Jattawa Danai, Suwanasopee Thanathip, Elzo Mauricio A, Koonawootrittriron Skorn
Department of Animal Science, Faculty of Agriculture, Kasetsart University, Bangkok 10900, Thailand.
Tropical Animal Genetic Special Research Unit (TAGU), Kasetsart University, Bangkok 10900, Thailand.
Anim Biosci. 2025 Mar;38(3):419-430. doi: 10.5713/ab.24.0317. Epub 2024 Oct 24.
This study assessed the impact of incorporating imputed single nucleotide polymorphism (SNP) information from non-genotyped animals on genomic-polygenic evaluations in a Thai multibreed dairy population under various levels of imputation accuracy.
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.
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 estimated breeding values from SNP Sets 2 and 3 were high for all traits (0.9990±0.0003).
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.
本研究评估了在不同水平的插补准确性下,纳入非基因分型动物的推算单核苷酸多态性(SNP)信息对泰国多品种奶牛群体基因组多基因评估的影响。
数据包括12859头头胎泌乳奶牛的305天产奶量(MY)、305天乳脂量(Fat)和初产年龄(AFC)的系谱和表型记录,以及4364头动物不同密度的基因型记录。一组64头用GeneSeek基因组分析80K进行基因分型且有四个或更多基因分型后代的动物被定义为目标动物,以模拟非基因分型个体的插补情况。实际基因型和推算基因型用于构建三个SNP集。所有SNP集都包含来自基因分型动物的实际和推算SNP标记。SNP集1不包含来自目标动物的SNP,而SNP集2纳入了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。使用三性状单步模型进行基因组多基因评估,该模型包括当代组、产犊年龄和杂合度作为固定效应,动物加性遗传效应和残差作为随机效应。
无论基因分型后代的数量如何,非基因分型动物的插补准确性相似(平均:40.55%;范围:34.68%至53.82%)。MY和AFC的加性遗传方差、环境方差以及协方差估计值在不同SNP集之间有所不同。SNP集1和2的加性遗传方差略高于SNP集3,环境方差和协方差略低于SNP集3。所有SNP集之间,MY、Fat和AFC的遗传力以及加性遗传、环境和表型相关性相似。SNP集2和3的基因组多基因估计育种值之间的Spearman秩相关对于所有性状都很高(0.9990±0.0003)。
利用来自推算的非基因分型动物的表型和系谱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。