Danai Jattawa, Thanathip Suwanasopee, Mauricio A Elzo, Skorn Koonawootrittriron
{"title":"在泰国多品种基因组多基因评估中纳入非基因分型奶牛的推算基因型。","authors":"Danai Jattawa, Thanathip Suwanasopee, Mauricio A Elzo, Skorn Koonawootrittriron","doi":"10.5713/ab.24.0317","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study assessed the impact of incorporating imputed SNP information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy.</p><p><strong>Methods: </strong>Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects.</p><p><strong>Results: </strong>The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic EBVs from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003).</p><p><strong>Conclusion: </strong>Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.</p>","PeriodicalId":7825,"journal":{"name":"Animal Bioscience","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation.\",\"authors\":\"Danai Jattawa, Thanathip Suwanasopee, Mauricio A Elzo, Skorn Koonawootrittriron\",\"doi\":\"10.5713/ab.24.0317\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>This study assessed the impact of incorporating imputed SNP information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy.</p><p><strong>Methods: </strong>Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects.</p><p><strong>Results: </strong>The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic EBVs from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003).</p><p><strong>Conclusion: </strong>Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.</p>\",\"PeriodicalId\":7825,\"journal\":{\"name\":\"Animal Bioscience\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2024-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Animal Bioscience\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.5713/ab.24.0317\",\"RegionNum\":2,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, DAIRY & ANIMAL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Animal Bioscience","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.5713/ab.24.0317","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
摘要
目的本研究评估了在泰国多品种奶牛群体中,在不同的归因准确性水平下,纳入非基因分型动物的归因 SNP 信息对基因组-多基因评估的影响:数据包括12859头初产奶牛的305天产奶量(MY)、305天脂肪(Fat)和初产年龄(AFC)的血统和表型记录,以及4364头动物的不同密度的基因型记录。使用 GeneSeek Genomic Profiler 80K 进行基因分型并有四个或更多基因分型后代的一组 64 头动物被定义为目标动物,以模拟非基因分型个体的估算情况。实际基因型和估算基因型被用来构建三个 SNP 集。所有 SNP 组都包含来自基因分型动物的实际和估算 SNP 标记。SNP 集 1 不包含来自目标动物的 SNP,SNP 集 2 包含来自目标动物的推算 SNP,SNP 集 3 添加了来自目标动物的实际 SNP。基因组-多基因评估采用 3 性状单步模型进行,该模型将当代组、产犊年龄和杂合度作为固定效应,将动物附加基因和残差作为随机效应:无论基因分型后代的数量如何,非基因分型动物的估算准确率相似(平均:40.55%;范围:34.68% 至 53.82%)。不同 SNP 组对 MY 和 AFC 的加性遗传变异、环境变异和协方差的估计值各不相同。与 SNP 组 3 相比,SNP 组 1 和 2 的加性遗传变异和协方差略高,环境变异和协方差略低。在所有 SNP 组中,MY、脂肪和 AFC 之间的遗传力和加性遗传、环境和表型相关性相似。对于所有性状,SNP 集 2 和 3 的基因组-多基因 EBV 之间的 Spearman 等级相关性都很高(0.9990±0.0003):利用来自非基因分型动物的表型和血统数据,提高了泰国多品种奶牛群体遗传改良计划的效率和成本效益。
Inclusion of imputed genotypes from non-genotyped dairy cattle in a Thai multibreed genomic-polygenic evaluation.
Objective: This study assessed the impact of incorporating imputed SNP information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy.
Methods: Data encompassed pedigree and phenotypic records for 305-day milk yield (MY), 305-day fat (Fat), and age at first calving (AFC) from 12,859 first-lactation cows, and genotypic records of various densities from 4,364 animals. A set of 64 animals genotyped with GeneSeek Genomic Profiler 80K and with four or more genotyped progenies was defined as target animals to simulate imputation scenarios for non-genotyped individuals. Actual and imputed genotypes were utilized to construct three SNP sets. All SNP Sets contained actual and imputed SNP markers from genotyped animals. SNP Set 1 contained no SNPs from target animals, whereas SNP Set 2 incorporated imputed SNPs from target animals, and SNP Set 3 added actual SNPs from target animals. Genomic-polygenic evaluations were conducted using a 3-trait single-step model that included contemporary group, calving age, and heterozygosity as fixed effects and animal additive genetic and residual as random effects.
Results: The imputation accuracy was similar across non-genotyped animals irrespective of the number of genotyped progenies (average: 40.55%; range: 34.68% to 53.82%). Estimates of additive genetic and environmental variances and covariances for MY and AFC varied across SNP sets. SNP Sets 1 and 2 had slightly higher additive genetic and lower environmental variances and covariances than SNP Set 3. Heritabilities and additive genetic, environmental, and phenotypic correlations between MY, Fat, and AFC were similar across all SNP Sets. Spearman rank correlations between genomic-polygenic EBVs from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003).
Conclusion: Utilization of phenotypic and pedigree data from imputed non-genotyped animals enhanced the efficiency and cost-effectiveness of the genetic improvement program in the Thai multibreed dairy cattle population.