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Short communication: Implementation of a breeding value for heat tolerance in Australian dairy cattle

Open ArchivePublished:July 12, 2017DOI:https://doi.org/10.3168/jds.2017-12898

      ABSTRACT

      Excessive ambient temperature and humidity can impair milk production and fertility of dairy cows. Selection for heat-tolerant animals is one possible option to mitigate the effects of heat stress. To enable selection for this trait, we describe the development of a heat tolerance breeding value for Australian dairy cattle. We estimated the direct genomic values of decline in milk, fat, and protein yield per unit increase of temperature-humidity index (THI) using 46,726 single nucleotide polymorphisms and a reference population of 2,236 sires and 11,853 cows for Holsteins and 506 sires and 4,268 cows for Jerseys. This new direct genomic value is the Australian genomic breeding value for heat tolerance (HT ABVg). The components of the HT ABVg are the decline in milk, fat, and protein per unit increase in THI when THI increases above the threshold of 60. These components are weighted by their respective economic values, assumed to be equivalent to the weights applied to milk, fat, and protein yield in the Australian selection indices. Within each breed, the HT ABVg is then standardized to have a mean of 100 and standard deviation (SD) of 5, which is consistent with the presentation of breeding values for many other traits in Australia. The HT ABVg ranged from −4 to +3 SD in Holsteins and −3 to +4 SD in Jerseys. The mean reliabilities of HT ABVg among validation sires, calculated from the prediction error variance and additive genetic variance, were 38% in both breeds. The range in ABVg and their reliability suggests that HT can be improved using genomic selection. There has been a deterioration in the genetic trend of HT, and to moderate the decline it is suggested that the HT ABVg should be included in a multitrait economic index with other traits that contribute to farm profit.

      Key words

      Short Communication

      Heat stress is an issue of growing concern for many livestock production systems worldwide, including the dairy industry in some regions of Australia. Heat stress affects not only animal welfare but also economic returns (
      • Kadzere C.T.
      • Murphy M.R.
      • Silanikove N.
      • Maltz E.
      Heat stress in lactating dairy cows: A review.
      ;
      • St-Pierre N.R.
      • Cobanov B.
      • Schnitkey G.
      Economic losses from heat stress by US livestock industries.
      ). When exposed to heat stress conditions, such as high temperature and humidity, cows produce less milk due to reduced feed intake and secretory function of the udder (
      • Silanikove N.
      Effects of water scarcity and hot environment on appetite and digestion in ruminants: A review.
      ). Heat stress is also known to reduce conception rates (
      • Hansen P.J.
      • Aréchiga C.F.
      Strategies for managing reproduction in the heat-stressed dairy cow.
      ) and increase the incidence of health problems and mortality rates in dairy cows (
      • Kadzere C.T.
      • Murphy M.R.
      • Silanikove N.
      • Maltz E.
      Heat stress in lactating dairy cows: A review.
      ;
      • Dikmen S.
      • Hansen P.J.
      Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment?.
      ).
      Mitigation of heat stress can be multifaceted and includes physical protection, nutritional management, and genetic improvement (
      • Beede D.K.
      • Collier R.J.
      Potential nutritional strategies for intensively managed cattle during thermal stress.
      ). Although efforts have been made at a herd management level to provide a suitable environment for cows (i.e., shade and cooling devices) and extensive research has been conducted in nutritional management for lactating cows in hot climates (
      • Beede D.K.
      • Collier R.J.
      Potential nutritional strategies for intensively managed cattle during thermal stress.
      ;
      • West J.W.
      Nutritional strategies for managing the heat-stressed dairy cow.
      ), genetic improvement for heat tolerance (HT) has not been practiced in the dairy industry. It is, however, widely recognized that genetic variation is associated with the performance of dairy cows under heat stress conditions, making the selection for this trait possible (
      • Bohmanova J.
      • Misztal I.
      • Tsuruta S.
      • Norman H.D.
      • Lawlor T.J.
      Genotype by environment interaction due to heat stress.
      ).
      In Australia, genomic selection for HT can take advantage of the genotypes from thousands of cows and bulls already available.
      • Nguyen T.T.T.
      • Bowman P.
      • Haile-Mariam M.
      • Pryce J.E.
      • Hayes B.J.
      Genomic selection for heat tolerance in Australian dairy cattle.
      used herd test-day and weather records over an 11-yr period to estimate tolerance to heat stress. They determined the extent of the decline in milk, fat, and protein yield as temperature-humidity index (THI) increased for Holsteins and Jerseys. Genomic EBV for HT were determined using high-density SNP data. These HT genomic EBV were also empirically validated through a heat challenge experiment in which genomically predicted heat-tolerant cows showed significantly less milk yield losses as well as lower rectal and intravaginal temperatures than the predicted heat-susceptible animals when challenged with a mild simulated heat wave (
      • Garner J.B.
      • Douglas M.
      • Williams R.S.O.
      • Wales W.J.
      • Nguyen T.T.T.
      • Hayes B.J.
      Genomic selection improves heat tolerance in dairy cattle.
      ).
      Here, we describe an implementation plan of the Australian genomic breeding values for HT (HT ABVg) for Holstein and Jersey dairy cattle. Definition, expression, and methods of how breeding values for HT could be derived and implemented are presented and discussed.
      Breeding values for HT can be expressed in several ways (
      • Nguyen T.T.T.
      • Hayes B.J.
      • Pryce J.E.
      A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle.
      ). Following consultation with Australian dairy industry representatives, HT is expressed as the decline in milk, fat, and protein yield per unit increase in THI when THI increases above the threshold of 60, weighted by their respective economic weights. It is then standardized to have a mean of 100 and standard deviation of 5 for each breed. This method of expressing the breeding value is typical of many breeding values evaluated for Australian dairy cattle. To calculate HT ABVg, first, SNP effects for the decline in milk, fat, and protein yield with increasing heat stress were calculated using the following steps.
      • (1)
        Calculation of phenotypes for HT for cows
        The data set used to calculate phenotypes for HT for cows was similar to that used by
        • Nguyen T.T.T.
        • Bowman P.
        • Haile-Mariam M.
        • Pryce J.E.
        • Hayes B.J.
        Genomic selection for heat tolerance in Australian dairy cattle.
        , with several improvements: (1) climate data (i.e., hourly temperature and relative humidity) were extended to include recent data (i.e., to August 2016); (2) the distance between herds and weather stations was measured using the GPS coordinates of each herd instead of its postcode centroid, where possible; and (3) herds that did not have a weather station within a 60-km radius were excluded from analyses. A summary of the final data set is given in Table 1. The rates of decline (slope) in milk, fat, and protein yield after a heat stress event were estimated using a reaction norm model (
        • Nguyen T.T.T.
        • Bowman P.
        • Haile-Mariam M.
        • Pryce J.E.
        • Hayes B.J.
        Genomic selection for heat tolerance in Australian dairy cattle.
        ). In this model, data on milk, fat, or protein yield were fitted with fixed effects of herd test day, year season of calving, parity, a third-degree polynomial on age at calving, parity × an eighth-degree polynomial on DIM, and stage of lactation × a linear polynomial on THI. Random effects included a random regression on a linear orthogonal polynomial of THI and a residual term. In the model, the threshold of THI was set to 60 following
        • Hayes B.J.
        • Carrick M.
        • Bowman P.
        • Goddard M.E.
        Genotype × environment interaction for milk production of daughters of Australian dairy sires from test-day records.
        . The analyses were conducted using ASReml (
        • Gilmour A.R.
        • Gogel B.J.
        • Cullis B.R.
        • Thompson R.
        ) and were done separately for Holsteins and Jerseys.
        Table 1Characteristics of the data sets used in the calculation of heat tolerance phenotypes for cows
        Item (no.)HolsteinJersey
        1st parity2nd parity3rd parity1st parity2nd parity3rd parity
        Records2,798,6692,057,8381,319,742591,434446,438309,217
        Herds1,9271,8411,650554537498
        Cows424,540325,026214,78784,70267,45147,520
        Sires10,2309,4768,2223,1752,9502,577
      • (2)
        Calculation of pseudophenotypes for HT for sires
        Using the cow slope solutions obtained in step 1, sire slopes or daughter trait deviations (equivalent to daughter yield deviations) for decline in milk, fat, and protein yield as THI increases were estimated.
      • (3)
        Calculation of SNP effects
        The SNP effects were calculated separately for the decline in milk, fat, and protein yield with increasing THI. The number of animals in the reference populations was 11,853 cows and 2,236 sires for Holsteins and 4,268 cows and 506 sires for Jerseys. The sires needed to have at least 5 daughters with phenotypes to be included in the reference sets. Genotypes for 46,726 SNP were obtained from DataGene (Melbourne, Australia) in September 2016. A genomic best linear unbiased prediction model was used to fit the daughter trait deviations obtained from step 2 (
        • Nguyen T.T.T.
        • Bowman P.
        • Haile-Mariam M.
        • Pryce J.E.
        • Hayes B.J.
        Genomic selection for heat tolerance in Australian dairy cattle.
        ) and then back solved to obtain SNP effects. The HT ABVg were then calculated using the following steps.
      • (4)
        Calculation of DGV for decline in milk, fat, and protein yields (kg) with heat stress
        The following equations were used to calculate direct genomic values (DGV) for decline in milk, fat, and protein yields (kg) with heat stress:
        DGVHT_trait=WbˆHT_trait,


        where W is the i animal × j SNP standardized genotype matrix, and
        W=(Xij2pj)/2pj(1pj),


        where Xij is the i animal × j SNP genotype (0, 1, or 2 for each genotype); pj is the frequency of allele 2 at SNP j (
        • Yang J.
        • Benyamin B.
        • McEvoy B.P.
        • Gordon S.
        • Henders A.K.
        • Nyholt D.R.
        • Madden P.A.
        • Heath A.C.
        • Martin N.G.
        • Montgomery G.W.
        • Goddard M.E.
        • Visscher P.M.
        Common SNPs explain a large proportion of the heritability for human height.
        ); and bˆHT_trait is bˆHT_milk, bˆHT_fat, or bˆHT_prot, which is the vector of SNP effects for decline of each trait (milk, fat, and protein yield) due to heat stress.
      • (5)
        Combination of DGV in each trait
        The following equation was used to calculate DGV in each trait to form a decline in milk production traits due to heat stress:
        DGVHT_ASI=EWm×DGVHT_milk+EWfat×DGVHT_fat+EWprot×DGVHT_prot,


        where DGVHT_ASI is the decline in the Australian selection index (ASI) in AU$ per unit increase in THI and EWm = −0.10, EWfat = 1.79, and EWprot = 6.92 are the economic weights of milk, fat, and protein yield, respectively, which are used by DataGene to calculate national dairy selection indices in Australia (
        • Byrne T.J.
        • Santos B.F.S.
        • Amer P.R.
        • Martin-Collado D.
        • Pryce J.E.
        • Axford M.
        Breeding objectives and indexes for the Australian dairy industry.
        ). Note that the ASI is a production trait only selection index, but the same weights are also applied in Australian multitrait selection indices that include traits aligning to costs of production.
      • (6)
        Expression of HT ABVg
        The following equation was used to calculate expression of HT ABVg:
        HTABVg=100+5×[DGVHT_ASImean(DGVHT_ASI)]/SD(DGVHT_ASI),


        where the mean and standard deviation were derived from a population that included reference and validation bulls used in this study.
      A cohort of 497 Holstein sires born in and after 2006 and 183 Jersey sires born in and after 2007 was used as a validation population. These animals and their daughters were not included in the reference sets. Regardless of year of birth, paternal half-sib sires were placed together in either the reference set or the validation set. The DGV for decline in milk, fat, and protein yield were calculated for validation sires. The reliability for HT ABVg was assumed to be equivalent to the reliability of HT on protein yield, which is similar to the approach used by DataGene to obtain the reliability of ASI (because protein has a greater economic value than milk and fat yield). The reliability was calculated as
      RELi=1SEi2σ2,


      where RELi is the reliability of DGV of the decline of protein yield with heat stress of individual i, SEi2 is the prediction error variance of individual i (obtained from the genomic best linear unbiased prediction model), and σ2 is the genomic variance of the decline of protein yield with heat stress.
      The HT ABVg ranged from 84 to 112 (−4 to +3 SD) in Holsteins and 86 to 117 (−3 to +4 SD) in Jerseys (Figure 1). Under this standardized expression, if a bull has a breeding value larger than 100, his daughters will be more heat tolerant than those of the average bull. Likewise, if a bull has a breeding value less than 100, his daughters will be less heat tolerant than those of the average bull.
      Figure thumbnail gr1
      Figure 1(a) Distribution of Australian genomic breeding values for heat tolerance in 497 Holstein (white bars) and 183 Jersey (gray bars) bulls without daughters in the reference and (b) corresponding reliability.
      The reliability of HT ABVg in genotyped Holstein bulls with no daughters in the reference set ranged from 16 to 54% and had a mean of 38% and a standard deviation of 7%. In Jerseys, the reliability of HT ABVg ranged from 15 to 54% and had a mean of 38% and a standard deviation of 9% (Figure 1b). Although the reference population of Jerseys is much smaller than that of Holsteins, a similar average reliability was achieved, probably because of the smaller effective population size in Jerseys (
      • Hayes B.J.
      • Bowman P.J.
      • Chamberlain A.C.
      • Verbyla K.
      • Goddard M.E.
      Accuracy of genomic breeding values in multi-breed dairy cattle populations.
      ). The reliability of HT ABVg is moderate and comparable with that of other economically important traits such as feed saved (mean reliability of 37% in genotyped Holstein bulls with no daughters;
      • Pryce J.E.
      • Gonzalez-Recio O.
      • Nieuwhof G.
      • Wales W.J.
      • Coffey M.P.
      • Hayes B.J.
      • Goddard M.E.
      Definition and implementation of a breeding value for feed efficiency in dairy cows.
      ). Although this level of reliability enables genetic gain to be made for HT, it is desirable to improve the reliability further by increasing the size of the reference population. In this regard, the Genomic Information Nucleus (Ginfo), which includes herds with high-quality phenotype recording, has been established in Australia (
      • Pryce J.E.
      • Douglas P.
      • Reich C.M.
      • Chamberlain A.J.
      • Bowman P.J.
      • Nguyen T.T.T.
      • Mason B.A.
      • Prowse-Wilkins C.P.
      • Nieuwhof G.J.
      • Hancock T.
      • Shaffer M.
      • Hayes B.J.
      Reliabilities of Australian dairy genomic breeding values increase through the addition of genotyped females with excellent phenotypes.
      ). Our experience shows that the inclusion of Ginfo cows in the reference populations increases reliabilities of young genotyped bulls by between 5 and 7% for Holsteins and between 2 and 3% for Jerseys, depending on traits (
      • Pryce J.E.
      • Douglas P.
      • Reich C.M.
      • Chamberlain A.J.
      • Bowman P.J.
      • Nguyen T.T.T.
      • Mason B.A.
      • Prowse-Wilkins C.P.
      • Nieuwhof G.J.
      • Hancock T.
      • Shaffer M.
      • Hayes B.J.
      Reliabilities of Australian dairy genomic breeding values increase through the addition of genotyped females with excellent phenotypes.
      ). For HT, adding 4,711 Holstein and 3,153 Jersey Ginfo cows in the corresponding reference populations improved reliability by 3 and 6%, respectively (
      • Nguyen T.T.T.
      • Garner J.B.
      • Bowman P.J.
      • Haile-Mariam M.
      • Pryce J.E.
      • Hayes B.J.
      Breeding value for heat tolerance in Australian dairy cattle: A technical platform for implementation.
      ). As the size of the Ginfo reference population is projected to increase over time, the reliability of HT ABVg and many other traits is expected to improve.
      Genetic trends show a slight decline in HT over time (Figure 2). This is expected given the unfavorable correlation of HT with milk, fat, and protein yield (
      • Nguyen T.T.T.
      • Bowman P.
      • Haile-Mariam M.
      • Pryce J.E.
      • Hayes B.J.
      Genomic selection for heat tolerance in Australian dairy cattle.
      ). Between the years 1990 and 2011, HT ABVg declined at the rate of 0.3%/yr in both breeds. This indicates that herds in a warmer climate may wish to take steps to prevent a further decline in HT. The best way to achieve this is to have HT included in the multitrait economic selection indices, such as the Australian balanced performance index (
      • Byrne T.J.
      • Santos B.F.S.
      • Amer P.R.
      • Martin-Collado D.
      • Pryce J.E.
      • Axford M.
      Breeding objectives and indexes for the Australian dairy industry.
      ), so that HT can be selected jointly with other economic drivers (
      • Nguyen T.T.T.
      • Hayes B.J.
      • Pryce J.E.
      A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle.
      ).
      Figure thumbnail gr2
      Figure 2Genetic trend of Australian genomic breeding values (ABVg) for heat tolerance in (a) 2,665 Holstein bulls and (b) 641 Jersey bulls that were born in and after 1990.
      The implementation of HT ABVg is considered an initial stage to improve HT in Australian dairy cattle. It is also possible to calculate conventional HT ABV using pedigree relationship instead of genomic relationship. This will allow every cow with good-quality herd test records to have an HT breeding value; thus, progress on HT can be monitored at the herd level and animals to breed for replacement can be selected.
      It is widely recognized that heat stress negatively affects not only production but also fertility and health traits (
      • Kadzere C.T.
      • Murphy M.R.
      • Silanikove N.
      • Maltz E.
      Heat stress in lactating dairy cows: A review.
      ;
      • Hansen P.J.
      Exploitation of genetic and physiological determinants of embryonic resistance to elevated temperature to improve embryonic survival in dairy cattle during heat stress.
      ;
      • Dikmen S.
      • Cole J.B.
      • Null D.J.
      • Hansen P.J.
      Heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle.
      ).
      • Nguyen T.T.T.
      • Bowman P.
      • Haile-Mariam M.
      • Pryce J.E.
      • Hayes B.J.
      Genomic selection for heat tolerance in Australian dairy cattle.
      found a favorable correlation between HT tolerance and fertility, suggesting that improvement in HT will help improve fertility. We also envisage that the methods described here could be applied across several health traits, such as mastitis resistance. If genetic variation of HT on these traits can be quantified, it is possible to breed for more robust dairy cattle that are tolerant to heat stress in terms of health and fertility and capable of producing high milk yield in varying climatic conditions.
      In conclusion, the present study proposes a new measure of genetic merit for the Australian dairy industry: the HT ABVg. This is a genomic-only breeding value that is expressed as the decline in milk, fat, and protein production per unit increase in THI above the threshold where heat stress affects production. Selection for HT is timely to prevent further deterioration in tolerance of heat stress. In this context, DataGene plans to release the HT ABVg in late 2017. The best strategy for achieving an optimal outcome is to select for improvement in HT in combination with other traits that contribute to profitability. Further investigations on genetic variation of HT on health traits and fertility will enable a better understanding of the effects of HT on overall performance of animals and net returns, thereby permitting the selection of more robust animals for a more resilient and profitable industry.

      ACKNOWLEDGMENTS

      This study was undertaken as part of the DairyBio program, which is cofunded by the Government of Victoria (Melbourne, Australia) and Dairy Australia (Melbourne, Australia). The authors thank DataGene (Melbourne, Australia) for providing production and genotype data.

      REFERENCES

        • Beede D.K.
        • Collier R.J.
        Potential nutritional strategies for intensively managed cattle during thermal stress.
        J. Anim. Sci. 1986; 62: 543-554
        • Bohmanova J.
        • Misztal I.
        • Tsuruta S.
        • Norman H.D.
        • Lawlor T.J.
        Genotype by environment interaction due to heat stress.
        J. Dairy Sci. 2008; 91 (18218772): 840-846
        • Byrne T.J.
        • Santos B.F.S.
        • Amer P.R.
        • Martin-Collado D.
        • Pryce J.E.
        • Axford M.
        Breeding objectives and indexes for the Australian dairy industry.
        J. Dairy Sci. 2016; 99 (27522425): 8146-8167
        • Dikmen S.
        • Cole J.B.
        • Null D.J.
        • Hansen P.J.
        Heritability of rectal temperature and genetic correlations with production and reproduction traits in dairy cattle.
        J. Dairy Sci. 2012; 95 (22612974): 3401-3405
        • Dikmen S.
        • Hansen P.J.
        Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment?.
        J. Dairy Sci. 2009; 92 (19109269): 109-116
        • Garner J.B.
        • Douglas M.
        • Williams R.S.O.
        • Wales W.J.
        • Nguyen T.T.T.
        • Hayes B.J.
        Genomic selection improves heat tolerance in dairy cattle.
        Nature Scientific Rep. 2016; 6: 1-8
        • Gilmour A.R.
        • Gogel B.J.
        • Cullis B.R.
        • Thompson R.
        ASReml User Guide Release 3.0. VSN International Ltd., Hemel Hempstead, United Kingdom2009
        • Hansen P.J.
        Exploitation of genetic and physiological determinants of embryonic resistance to elevated temperature to improve embryonic survival in dairy cattle during heat stress.
        Theriogenology. 2007; 68 (17482669): S242-S249
        • Hansen P.J.
        • Aréchiga C.F.
        Strategies for managing reproduction in the heat-stressed dairy cow.
        J. Anim. Sci. 1999; 77 (15526779): 36-50
        • Hayes B.J.
        • Bowman P.J.
        • Chamberlain A.C.
        • Verbyla K.
        • Goddard M.E.
        Accuracy of genomic breeding values in multi-breed dairy cattle populations.
        Genet. Sel. Evol. 2009; 41 (19930712): 51
        • Hayes B.J.
        • Carrick M.
        • Bowman P.
        • Goddard M.E.
        Genotype × environment interaction for milk production of daughters of Australian dairy sires from test-day records.
        J. Dairy Sci. 2003; 86 (14672205): 3736-3744
        • Kadzere C.T.
        • Murphy M.R.
        • Silanikove N.
        • Maltz E.
        Heat stress in lactating dairy cows: A review.
        Livest. Prod. Sci. 2002; 77: 59-91
        • Nguyen T.T.T.
        • Bowman P.
        • Haile-Mariam M.
        • Pryce J.E.
        • Hayes B.J.
        Genomic selection for heat tolerance in Australian dairy cattle.
        J. Dairy Sci. 2016; 99 (27037467): 2849-2862
        • Nguyen T.T.T.
        • Garner J.B.
        • Bowman P.J.
        • Haile-Mariam M.
        • Pryce J.E.
        • Hayes B.J.
        Breeding value for heat tolerance in Australian dairy cattle: A technical platform for implementation.
        in: Proc. HERD 17. DataGene, Bendigo, Australia2017: 35-39
        • Nguyen T.T.T.
        • Hayes B.J.
        • Pryce J.E.
        A practical future-scenarios selection tool to breed for heat tolerance in Australian dairy cattle.
        Anim. Prod. Sci. 2016; 57: 1488-1493
        • Pryce J.E.
        • Douglas P.
        • Reich C.M.
        • Chamberlain A.J.
        • Bowman P.J.
        • Nguyen T.T.T.
        • Mason B.A.
        • Prowse-Wilkins C.P.
        • Nieuwhof G.J.
        • Hancock T.
        • Shaffer M.
        • Hayes B.J.
        Reliabilities of Australian dairy genomic breeding values increase through the addition of genotyped females with excellent phenotypes.
        Proc. Assoc. Admt. Anim. Breed. Genet. 2016; 22 (In press.)
        • Pryce J.E.
        • Gonzalez-Recio O.
        • Nieuwhof G.
        • Wales W.J.
        • Coffey M.P.
        • Hayes B.J.
        • Goddard M.E.
        Definition and implementation of a breeding value for feed efficiency in dairy cows.
        J. Dairy Sci. 2015; 98 (26254533): 7340-7350
        • Silanikove N.
        Effects of water scarcity and hot environment on appetite and digestion in ruminants: A review.
        Livest. Prod. Sci. 1992; 30: 175-194
        • St-Pierre N.R.
        • Cobanov B.
        • Schnitkey G.
        Economic losses from heat stress by US livestock industries.
        J. Dairy Sci. 2003; 86 (12613848): E52-E77
        • West J.W.
        Nutritional strategies for managing the heat-stressed dairy cow.
        J. Dairy Sci. 1998; 82: 21-35
        • Yang J.
        • Benyamin B.
        • McEvoy B.P.
        • Gordon S.
        • Henders A.K.
        • Nyholt D.R.
        • Madden P.A.
        • Heath A.C.
        • Martin N.G.
        • Montgomery G.W.
        • Goddard M.E.
        • Visscher P.M.
        Common SNPs explain a large proportion of the heritability for human height.
        Nat. Genet. 2010; 42 (20562875): 565-569