期刊论文详细信息
BMC Genetics
Improved estimation of inbreeding and kinship in pigs using optimized SNP panels
Egbert F Knol1  Simone EF Guimarães2  Paulo S Lopes2  Naomi Duijvesteijn1  Barbara Harlizius1  Fabyano F Silva2  Marcos S Lopes1 
[1] TOPIGS Research Center IPG B.V., P.O. Box 43, 6640 AA, Beuningen, the Netherlands;Departamento de Zootecnia, Universidade Federal de Viçosa, 36571-000, Viçosa, MG, Brazil
关键词: Relationship;    Genomic selection;    Pedigree;    Bootstrap;    Linkage equilibrium;   
Others  :  1086611
DOI  :  10.1186/1471-2156-14-92
 received in 2012-11-16, accepted in 2013-09-19,  发布年份 2013
PDF
【 摘 要 】

Background

Traditional breeding programs consider an average pairwise kinship between sibs. Based on pedigree information, the relationship matrix is used for genetic evaluations disregarding variation due to Mendelian sampling. Therefore, inbreeding and kinship coefficients are either over or underestimated resulting in reduction of accuracy of genetic evaluations and genetic progress. Single nucleotide polymorphism (SNPs) can be used to estimate pairwise kinship and individual inbreeding more accurately. The aim of this study was to optimize the selection of markers and determine the required number of SNPs for estimation of kinship and inbreeding.

Results

A total of 1,565 animals from three commercial pig populations were analyzed for 28,740 SNPs from the PorcineSNP60 Beadchip. Mean genomic inbreeding was higher than pedigree-based estimates in lines 2 and 3, but lower in line 1. As expected, a larger variation of genomic kinship estimates was observed for half and full sibs than for pedigree-based kinship reflecting Mendelian sampling. Genomic kinship between father-offspring pairs was lower (0.23) than the estimate based on pedigree (0.26). Bootstrap analyses using six reduced SNP panels (n = 500, 1000, 1500, 2000, 2500 and 3000) showed that 2,000 SNPs were able to reproduce the results very close to those obtained using the full set of unlinked markers (n = 7,984-10,235) with high correlations (inbreeding r > 0.82 and kinship r > 0.96) and low variation between different sets with the same number of SNPs.

Conclusions

Variation of kinship between sibs due to Mendelian sampling is better captured using genomic information than the pedigree-based method. Therefore, the reduced sets of SNPs could generate more accurate kinship coefficients between sibs than the pedigree-based method. Variation of genomic kinship of father-offspring pairs is recommended as a parameter to determine accuracy of the method rather than correlation with pedigree-based estimates. Inbreeding and kinship coefficients can be estimated with high accuracy using ≥2,000 unlinked SNPs within all three commercial pig lines evaluated. However, a larger number of SNPs might be necessary in other populations or across lines.

【 授权许可】

   
2013 Lopes et al.; licensee BioMed Central Ltd.

【 预 览 】
附件列表
Files Size Format View
20150116013443169.pdf 887KB PDF download
Figure 5. 33KB Image download
Figure 4. 76KB Image download
Figure 3. 35KB Image download
Figure 2. 26KB Image download
Figure 1. 38KB Image download
【 图 表 】

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

【 参考文献 】
  • [1]Henderson CR: Best linear unbiased estimation and prediction under a selection model. Biometrics 1975, 31:423-447.
  • [2]Visscher PM, Medland SE, Ferreira MAR, Morley KI, Zhu G, Cornes BK, Montgomery GW, Martin NG: Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet 2006, 2(3):e41.
  • [3]Li CC, Horvitz DG: Some methods of estimating the inbreeding coefficient. Am J Hum Genet 1953, 5(2):107.
  • [4]Queller DC, Goodnight KF: Estimating relatedness using genetic markers. Evolution 1989, 43(2):258-275.
  • [5]Ritland K: Estimators for pairwise relatedness and individual inbreeding coefficients. Genet Res 1996, 67(2):175-186.
  • [6]Lynch M, Ritland K: Estimation of pairwise relatedness with molecular markers. Genetics 1999, 152(4):1753-1766.
  • [7]Hardy OJ, Vekemans X: SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2002, 2(4):618-620.
  • [8]Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, De Bakker PIW, Daly MJ: PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Human Genet 2007, 81(3):559-575.
  • [9]VanRaden PM: Efficient methods to compute genomic predictions. J Dairy Sci 2008, 91(11):4414-4423.
  • [10]Aulchenko YS, Ripke S, Isaacs A, van Duijn CM: GenABEL: an R package for genome-wide association analysis. Bioinformatics 2007, 23(10):1294-1296.
  • [11]Garant D, Kruuk LEB: How to use molecular marker data to measure evolutionary parameters in wild populations. Mol Ecol 2005, 14(7):1843-1859.
  • [12]Santure AW, Stapley J, Ball AD, Birkhead TIMR, Burke T, Slate JON: On the use of large marker panels to estimate inbreeding and relatedness: empirical and simulation studies of a pedigreed zebra finch population typed at 771 SNPs. Mol Ecol 2010, 19(7):1439-1451.
  • [13]Guo SW: Variation in genetic identity among relatives. Hum Hered 1996, 46(2):61-70.
  • [14]Bolormaa S, Ruvinsky A, Walkden-Brown S, Van der Werf J: DNA-based parentage verification in two Australian goat herds. Small Ruminant Res 2008, 80(1):95-100.
  • [15]Hill WG, Salisbury BA, Webb AJ: Parentage identification using single nucleotide polymorphism genotypes: application to product tracing. J Anim Sci 2008, 86(10):2508-2517.
  • [16]Fisher PJ, Malthus B, Walker MC, Corbett G, Spelman RJ: The number of single nucleotide polymorphisms and on-farm data required for whole-herd parentage testing in dairy cattle herds. J Dairy Sci 2009, 92(1):369-374.
  • [17]Hara K, Watabe H, Sasazaki S, Mukai F, Mannen H: Development of SNP markers for individual identification and parentage test in a Japanese Black cattle population. Anim Sci J 2010, 81(2):152-157.
  • [18]Harlizius B, Lopes MS, Duijvesteijn N, van de Goor LHP, van Haeringen WA, Panneman H, Guimarães SEF, Merks JWM, Knol EF: A SNP set for paternal identification to reduce the costs of trait recording in commercial pig breeding. J Anim Sci 2011, 89(6):1661-1668.
  • [19]Rolf MM, Taylor JF, Schnabel RD, McKay SD, McClure MC, Northcutt SL, Kerley MS, Weaber RL: Impact of reduced marker set estimation of genomic relationship matrices on genomic selection for feed efficiency in Angus cattle. BMC genetics 2010, 11(1):24.
  • [20]Pinto N, Gusmão L, Amorim A: X-chromosome markers in kinship testing: a generalisation of the IBD approach identifying situations where their contribution is crucial. Forensic Sci Int Genet 2011, 5(1):27-32.
  • [21]VanRaden PM, Olson KM, Wiggans GR, Cole JB, Tooker ME: Genomic inbreeding and relationships among Holsteins, Jerseys, and Brown Swiss. J Dairy Sci 2011, 94(11):5673-5682.
  • [22]Powell JE, Visscher PM, Goddard ME: Reconciling the analysis of IBD and IBS in complex trait studies. Nat Rev Genet 2010, 11(11):800-805.
  • [23]Keller MC, Visscher PM, Goddard ME: Quantification of inbreeding due to distant ancestors and its detection using dense single nucleotide polymorphism data. Genetics 2011, 189(1):237-249.
  • [24]Ramos AM, Crooijmans RPMA, Affara NA, Amaral AJ, Archibald AL, Beever JE, Bendixen C, Churcher C, Clark R, Dehais P: Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology. PLoS ONE 2009, 4(8):e6524.
  • [25]Pedersen LD, Sørensen AC, Berg P: Marker‒assisted selection reduces expected inbreeding but can result in large effects of hitchhiking. J Anim Breed Genet 2010, 127(3):189-198.
  • [26]Pimentel ECG, Erbe M, Koenig S, Simianer H: Genome partitioning of genetic variation for milk production and composition traits in Holstein cattle. Frontiers in genetics 2011, 2:19.
  • [27]Weir BS, Anderson AD, Hepler AB: Genetic relatedness analysis: modern data and new challenges. Nat Rev Genet 2006, 7(10):771-780.
  • [28]Sandler L, Hiraizumi Y, Sandler I: Meiotic drive in natural populations of Drosophila Melanogaster. I. the cytogenetic basis of segregation-distortion. Genet 1959, 44(2):233-250.
  • [29]Zhan H, Xu S: Generalized linear mixed model for segregation distortion analysis. BMC genetics 2011, 12(1):97.
  • [30]Hayes BJ, Visscher P, Goddard M: Increased accuracy of artificial selection by using the realized relationship matrix. Genet Res 2009, 91(01):47-60.
  • [31]Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, Madden PA, Heath AC, Martin NG, Montgomery GW: Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010, 42(7):565-569.
  • [32]Ai H, Huang L, Ren J: Genetic diversity, linkage disequilibrium and selection signatures in Chinese and Western pigs revealed by genome-wide SNP markers. PLoS ONE 2013, 8(2):e56001.
  • [33]Tortereau F, Servin B, Frantz L, Megens H-J, Milan D, Rohrer G, Wiedmann R, Beever J, Archibald A, Schook L: A high density recombination map of the pig reveals a correlation between sex-specific recombination and GC content. BMC Genomics 2012, 13(1):586. BioMed Central Full Text
  • [34]Arias JA, Keehan M, Fisher P, Coppieters W, Spelman R: A high density linkage map of the bovine genome. BMC genetics 2009, 10(1):18.
  • [35]Veroneze R, Lopes PS, Guimarães SEF, Silva FF, Lopes MS, Harlizius B, Knol EF: Linkage disequilibrium and haplotype block structure in six commercial pig lines. J Anim Sci 2013, 91:3493-3501.
  • [36]Qanbari S, Pimentel E, Tetens J, Thaller G, Lichtner P, Sharifi A, Simianer H: The pattern of linkage disequilibrium in German Holstein cattle. Anim Genet 2010, 41(4):346-356.
  • [37]Clark SA, Hickey JM, Daetwyler HD, van der Werf JH: The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol 2012, 44(1):4. BioMed Central Full Text
  • [38]Duijvesteijn N, Knol EF, Merks JWM, Crooijmans RPMA, Groenen MAM, Bovenhuis H, Harlizius B: A genome-wide association study on androstenone levels in pigs reveals a cluster of candidate genes on chromosome 6. BMC genetics 2010, 11(1):42.
  • [39]R Development Core Team R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
  • [40]Jacquard A: The genetic structure of populations. New York: Springer; 1974.
  • [41]Lynch M, Walsh B: Genetics and analysis of quantitative traits. Massachusetts: Sinauer Associates; 1998.
  • [42]Kalinowski ST, Taper ML, Marshall TC: Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol 2007, 16(5):1099-1106.
  • [43]Gutiérrez JP, Goyache F: A note on ENDOG: a computer program for analysing pedigree information. J Anim Breed Genet 2005, 122(3):172-176.
  • [44]Meuwissen THE, Luo Z: Computing inbreeding coefficients in large populations. Genet Sel Evol 1992, 24(4):305-313. BioMed Central Full Text
  文献评价指标  
  下载次数:0次 浏览次数:11次