Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-23T17:22:01.764Z Has data issue: false hasContentIssue false

Application of the a posteriori granddaughter design to the Holstein genome

Published online by Cambridge University Press:  18 March 2014

J. I. Weller*
Affiliation:
The Volcani Center, Institute of Animal Sciences, A.R.O., Bet Dagan 50250, Israel
J. B. Cole
Affiliation:
Animal Improvement Programs Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD 20705-2350, USA
P. M. VanRaden
Affiliation:
Animal Improvement Programs Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD 20705-2350, USA
G. R. Wiggans
Affiliation:
Animal Improvement Programs Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD 20705-2350, USA
Get access

Abstract

An a posteriori granddaughter design was applied to estimate quantitative trait loci genotypes of sires with many sons in the US Holstein population. The results of this analysis can be used to determine concordance between specific polymorphisms and segregating quantitative trait loci. Determination of the actual polymorphisms responsible for observed genetic variation should increase the accuracy of genomic evaluations and rates of genetic gain. A total of 52 grandsire families, each with ⩾100 genotyped sons with genetic evaluations based on progeny tests, were analyzed for 33 traits (milk, fat and protein yields; fat and protein percentages; somatic cell score (SCS); productive life; daughter pregnancy rate; heifer and cow conception rates; service-sire and daughter calving ease; service-sire and daughter stillbirth rates; 18 conformation traits; and net merit). Of 617 haplotype segments spanning the entire bovine genome and each including ~5×106 bp, 5 cM and 50 genes, 608 autosomal segments were analyzed. A total of 19 335 unique haplotypes were found among the 52 grandsires. There were a total of 133 chromosomal segment-by-trait combinations, for which the nominal probability of significance for the haplotype effect was <10−8, which corresponds to genome-wide significance of <10−4. The number of chromosomal regions that met this criterion by trait ranged from one for rear legs (rear view) to seven for net merit. For each of the putative quantitative trait loci, at least one grandsire family had a within-family contrast with a t-value of >3. Confidence intervals (CIs) were estimated by the nonparametric bootstrap for the largest effect for each of nine traits. The bootstrap distribution generated by 100 samples was bimodal only for net merit, which had the widest 90% CI (eight haplotype segments). This may be due to the fact that net merit is a composite trait. For all other chromosomes, the CI spanned less than a third of the chromosome. The narrowest CI (a single haplotype segment) was found for SCS. It is likely that analysis by more advanced methods could further reduce CIs at least by half. These results can be used as a first step to determine the actual polymorphisms responsible for observed quantitative variation in dairy cattle.

Type
Full Paper
Copyright
© The Animal Consortium 2014. Parts of this are a work of the U.S. Government and not subject to copyright protection in the United States. 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ashwell, MS, Heyen, DW, Sonstegard, TS, Van Tassell, CP, Da, Y, VanRaden, PM, Ron, M, Weller, JI and Lewin, HA 2004. Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle. Journal of Dairy Science 87, 468475.Google Scholar
Bennewitz, J, Reinsch, N and Kalm, E 2002. Improved confidence intervals in quantitative trait loci mapping by permutation bootstrapping. Genetics 160, 16731686.Google Scholar
Bennewitz, J, Reinsch, N, Guiard, V, Fritz, S, Thomsen, H, Looft, C, Kühn, C, Schwerin, M, Weimann, C, Erhardt, G, Reinhardt, F, Reents, R, Boichard, D and Kalm, E 2004. Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design. Genetics 168, 10191027.Google Scholar
Cohen-Zinder, M, Seroussi, E, Larkin, DM, Loor, JJ, Everts-van der Wind, A, Lee, JH, Drackley, JK, Band, MR, Hernandez, AG, Shani, M, Lewin, HA, Weller, JI and Ron, M 2005. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Research 15, 936944.Google Scholar
Cole, JB, VanRaden, PM and Multi-State Project S-1040 2010. Net merit as a measure of lifetime profit: 2010 revision AIPL Res Rep NM$4(12-09). Retrieved April 4, 2013 from http://aipl.arsusda.gov/reference/nmcalc-2010.htm Google Scholar
Cole, JB, VanRaden, PM, O'Connell, JR, Van Tassell, CP, Sonstegard, TS, Schnabel, RD, Taylor , JF and Wiggans, GR 2009. Distribution and location of genetic effects for dairy traits. Journal of Dairy Science 92, 29312946.Google Scholar
Cole, JB, Wiggans, GR, Ma, L, Sonstegard, TS, Lawlor, TJ Jr, Crooker, BA, Van Tassel, CP, Yang, J, Wang, S, Matukumalli, LK and Da, Y 2011. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary US Holstein cows. BMC Genomics 12, 408.Google Scholar
Darvasi, A and Soller, M 1997. A simple method to calculate resolving power and confidence interval of QTL map location behavior. Genetics 27, 125132.Google Scholar
Darvasi, A, Weinreb, A, Minke, V, Weller, JI and Soller, M 1993. Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map. Genetics 134, 943951.Google Scholar
Druet, T and Georges, M 2010. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics 184, 789798.Google Scholar
Georges, M, Nielsen, D, Mackinnon, M, Mishra, A, Okimoto, R, Pasquino, AT, Sargeant, LS, Sorensen, A, Steele, MR, Zhao, X, Womack, JE and Hoeschele, I 1995. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics 139, 907920.Google Scholar
Grisart, B, Coppieters, W, Farnir, F, Karim, L, Ford, C, Berzi, P, Cambisano, N, Mni, M, Reid, S, Simon, P, Spelman, R, Georges, M and Snell, R 2002. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Research 12, 222231.Google Scholar
Illumina Inc 2011. BovineSNP50 Genotyping BeadChip. Retrieved April 4, 2013 from http://www.illumina.com/Documents/products/datasheets/datasheet_bovine_snp5O.pdf Google Scholar
Korol, AB, Ronin, YI and Kirzhner, VM 1995. Interval mapping of quantitative trait loci employing correlated trait complexes. Genetics 140, 11371147.CrossRefGoogle ScholarPubMed
Lander, E and Kruglyak, L 1995. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nature Genetics 11, 241247.Google Scholar
Lander, ES and Botstein, D 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185199.Google Scholar
Lund, MS, Guldbrandtsen, B, Buitenhuis, AJ, Thomsen, B and Bendixen, C 2008. Detection of quantitative trait loci in Danish Holstein cattle affecting clinical mastitis, somatic cell score, udder conformation traits, and assessment of associated effects on milk yield. Journal of Dairy Science 91, 40284036.Google Scholar
Meuwissen, THE and Goddard, ME 2004. Mapping multiple QTL using linkage disequilibrium and linkage analysis information and multitrait data. Genetics Selection Evolution 36, 261279.CrossRefGoogle ScholarPubMed
Meuwissen, THE, Karlsen, A, Lien, S, Olsaker, I and Goddard, ME 2002. Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. Genetics 161, 373379.Google Scholar
Olsen, HG, Lien, S, Gautier, M, Nilsen, H, Roseth, A, Berg, PR, Sundsaasen, M, Svendsen, KK and Meuwissen, TH 2005. Mapping of a milk production QTL to a 420 kb region on bovine chromosome 6. Genetics 169, 275283.CrossRefGoogle ScholarPubMed
Ron, M and Weller, JI 2007. From QTL to QTN identification in livestock – winning by points rather than knock-out: a review. Animal Genetics 38, 429439.Google Scholar
Ron, M, Feldmesser, E, Golik, M, Tager-Cohen, I, Kliger, D, Reiss, V, Domochovsky, R, Alus, O, Seroussi, E, Ezra, E and Weller, JI 2004. A complete genome scan of the Israeli Holstein population for quantitative trait loci by a daughter design. Journal of Dairy Science 87, 476490.Google Scholar
SAS Institute Inc 2012. Base SAS® 9.3 Procedures Guide, 2nd edition, SAS Institute Inc, Cary, NC.Google Scholar
Seroussi, E, Glick, G, Shirak, A, Yakobson, E, Weller, JI, Ezra, E and Zeron, Y 2010. Analysis of copy loss and gain variations in Holstein cattle autosomes using BeadChip SNPs. BMC Genomics 11, 673.CrossRefGoogle ScholarPubMed
VanRaden, P 2011. findhap.f90. Retrieved April 4, 2013 from http://aipl.arsusda.gov/software/findhap/ Google Scholar
VanRaden, PM and Wiggans, GR 1991. Derivation, calculation, and use of national animal model information. Journal of Dairy Science 74, 27372746.Google Scholar
VanRaden, PM, Van Tassel, CP, Wiggans, GR, Sonstegard, TS, Schnabel, RD, Taylor, JF and Schenkel, FS 2009. Invited review: reliability of genomic predictions for North American Holstein bulls. Journal of Dairy Science 92, 1624.Google Scholar
Visscher, PM, Thompson, R and Haley, CS 1996. Confidence intervals in QTL mapping by bootstrapping. Genetics 143, 10131020.Google Scholar
Weller, JI 2007. Marker-assisted selection in dairy cattle. Marker-assisted selection: current status and future perspectives in crops, livestock, forestry and fish (ed. EP Guimarães, J Ruane, BD Scherf, A Sonnino and JD Dargie), 199228. Food and Agriculture Organization of the United Nations, Rome, Italy.Google Scholar
Weller, JI and Ron, M 2011. Invited review: quantitative trait nucleotide determination in the era of genomic selection. Journal of Dairy Science 94, 10821090.Google Scholar
Weller, JI and Soller, M 2004. An analytical formula to estimate confidence interval of QTL location with a saturated genetic map as a function of experimental design. Theoretical and Applied Genetics 109, 12241229.Google Scholar
Weller, JI, Kashi, Y and Soller, M 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. Journal of Dairy Science 73, 25252537.Google Scholar
Weller, JI, VanRaden, PM and Wiggans, GR 2013. Application of a posteriori granddaughter and modified granddaughter designs to determine Holstein haplotype effects. Journal of Dairy Science 96, 53765387.Google Scholar
Weller, JI, Weller, H, Kliger, D and Ron, M 2002. Estimation of quantitative trait locus allele frequency via a modified granddaughter design. Genetics 162, 841849.CrossRefGoogle Scholar
Winter, A, Krämer, W, Werner, FAO, Kollers, S, Kata, S, Durstewitz, G, Buitkamp, J, Womack, JE, Thaller, G and Fries, R 2002. Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk. Proceeding of the National Academy of Sciences USA 99, 93009305.Google Scholar
Supplementary material: File

Weller supplementary material 1

Weller supplementary material 1

Download Weller supplementary material 1(File)
File 658.2 KB
Supplementary material: File

Weller supplementary material 2

Weller supplementary material 2

Download Weller supplementary material 2(File)
File 626.3 KB
Supplementary material: File

Weller supplementary material 3

Weller supplementary material 3

Download Weller supplementary material 3(File)
File 698.8 KB
Supplementary material: File

Weller supplementary material 4

Weller supplementary material 4

Download Weller supplementary material 4(File)
File 201.4 KB