.
      • Veroneze R.
      • Thompson G.
      • Carvalheira J.
      GWAS and gene networks for milk-related traits from test-day multiple lactations in Portuguese Holstein cattle.
      Neogen GGP LD v.1, Illumina BovineSNP50 BeadChip v.1 and v.2, Illumina 57K, Illumina 77K, Neogen GGP HD v.3, Affymetrix Ax58K panel<0.95<0.02P < 10−6
      • Sodeland M.
      • Kent M.P.
      • Olsen H.G.
      • Opsal M.A.
      • Svendsen M.
      • Sehested E.
      • Hayes B.J.
      • Lien S.
      Quantitative trait loci for clinical mastitis on chromosomes 2, 6, 14 and 20 in Norwegian Red cattle.
      Affymetrix 25K MIP array≤0.75≤0.025
      • Strillacci M.G.
      • Frigo E.
      • Schiavini F.
      • Samore A.B.
      • Canavesi F.
      • Vevey M.
      • Cozzi M.C.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.
      Illumina BovineHD BeadChip (777K)UMD 3.1 and Btau 4.6.1
      • Strucken E.M.
      • Bortfeldt R.H.
      • de Koning D.J.
      • Brockmann G.A.
      Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      <0.95<0.0164
      • Su G.
      • Christensen O.F.
      • Janss L.
      • Lund M.S.
      Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.2<0.01
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      <0.99<0.05
      • Tribout T.
      • Croiseau P.
      • Lefebvre R.
      • Barbat A.
      • Boussaha M.
      • Fritz S.
      • Boichard D.
      • Hoze C.
      • Sanchez M.P.
      Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle.
      Illumina Bovine SNP50 BeadChip (50K),
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Illumina BovineHD Beadchip (777K)
      UMD 3.1≤0.90<0.01P ≤ 10−4
      • Veerkamp R.F.
      • Bouwman A.C.
      • Schrooten C.
      • Calus M.P.L.
      Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle.
      Illumina BovineHD BeadChip
      • Wang X.
      • Ma P.P.
      • Liu J.F.
      • Zhang Q.
      • Zhang Y.
      • Ding X.D.
      • Jiang L.
      • Wang Y.C.
      • Zhang Y.
      • Sun D.X.
      • Zhang S.L.
      • Su G.S.
      • Yu Y.
      Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      UMD 3.1<0.90≤0.03P ≤ 10−6
      • Welderufael B.G.
      • Løvendahl P.
      • de Koning D.J.
      • Janss L.L.G.
      • Fikse W.F.
      Genome-wide association study for susceptibility to and recoverability from mastitis in Danish Holstein cows.
      Illumina BovineSNP50 BeadChip v.2UMD 3.1<0.90<0.01
      • Wijga S.
      • Bastiaansen J.W.M.
      • Wall E.
      • Strandberg E.
      • de Haas Y.
      • Giblin L.
      • Bovenhuis H.
      Genomic associations with somatic cell score in first-lactation Holstein cows.
      Illumina BovineSNP50 BeadChip
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Baylor 4.0 or bosTau4<0.95≤0.01X
      Did not mention the version of the Illumina BovineSNP50 BeadChip.
      < 600
      • Yang F.
      • Chen F.H.
      • Li L.L.
      • Yan L.
      • Badri T.
      • Lv C.L.
      • Yu D.L.
      • Zhang M.L.
      • Jang X.J.
      • Li J.
      • Yuan L.
      • Wang G.L.
      • Li H.
      • Li J.
      • Cai Y.F.
      Three novel players: PTK2B, SYK, and TNFRSF21 were identified to be involved in the regulation of bovine mastitis susceptibility via GWAS and post-transcriptional analysis.
      Illumina Hiseq XtenUMD 3.1.1
      • Zhou Y.
      • Connor E.E.
      • Wiggans G.R.
      • Lu Y.
      • Tempelman R.J.
      • Schroeder S.G.
      • Chen H.
      • Liu G.E.
      Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle.
      Illumina BovineHD BeadChipUMD 3.1≤0.90≤0.05P ≤ 10−6
      • Zhou J.
      • Liu L.
      • Chen C.J.
      • Zhang M.
      • Lu X.
      • Zhang Z.
      • Huang X.
      • Shi Y.
      Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle.
      llumina Bovine BeadChip (150K)UMD 3.1.1≤0.90
      1 MAF = minor allele frequency.
      2 Did not mention the version of the Illumina BovineSNP50 BeadChip.
      Table 3Genome-wide association analysis summary of 52 full-text manuscripts
      ManuscriptGWAS approachNumber of animals used for GWASNumber of SNPsMultiple correction
      FDR = false discovery rate.
      Number of SNPs/Windows associated
      CM = clinical mastitis; CNVR = copy number variant region; LASCS = lactation-average somatic cell score, SCS-SD = SD for test-day SCS.
      • Abdel-Shafy H.
      • Bortfeldt R.H.
      • Tetens J.
      • Brockmann G.A.
      Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle.
      Linear regression in PLINK (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.R.
      • Bender D.
      • Maller J.
      • Sklar P.
      • de Bakker P.I.W.
      • Daly M.J.
      • Sham P.C.
      PLINK: A tool set for whole-genome association and population-based linkage analyses.
      )
      2,354 sires37,424Bonferroni16 SNPs, 9 QTL windows
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.
      GCTA was used (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      ). Specific method not specified
      5,147 sires15,552,968Bonferroni22 SNPs
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Weighting sequence variants based on their annotation increases the power of genome-wide association studies in dairy cattle.
      GCTA was used (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      ). Specific method not specified
      4,921 sires16,503,508Bonferroni
      • Cecchinato A.
      • Macciotta N.P.P.
      • Mele M.
      • Tagliapietra F.
      • Schiavon S.
      • Bittante G.
      • Pegolo S.
      Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle.
      Mixed model using GenABEL R package (
      • Aulchenko Y.S.
      • Ripke S.
      • Isaacs A.
      • van Duijn C.M.
      GenABEL: An R library for genome-wide association analysis.
      ) followed by regression-genomic control approach (GRAMMAR-GC;
      • Amin N.
      • van Duijn C.M.
      • Aulchenko Y.S.
      A genomic background based method for association analysis in related individuals.
      )
      1,011 cows37,568Bonferroni17 SNPs
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      Single-locus model1,654 cows45,878Bonferroni613 SNPs
      • Durán Aguilar M.
      • Roman Ponce S.I.
      • Ruiz Lopez F.J.
      • Gonzalez Padilla E.
      • Vasquez Pelaez C.G.
      • Bagnato A.
      • Strillacci M.G.
      Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers.
      Two methods: linear regression in SVS 8.3.1 for CNVs detected by CNAM algorithm, and linear regression with CNVRULER for CNVs detected using PennCNV's Hidden Markov Model220 cows for SVS analysis, 123 cows for PennCNV analysisFDR24 CNVR:SVS and 47 CNVR:PennCNV
      • Fang L.
      • Sahana G.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      integrating sequence-based GWAS and RNA-seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle.
      Variance based 2-step method with linear models and generalized linear regression models using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      5,056 Holstein, 1,231 Jersey, 4,310 Nordic Red cattle15,355,382 (Holstein),Bonferroni5 SNPs (Holstein), 0 SNPs (Jersey), 2 SNPs (Nordic Red)
      13,403,916 (Jersey),
      15,243,827 (Nordic Red)
      • Fang L.
      • Sahana G.
      • Ma P.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds.
      Variance based 2-step method with linear models and generalized linear regression models using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      5,056 Holstein, 1,231 Jersey cattle15,355,382 (Holstein),Bonferroni
      13,403,916 (Jersey)
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      Variance based 2-step method with linear models and generalized linear regression models using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      5,056 Holstein, 1,231 Jersey, 4,310 Nordic Red cattle15,355,382 (Holstein),Bonferroni6 QTL windows
      13,403,916 (Jersey),
      15,243,827 (Nordic Red)
      • Fang Z.H.
      • Pausch H.
      Multi-trait meta-analyses reveal 25 quantitative trait loci for economically important traits in Brown Swiss cattle.
      Variance based 2-step method with linear models and generalized linear regression models using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      4,578 sires598,016Bonferroni24 SNPs
      • Freebern E.
      • Santos D.J.A.
      • Fang L.
      • Jiang J.
      • Parker Gaddis K.L.
      • Liu G.E.
      • VanRaden P.M.
      • Maltecca C.
      • Cole J.B.
      • Ma L.
      GWAS and fine-mapping of livability and six disease traits in Holstein cattle.
      Mixed model using MMAP (
      • O'Connell J.R.
      MMAP User Guide.
      )
      14,382 sires3,148,506Bonferroni1 SNPs
      • Guo J.
      • Jorjani H.
      • Carlborg Ö.
      A genome-wide association study using international breeding-evaluation data identifies major loci affecting production traits and stature in the Brown Swiss cattle breed.
      Mixed model using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      4,803 siresBonferroni1 SNPs
      • Iung L.H.S.
      • Petrini J.
      • Ramírez-Díaz J.
      • Salvian M.
      • Rovadoscki G.A.
      • Pilonetto F.
      • Dauria B.D.
      • Machado P.F.
      • Coutinho L.L.
      • Wiggans G.R.
      • Mourão G.B.
      Genome-wide association study for milk production traits in a Brazilian Holstein population.
      Weighted single-step genomic BLUP (ssGBLUP) method (
      • Wang H.
      • Misztal I.
      • Aguilar I.
      • Legarra A.
      • Muir W.M.
      Genome-wide association mapping including phenotypes from relatives without genotypes.
      )
      1,067 cows56,2563 windows of 10 SNPs each
      • Jiang J.C.
      • Ma L.
      • Prakapenka D.
      • VanRaden P.M.
      • Cole J.B.
      • Da Y.
      A large-scale genome-wide association study in US Holstein cattle.
      Two methods: approximate generalized least squares (AGLS) analysis using EPSNPmpi (
      • Ma L.
      • Runesha H.B.
      • Dvorkin D.
      • Garbe J.R.
      • Da Y.
      Parallel and serial computing tools for testing single-locus and epistatic SNP effects of quantitative traits in genome-wide association studies.
      ;
      • Weeks N.T.
      • Luecke G.R.
      • Groth B.M.
      • Kraeva M.
      • Ma L.
      • Kramer L.M.
      • Koltes J.E.
      • Reecy J.M.
      High-performance epistasis detection in quantitative trait GWAS.
      ), and a Bayesian mixed model using BOLT-LMM
      293,467 cows57,067Bonferroni140 SNPs
      • Kirsanova E.
      • Heringstad B.
      • Lewandowska-Sabat A.
      • Olsaker I.
      Identification of candidate genes affecting chronic subclinical mastitis in Norwegian Red cattle: Combining genome-wide association study, topologically associated domains and pathway enrichment analysis.
      Variance based two-step method (
      • Svishcheva G.R.
      • Axenovich T.I.
      • Belonogova N.M.
      • van Duijn C.M.
      • Aulchenko Y.S.
      Rapid variance components-based method for whole-genome association analysis.
      ) using GRAMMA-Gamma function in GenABEL R package (
      • Aulchenko Y.S.
      • Ripke S.
      • Isaacs A.
      • van Duijn C.M.
      GenABEL: An R library for genome-wide association analysis.
      )
      3,795 sires613,90893 SNPs
      • Kurz J.P.
      • Yang Z.
      • Weiss R.B.
      • Wilson D.J.
      • Rood K.A.
      • Liu G.E.
      • Wang Z.D.
      A genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.
      Mixed model (
      • Segura V.
      • Vilhjálmsson B.J.
      • Platt A.
      • Korte A.
      • Seren Ü.
      • Long Q.
      • Nordborg M.
      An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations.
      ) using SVS
      43 cows585,949Bonferroni117 SNPs, 27 QTL windows
      • Lu H.
      • Wang Y.
      • Bovenhuis H.
      Genome-wide association study for genotype by lactation stage interaction of milk production traits in dairy cattle.
      Mixed model using ASREML (
      • Gilmour A.R.
      • Gogel B.J.
      • Cullis B.R.
      • Welham S.J.
      • Thompson R.
      ASReml User Guide Release 1.0.
      )
      1,800 cows30,348FDR
      • Ma P.
      • Lund M.S.
      • Aamand G.P.
      • Su G.
      Use of a Bayesian model including QTL markers increases prediction reliability when tests animals are distant from the reference population.
      EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      ). Specific method not specified
      3,114 sires15,388,916Bonferroni
      • Macciotta N.P.P.
      • Gaspa G.
      • Bomba L.
      • Vicario D.
      • Dimauro C.
      • Cellesi M.
      • Ajmone-Marsan P.
      Genome-wide association analysis in Italian Simmental cows for lactation curve traits using a low-density (7K) SNP panel.
      Mixed model using ASREML (
      • Gilmour A.R.
      • Cullis B.R.
      • Welhan S.J.
      • Thompson R.
      ASREML manual.
      )
      337 cows6,891Bonferroni1 SNPs
      • Marete A.
      • Lund M.S.
      • Boichard D.
      • Ramayo-Caldas Y.
      A system-based analysis of the genetic determinism of udder conformation and health phenotypes across three French dairy cattle breeds.
      Mixed model using GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      )
      CM phenotype: 13,879 Monbéliarde, 9,013 Normande, 32,491 Holstein. SCS phenotype: 20,141 Monbéliarde, 11,965 Normande, 46,732 Holstein38,827 (Montbéliarde),BonferroniCM: 14 (Montbéliarde), 12 (Nordic Red), 336 (Holstein), SCC: 11 (Holstein)
      38,109 (Normande),
      40,810 (Holstein)
      • Marete A.
      • Sahana G.
      • Fritz S.
      • Lefebvre R.
      • Barbat A.
      • Lund M.S.
      • Guldbrandtsen B.
      • Boichard D.
      Genome-wide association study for milking speed in French Holstein cows.
      Mixed model using GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      )
      32,491 cows49,835Bonferroni19 SNPs (CM), 28 SNPs (SCS)
      • Meier S.
      • Arends D.
      • Korkuc P.
      • Neumann G.B.
      • Brockmann G.A.
      A genome-wide association study for clinical mastitis in the dual-purpose German Black Pied cattle breed.
      Mixed model using GEMMA (
      • Zhou X.
      • Stephens M.
      Efficient multivariate linear mixed model algorithms for genome-wide association studies.
      )
      1,062 cows38,224Bonferroni5 SNPs
      • Meredith B.K.
      • Kearney F.J.
      • Finlay E.K.
      • Bradley D.G.
      • Fahey A.G.
      • Berry D.P.
      • Lynn D.J.
      Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland.
      Two methods: mixed model and modified ‘BayesB' Bayesian method (
      • Meuwissen T.H.
      • Hayes B.J.
      • Goddard M.E.
      Prediction of total genetic value using genome-wide dense marker maps.
      )
      773 sires, 493 cows40,668 (sires),FDR10 SNPs (sires)
      39,143 (cows)
      • Meredith B.K.
      • Berry D.P.
      • Kearney F.
      • Finlay E.K.
      • Fahey A.G.
      • Bradley D.G.
      • Lynn D.J.
      A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility.
      Mixed model using DMU (
      • Madsen P.
      • Jensen J.
      DMU: A user's guide. A package for analysing multivariate mixed models. Version 6, release 5.0.
      )
      702 sires578,181FDR138 SNPs, 28 QTL windows
      • Miles A.M.
      • Huson H.J.
      Time-and population-dependent genetic patterns underlie bovine milk somatic cell count.
      Mixed model using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      458 cows581,663Bonferroni and FDR166 SNPs
      • Mulder H.A.
      • Crump R.E.
      • Calus M.P.L.
      • Veerkamp R.F.
      Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms.
      Univariate Bayesian SSVS approach (
      • Calus M.P.L.
      • Meuwissen T.H.E.
      • de Roos A.P.W.
      • Veerkamp R.F.
      Accuracy of genomic selection using different methods to define haplotypes.
      ;
      • Verbyla K.L.
      • Calus M.P.L.
      • Mulder H.A.
      • de Haas Y.
      • Veerkamp R.F.
      Predicting energy balance for dairy cows using high-density single nucleotide polymorphism information.
      )
      1,563 cows37,59054 SNPs
      • Naderi S.
      • Bohlouli M.
      • Yin T.
      • Konig S.
      Genomic breeding values, SNP effects and gene identification for disease traits in cow training sets.
      Mixed model using GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      )
      6,737 cows43,939FDR4 SNPs
      • Nani J.P.
      • Raschia M.A.
      • Poli M.A.
      • Calvinho L.F.
      • Amadio A.F.
      Genome-wide association study for somatic cell score in Argentinean dairy cattle.
      General linear model in PLINK540 cows38,872FDR10 SNPs
      • Oliveira H.R.
      • Cant J.P.
      • Brito L.F.
      • Feitosa F.L.B.
      • Chud T.C.S.
      • Fonseca P.A.S.
      • Jamrozik J.
      • Silva F.F.
      • Lourenco D.A.L.
      • Schenkel F.S.
      Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.
      1,141 Ayrshire sires and cows, 7,329 Holstein sires and cows, 524 Jersey sires and cows41,517 (Ayrshire),377 windows
      43,556 (Holstein),
      38,194 (Jersey)
      • Oliveira H.R.
      • Lourenco D.A.L.
      • Masuda Y.
      • Misztal I.
      • Tsuruta S.
      • Jamrozik J.
      • Brito L.F.
      • Silva F.F.
      • Cant J.P.
      • Schenkel F.S.
      Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.
      Single-step genomic BLUP method (
      • Wang H.
      • Misztal I.
      • Aguilar I.
      • Legarra A.
      • Fernando R.L.
      • Vitezica Z.
      • Okimoto R.
      • Wing T.
      • Hawken R.
      • Muir W.M.
      Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.
      ) using postGSf90 (
      • Aguilar I.
      • Misztal I.
      • Tsuruta S.
      • Legarra A.
      • Wang H.
      PREGSF90–POSTGSF90: Computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. Proc. 10th World Congr. Genet. Appl. Livest. Prod..
      )
      634 Ayrshire, 8,517 Holstein, 849 Jersey sires. 1,193 Ayrshire, 12,146 Holstein, 578 Jersey cows38,096 (Ayrshire),1921 SNPs
      40,658 (Holstein),
      34,500 (Jersey)
      • Pegolo S.
      • Momen M.
      • Morota G.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle.
      Structural equation model (
      • Gianola D.
      • Sorensen D.
      Quantitative genetic models for describing simultaneous and recursive relationships between phenotypes.
      ;
      • Momen M.
      • Ayatollahi Mehrgardi A.
      • Amiri Roudbar M.
      • Kranis A.
      • Mercuri Pinto R.
      • Valente B.D.
      • Morota G.
      • Rosa G.J.M.
      • Gianola D.
      Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models.
      , unpublished
      M. Momen, Virginia Polytechnic Institute and State University, Blacksburg, VA; M. T. Campbell, Virginia Polytechnic Institute and State University, Blacksburg, VA; H. Walia, University of Nebraska-Lincoln, Lincoln, NE; and G. Morota, Virginia Polytechnic Institute and State University, Blacksburg, VA.
      ) using the “SNP Snappy” strategy (
      • Meyer K.
      • Tier B.
      “SNP Snappy”: A strategy for fast genome-wide association studies fitting a full mixed model.
      ) in WOMBAT (
      • Meyer K.
      WOMBAT: A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML).
      )
      1,011 cows37,519Bonferroni6 SNPs
      • Prinsen R.
      • Rossoni A.
      • Gredler B.
      • Bieber A.
      • Bagnato A.
      • Strillacci M.G.
      A genome wide association study between CNVs and quantitative traits in Brown Swiss cattle.
      Correlation-trend test using SVS938 cattle (sires + cows)FDR
      • Qanbari S.
      • Pausch H.
      • Jansen S.
      • Somel M.
      • Strom T.M.
      • Fries R.
      • Nielsen R.
      • Simianer H.
      Classic selective sweeps revealed by massive sequencing in cattle.
      Mixed model using EMMAX (
      • Kang H.M.
      • Sul J.H.
      • Service S.K.
      • Zaitlen N.A.
      • Kong S.
      • Freimer N.B.
      • Sabatti C.
      • Eskin E.
      Variance component model to account for sample structure in genome-wide association studies.
      )
      3,602 cattle15,182,131Bonferroni2 SNPs
      • Rincón Flórez J.C.
      • López Herrera A.
      • Echeverri Zuluaga J.J.
      Genome-wide association study using the Bayes C method for important traits in dairy yield in Colombian Holstein cattle.
      Bayesian regression method ‘Bayes Cπ’ (
      • Habier D.
      • Fernando R.L.
      • Kizilkaya K.
      • Garrick D.J.
      Extension of the Bayesian alphabet for genomic selection.
      )
      150 cattle (37 sires and 113 cows)6,5105 SNPs
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Lund M.S.
      Confirmation and fine-mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle.
      Mixed model (
      • Yu J.
      • Pressoir G.
      • Briggs W.H.
      • Vroh Bi I.
      • Yamasaki M.
      • Doebley J.F.
      • McMullen M.D.
      • Gaut B.S.
      • Nielsen D.M.
      • Holland J.B.
      • Kresovich S.
      • Buckler E.S.
      A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.
      ) using DMU (
      • Madsen P.
      • Jensen J.
      DMU - A user's guide. A package for analysing multivariate mixed models. Version 6, release 5.1.
      )
      CM phenotype: 2,098 sires. SCS phenotype: 1,676 sires36,387FDR130 SNPs (CM), 13 SNPs (SCS)
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Holm L.E.
      • Panitz F.
      • Brondum R.F.
      • Bendixen C.
      • Lund M.S.
      Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle.
      Mixed model (
      • Yu J.
      • Pressoir G.
      • Briggs W.H.
      • Vroh Bi I.
      • Yamasaki M.
      • Doebley J.F.
      • McMullen M.D.
      • Gaut B.S.
      • Nielsen D.M.
      • Holland J.B.
      • Kresovich S.
      • Buckler E.S.
      A unified mixed-model method for association mapping that accounts for multiple levels of relatedness.
      ) using DMU (
      • Madsen P.
      • Jensen J.
      DMU - A user's guide. A package for analysing multivariate mixed models. Version 6, release 5.1.
      )
      CM phenotype: 4,992 Holstein, 4,442 Nordic Red, 1,126 Jersey sires. SCS phenotype: 4,992 Holstein sires648,219Bonferroni66 SNPs
      • Seroussi E.
      • Blum S.E.
      • Krifucks O.
      • Shirak A.
      • Jacoby S.
      • Leitner G.
      Basal levels of CD18 antigen presenting cells in cow milk associate with copy number variation of Fc gamma receptors.
      PLINK. Specific method not specified125 cowsBonferroni15 SNPs
      • Siebert L.
      • Staton M.E.
      • Headrick S.
      • Lewis M.
      • Gillespie B.
      • Young C.
      • Almeida R.A.
      • Oliver S.P.
      • Pighetti G.M.
      Genome-wide association study identifies loci associated with milk leukocyte phenotypes following experimental challenge with Streptococcus uberis..
      Wald test using PLINK (
      • Purcell S.
      • Neale B.
      • Todd-Brown K.
      • Thomas L.
      • Ferreira M.A.R.
      • Bender D.
      • Maller J.
      • Sklar P.
      • de Bakker P.I.W.
      • Daly M.J.
      • Sham P.C.
      PLINK: A tool set for whole-genome association and population-based linkage analyses.
      )
      34 cows (time to SCC “cure” after Streptococcus uberis challenge), 36 cows (SCC 0–7 d after challenge), 35 cows (SCC 0–28 d after challenge)45,997Arbitrary cut-off level16 SNPs
      • Silva A.A.
      • Silva D.A.
      • Silva F.F.
      • Costa C.N.
      • Silva H.T.
      • Lopes P.S.
      • Veroneze R.
      • Thompson G.
      • Carvalheira J.
      GWAS and gene networks for milk-related traits from test-day multiple lactations in Portuguese Holstein cattle.
      Weighted single-step GWAS test-day model (
      • Wang H.
      • Misztal I.
      • Aguilar I.
      • Legarra A.
      • Muir W.M.
      Genome-wide association mapping including phenotypes from relatives without genotypes.
      )
      1,338 cattle (785 cows and 553 sires)38,323
      • Sodeland M.
      • Kent M.P.
      • Olsen H.G.
      • Opsal M.A.
      • Svendsen M.
      • Sehested E.
      • Hayes B.J.
      • Lien S.
      Quantitative trait loci for clinical mastitis on chromosomes 2, 6, 14 and 20 in Norwegian Red cattle.
      Mixed modelCM phenotype: 2,086 sires. SCS phenotype: 2,118 sires17,34929 SNPs (CM), 13 SNPs (SCS)
      • Strillacci M.G.
      • Frigo E.
      • Schiavini F.
      • Samore A.B.
      • Canavesi F.
      • Vevey M.
      • Cozzi M.C.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.
      Multiple marker test using an adapted pipeline in R (
      • Bagnato A.
      • Schiavini F.
      • Rossoni A.
      • Maltecca C.
      • Dolezal M.
      • Medugorac I.
      • Sölkner J.
      • Russo V.
      • Fontanesi L.
      • Friedmann A.
      • Soller M.
      • Lipkin E.
      Quantitative trait loci affecting milk yield and protein percentage in a three-country Brown Swiss population.
      ;
      • Strillacci M.G.
      • Frigo E.
      • Canavesi F.
      • Ungar Y.
      • Schiavini F.
      • Zaniboni L.
      • Reghenzani L.
      • Cozzi M.C.
      • Samoré A.B.
      • Kashi Y.
      • Shimoni E.
      • Tal-Stein R.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Quantitative trait loci mapping for conjugated linoleic acid, vaccenic acid and ∆(9) -desaturase in Italian Brown Swiss dairy cattle using selective DNA pooling.
      )
      79 sires655,665Bonferroni171 SNPs
      • Strucken E.M.
      • Bortfeldt R.H.
      • de Koning D.J.
      • Brockmann G.A.
      Genome-wide associations for investigating time-dependent genetic effects for milk production traits in dairy cattle.
      Mixed model using GenABEL package in R (
      • Aulchenko Y.S.
      • Ripke S.
      • Isaacs A.
      • van Duijn C.M.
      GenABEL: An R library for genome-wide association analysis.
      )
      151 cows44,962Bonferroni17 SNPs
      • Su G.
      • Christensen O.F.
      • Janss L.
      • Lund M.S.
      Comparison of genomic predictions using genomic relationship matrices built with different weighting factors to account for locus-specific variances.
      Mixed model using DMU (Madsen et al., 2010)4,191 sires44,919
      • Tiezzi F.
      • Parker-Gaddis K.L.
      • Cole J.B.
      • Clay J.S.
      • Maltecca C.
      A Genome-wide association study for clinical mastitis in first parity US Holstein cows using single-step approach and genomic matrix re-weighting procedure.
      Single-step genomic BLUP method (
      • Aguilar I.
      • Misztal I.
      • Johnson D.L.
      • Legarra A.
      • Tsuruta S.
      • Lawlor T.J.
      Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.
      ;
      • Christensen O.F.
      • Lund M.S.
      Genomic prediction when some animals are not genotyped.
      ;
      • Misztal I.
      • Aggrey S.E.
      • Muir W.M.
      Experiences with a single-step genome evaluation.
      ) using PREGSF90-POSTGSF90 (
      • Aguilar I.
      • Misztal I.
      • Tsuruta S.
      • Legarra A.
      • Wang H.
      PREGSF90–POSTGSF90: Computational tools for the implementation of single-step genomic selection and genome-wide association with ungenotyped individuals in BLUPF90 programs. Proc. 10th World Congr. Genet. Appl. Livest. Prod..
      ) and THRGIBBS1F90 (
      • Tsuruta S.
      • Misztal I.
      THRGIBBS1F90 for estimation of variance components with threshold linear models.
      )
      1,361 sires39,00453 windows
      • Tribout T.
      • Croiseau P.
      • Lefebvre R.
      • Barbat A.
      • Boussaha M.
      • Fritz S.
      • Boichard D.
      • Hoze C.
      • Sanchez M.P.
      Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle.
      Mixed model using GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      )
      CM phenotype: 1,857 Montbéliarde, 1.427 Normande, 4,959 Holstein sires. SCS phenotype: 2.438 Montbéliarde, 2,203 Normande, 6,318 Holstein sires. Phenotype not specified: 44,832 Montbéliarde, 44,659 Normande, 46,753 Holstein cows46,753 (Holstein),Bonferroni5 windows
      44,832 (Montbéliarde),
      44,659 (Normande)
      • Veerkamp R.F.
      • Bouwman A.C.
      • Schrooten C.
      • Calus M.P.L.
      Genomic prediction using preselected DNA variants from a GWAS with whole-genome sequence data in Holstein-Friesian cattle.
      Mixed model using GCTA (
      • Yang J.
      • Lee S.H.
      • Goddard M.E.
      • Visscher P.M.
      GCTA: A tool for genome-wide complex trait analysis.
      )
      3,469 sires13,789,029Arbitrary cut-off levels
      • Wang X.
      • Ma P.P.
      • Liu J.F.
      • Zhang Q.
      • Zhang Y.
      • Ding X.D.
      • Jiang L.
      • Wang Y.C.
      • Zhang Y.
      • Sun D.X.
      • Zhang S.L.
      • Su G.S.
      • Yu Y.
      Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility.
      Mixed model using FORTRAN (
      • Jiang L.
      • Liu J.F.
      • Sun D.X.
      • Ma P.P.
      • Ding X.D.
      • Yu Y.
      • Zhang Q.
      Genome wide association studies for milk production traits in Chinese Holstein population.
      )
      2,093 cows43,885Bonferroni48 SNPs
      • Welderufael B.G.
      • Løvendahl P.
      • de Koning D.J.
      • Janss L.L.G.
      • Fikse W.F.
      Genome-wide association study for susceptibility to and recoverability from mastitis in Danish Holstein cows.
      Mixed model in DMU (
      • Madsen P.
      • Jensen J.
      A user's guide to DMU. A package for analysing multivariate mixed models. Version 6, Release 5.2.
      )
      993 cows39,378Arbitrary cut-off level14 SNPs (from healthy to CM), 15 SNPs (from CM to healthy)
      • Wijga S.
      • Bastiaansen J.W.M.
      • Wall E.
      • Strandberg E.
      • de Haas Y.
      • Giblin L.
      • Bovenhuis H.
      Genomic associations with somatic cell score in first-lactation Holstein cows.
      Mixed model using ASREML (
      • Gilmour A.R.
      • Gogel B.J.
      • Cullis B.R.
      • Thompson R.
      ASReml User Guide. Release 3.0.
      )
      1,484 cows37,590FDR2 SNPs (LASCS), 2 SNPs (SCS-SD)
      • Yang F.
      • Chen F.H.
      • Li L.L.
      • Yan L.
      • Badri T.
      • Lv C.L.
      • Yu D.L.
      • Zhang M.L.
      • Jang X.J.
      • Li J.
      • Yuan L.
      • Wang G.L.
      • Li H.
      • Li J.
      • Cai Y.F.
      Three novel players: PTK2B, SYK, and TNFRSF21 were identified to be involved in the regulation of bovine mastitis susceptibility via GWAS and post-transcriptional analysis.
      Logistic regression (
      • Guo P.
      • Zhu B.
      • Niu H.
      • Wang Z.
      • Liang Y.
      • Chen Y.
      • Zhang L.
      • Ni H.
      • Guo Y.
      • Hay E.H.A.
      • Gao X.
      • Gao H.
      • Wu X.
      • Xu L.
      • Li J.
      Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis.
      ) followed by Bayesian analysis
      40 cows10,05842 SNPs (Bayesian), 51 SNPs (logistic regression), both analysis 27 SNPs
      • Zhou Y.
      • Connor E.E.
      • Wiggans G.R.
      • Lu Y.
      • Tempelman R.J.
      • Schroeder S.G.
      • Chen H.
      • Liu G.E.
      Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle.
      Linear regression using SVS396 cowsFDR
      • Zhou J.
      • Liu L.
      • Chen C.J.
      • Zhang M.
      • Lu X.
      • Zhang Z.
      • Huang X.
      • Shi Y.
      Genome-wide association study of milk and reproductive traits in dual-purpose Xinjiang Brown cattle.
      Iterative fixed and random models (FarmCPU;
      • Liu X.
      • Huang M.
      • Fan B.
      • Buckler E.S.
      • Zhang Z.
      Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies.
      )
      473 cows139,376Bonferroni3 SNPs
      1 FDR = false discovery rate.
      2 CM = clinical mastitis; CNVR = copy number variant region; LASCS = lactation-average somatic cell score, SCS-SD = SD for test-day SCS.
      3 M. Momen, Virginia Polytechnic Institute and State University, Blacksburg, VA; M. T. Campbell, Virginia Polytechnic Institute and State University, Blacksburg, VA; H. Walia, University of Nebraska-Lincoln, Lincoln, NE; and G. Morota, Virginia Polytechnic Institute and State University, Blacksburg, VA.
      Moreover, significant markers or windows associated with CM and SCC-related traits and their coordinates were extracted for further analyses. Functional analyses, such as gene and QTL annotation, QTL enrichment analysis, GO terms enrichment, and KEGG pathway analyses, were conducted for 40 studies, with 37 and 7 studies reported markers and windows, respectively, and 4 studies reporting both. Twelve studies were excluded in further analysis due to not reporting the significant makers or windows or inability to convert reported coordinates to new bovine assembly, due to inadequate information.

      Annotation for Genes and QTL

      Conversion of Genomic Coordinates to ARS-UCD1.2

      All markers and windows identified in the 40 studies are provided in Supplementary Material 2 ( https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ). During conversion of genomic coordinates to new bovine assembly (ARS-UCD1.2) 1, 7, 1, and 1 significant markers from
      • Jiang J.C.
      • Ma L.
      • Prakapenka D.
      • VanRaden P.M.
      • Cole J.B.
      • Da Y.
      A large-scale genome-wide association study in US Holstein cattle.
      ,
      • Oliveira H.R.
      • Lourenco D.A.L.
      • Masuda Y.
      • Misztal I.
      • Tsuruta S.
      • Jamrozik J.
      • Brito L.F.
      • Silva F.F.
      • Cant J.P.
      • Schenkel F.S.
      Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.
      ,
      • Pegolo S.
      • Momen M.
      • Morota G.
      • Rosa G.J.M.
      • Gianola D.
      • Bittante G.
      • Cecchinato A.
      Structural equation modeling for investigating multi-trait genetic architecture of udder health in dairy cattle.
      , and
      • Tribout T.
      • Croiseau P.
      • Lefebvre R.
      • Barbat A.
      • Boussaha M.
      • Fritz S.
      • Boichard D.
      • Hoze C.
      • Sanchez M.P.
      Confirmed effects of candidate variants for milk production, udder health, and udder morphology in dairy cattle.
      , respectively were deleted. Likewise, 8, 1, and 9 windows from
      • Durán Aguilar M.
      • Roman Ponce S.I.
      • Ruiz Lopez F.J.
      • Gonzalez Padilla E.
      • Vasquez Pelaez C.G.
      • Bagnato A.
      • Strillacci M.G.
      Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers.
      ,
      • Meredith B.K.
      • Berry D.P.
      • Kearney F.
      • Finlay E.K.
      • Fahey A.G.
      • Bradley D.G.
      • Lynn D.J.
      A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility.
      , and
      • Oliveira H.R.
      • Lourenco D.A.L.
      • Masuda Y.
      • Misztal I.
      • Tsuruta S.
      • Jamrozik J.
      • Brito L.F.
      • Silva F.F.
      • Cant J.P.
      • Schenkel F.S.
      Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.
      , respectively, were deleted during the conversion. These markers and windows were deleted due to one of the following reasons: previously reported genomic coordinates did not align well with the new bovine assembly or partially aligned, but not enough to convert, or the region was split up into various parts. After conversion, an additional 8 markers were removed from the data set as they did not have chromosome number [7 from
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      and 1 from
      • Strillacci M.G.
      • Frigo E.
      • Schiavini F.
      • Samore A.B.
      • Canavesi F.
      • Vevey M.
      • Cozzi M.C.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.
      ; Supplementary Material 2; https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ]. Twenty-nine markers were removed as they had negative positions [2 from
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      , 26 from
      • Strillacci M.G.
      • Frigo E.
      • Schiavini F.
      • Samore A.B.
      • Canavesi F.
      • Vevey M.
      • Cozzi M.C.
      • Soller M.
      • Lipkin E.
      • Bagnato A.
      Genome-wide association study for somatic cell score in Valdostana Red Pied cattle breed using pooled DNA.
      and 1 from
      • Wang X.
      • Ma P.P.
      • Liu J.F.
      • Zhang Q.
      • Zhang Y.
      • Ding X.D.
      • Jiang L.
      • Wang Y.C.
      • Zhang Y.
      • Sun D.X.
      • Zhang S.L.
      • Su G.S.
      • Yu Y.
      Genome-wide association study in Chinese Holstein cows reveal two candidate genes for somatic cell score as an indicator for mastitis susceptibility.
      ]. Moreover, change in chromosome number was reported for 38 markers (5 articles) and 5 windows (2 articles) during conversion. For 6 windows, start and end positions were reversed as start position was greater than end position. Finally, out of 40 studies qualified for further quantitative analysis, 37 studies resulted in 3,949 markers (3,708 unique markers) and 7 studies resulted in 499 windows (275 unique windows), with 4 studies [
      • Abdel-Shafy H.
      • Bortfeldt R.H.
      • Tetens J.
      • Brockmann G.A.
      Single nucleotide polymorphism and haplotype effects associated with somatic cell score in German Holstein cattle.
      ,
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      ,
      • Kurz J.P.
      • Yang Z.
      • Weiss R.B.
      • Wilson D.J.
      • Rood K.A.
      • Liu G.E.
      • Wang Z.D.
      A genome-wide association study for mastitis resistance in phenotypically well-characterized Holstein dairy cattle using a selective genotyping approach.
      ,
      • Meredith B.K.
      • Berry D.P.
      • Kearney F.
      • Finlay E.K.
      • Fahey A.G.
      • Bradley D.G.
      • Lynn D.J.
      A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility.
      ] reporting both markers and windows.

      Annotation of Markers and Windows

      A total of 19,403 positional candidate genes (10,624 genes from markers and 8,779 genes from windows) were annotated for markers and windows, resulting in a total of 9,125 unique positional candidate genes within 0.1 Mb interval from 39 studies with 1 gene in a sex chromosome (Supplementary Material 3; https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ). One SNP from
      • Freebern E.
      • Santos D.J.A.
      • Fang L.
      • Jiang J.
      • Parker Gaddis K.L.
      • Liu G.E.
      • VanRaden P.M.
      • Maltecca C.
      • Cole J.B.
      • Ma L.
      GWAS and fine-mapping of livability and six disease traits in Holstein cattle.
      was not annotated for either genes or QTL. Many annotated genes were in BTA 7 (1,264 genes) followed by BTA 19 (626 genes) and 18 (469 genes). In general, the proportion of genes shared among the 39 studies was low (mean ± SD = 0.02 ± 0.07; Supplementary Material 4; https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ).
      • Fang L.
      • Sahana G.
      • Su G.
      • Yu Y.
      • Zhang S.
      • Lund M.
      • Sorensen P.
      integrating sequence-based GWAS and RNA-seq provides novel insights into the genetic basis of mastitis and milk production in dairy cattle.
      shared a high proportion of genes with
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      and
      • Cai Z.
      • Guldbrandtsen B.
      • Lund M.S.
      • Sahana G.
      Prioritizing candidate genes post-GWAS using multiple sources of data for mastitis resistance in dairy cattle.
      ; 1 and 0.62, respectively). Likewise,
      • Wijga S.
      • Bastiaansen J.W.M.
      • Wall E.
      • Strandberg E.
      • de Haas Y.
      • Giblin L.
      • Bovenhuis H.
      Genomic associations with somatic cell score in first-lactation Holstein cows.
      shared 0.75 of genes with
      • Mulder H.A.
      • Crump R.E.
      • Calus M.P.L.
      • Veerkamp R.F.
      Unraveling the genetic architecture of environmental variance of somatic cell score using high-density single nucleotide polymorphism and cow data from experimental farms.
      . Moreover, genes GC, ENSBTAG00000049290, and NPFFR2 were reported by 9, 8, and 7 studies, whereas the remaining genes were reported by ≤6 studies.
      In terms of traits associated with resistance to mastitis, 52 types of traits were derived from 39 studies. These traits were mainly grouped into CM and SCC-related traits. Many annotated genes (8,664; unique genes per trait) were related to SCC-related traits, whereas fewer genes (930; unique genes per trait) were associated with CM-related traits (Figure 2a). However, 469 genes were shared between those 2 traits. Among 13 studies that conducted CM-related GWAS, in general, gene overlap among studies was low (mean ± SD = 0.02 ± 0.10; Figure 2b). However, among those studies,
      • Sahana G.
      • Guldbrandtsen B.
      • Thomsen B.
      • Lund M.S.
      Confirmation and fine-mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle.
      and
      • Fang L.
      • Sorensen P.
      • Sahana G.
      • Panitz F.
      • Su G.
      • Zhang S.
      • Yu Y.
      • Li B.
      • Ma L.
      • Liu G.
      • Lund M.
      • Thomsen B.
      MicroRNA-guided prioritization of genome-wide association signals reveals the importance of microRNA-target gene networks for complex traits in cattle.
      shared 39 genes. Similarly, gene overlap across studies which performed GWAS on SCC-related traits was in general also low (mean ± SD = 0.01 ± 0.06; Supplementary Material 5; https://doi.org/10.7910/DVN/HNKBJS ;
      • Narayana S.G.
      • de Jong E.
      • Schenkel F.S.
      • Fonseca P.A.S.
      • Chud T.C.S.
      • Powel D.
      • Wachoski-Dark G.
      • Ronksley P.E.
      • Miglior F.
      • Orsel K.
      • Barkema H.W.
      Supplementary materials: Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. Harvard Dataverse, V1.
      ). Interestingly,
      • Durán Aguilar M.
      • Roman Ponce S.I.
      • Ruiz Lopez F.J.
      • Gonzalez Padilla E.
      • Vasquez Pelaez C.G.
      • Bagnato A.
      • Strillacci M.G.
      Genome-wide association study for milk somatic cell score in Holstein cattle using copy number variation as markers.
      who used copy number variations of SCS to perform GWAS shared a high number of genes with
      • Cole J.B.
      • Wiggans G.R.
      • Ma L.
      • Sonstegard T.S.
      • Lawlor Jr., T.J.
      • Crooker B.A.
      • Van Tassell C.P.
      • Yang J.
      • Wang S.W.
      • Matukumalli L.K.
      • Da Y.
      Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows.
      and
      • Oliveira H.R.
      • Cant J.P.
      • Brito L.F.
      • Feitosa F.L.B.
      • Chud T.C.S.
      • Fonseca P.A.S.
      • Jamrozik J.
      • Silva F.F.
      • Lourenco D.A.L.
      • Schenkel F.S.
      Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.
      ,b). In terms of breeds used for GWAS analysis of resistance to mastitis, there were 5,843, 2,311, and 1,915 genes annotated for Holstein, Jersey and Ayrshire, respectively (the 3 main breeds; Figure 3). Furthermore, 74 common genes were identified among the 3 main breeds in the present study.
      Figure thumbnail gr2
      Figure 2Overlapping of genes among 13 clinical mastitis studies. Within the matrix, the intensity of colors corresponds to the proportion of shared genes. Dark red colored diagonals show the proportion of genes ( = 1; 100%) for the particular studies.
      Figure thumbnail gr3
      Figure 3Overlap of genes between Holstein, Jersey, and Ayrshire breeds; each color in the Venn diagram represents a breed.
      QTL annotation yielded 132,140 QTL (87,737 QTL from markers and 44,403 QTL from windows) which resulted in 43,646 unique QTL from 39 studies. A high number of QTL of 8,513, 6,450 and 2,780 were located in BTA 14, 6 and 20, respectively. A high proportion of 64% of annotated QTL were associated with milk QTL class, whereas only 7.5% was associated with health QTL class (Figure 4). Moreover, within health QTL class, 2.6 and 2.2% of QTL were associated with CM and SCC-related traits (SCC_SCS; Figure 5). Interestingly, a low percentage of QTL was also related to susceptibility to other diseases such as ketosis, bovine tuberculosis, and bovine respiratory disease. QTL enrichment analysis demonstrated that a high number of enriched QTL was associated with SCS in BTA 5, 6, 16, and 20 (Figure 6). Additionally, QTL enrichment analysis for CM resulted in fewer significant QTL on BTA 8, 10, 18, and 25.
      Figure thumbnail gr4
      Figure 4Percentage of total annotated QTL (132,140 QTL) in different QTL classes. The different colors in the pie chart represent different QTL classes, and the numbers are the corresponding QTL percentages.
      Figure thumbnail gr5
      Figure 5Percentage of annotated QTL associated with “Health” QTL class. SCC_SCS = somatic cell count-related traits.