Department of Animal Science, University of Nebraska, Lincoln, NE 68583, USA.
USDA, ARS, Roman L. Hruska U.S. Meat Animal Research Center, Clay Center, NE 68933, USA.
J Anim Sci. 2023 Jan 3;101. doi: 10.1093/jas/skad274.
A beef cattle population (n = 2,343) was used to assess the impact of variants identified from the imputed low-pass sequence (LPS) on the estimation of variance components and genetic parameters of birth weight (BWT) and post-weaning gain (PWG). Variants were selected based on functional impact and were partitioned into four groups (low, modifier, moderate, high) based on predicted functional impact and re-partitioned based on the consequence of mutation, such as missense and untranslated region variants, into six groups (G1-G6). Each subset was used to construct a genomic relationship matrix (GRM) for univariate animal models. Multiple analyses were conducted to compare the proportion of additive genetic variation explained by the different subsets individually and collectively, and these estimates were benchmarked against all LPS variants in a single GRM and array (e.g., GeneSeek Genomic Profiler 100K) genotypes. When all variants were included in a single GRM, heritability estimates for BWT and PWG were 0.43 ± 0.05 and 0.38 ± 0.05, respectively. Heritability estimates for BWT ranged from 0.10 to 0.42 dependent on which variant subsets were included. Similarly, estimates for PWG ranged from 0.05 to 0.38. Results showed that variants in the subsets modifier and G1 (untranslated region) yielded the highest heritability estimates and were similar to the inclusion of all variants, while estimates from GRM containing only variants in the categories High, G4 (non-coding transcript exon), and G6 (start and stop loss/gain) were the lowest. All variants combined provided similar heritability estimates to chip genotypes and provided minimal to no additional information when combined with chip data. This suggests that the chip single nucleotide polymorphisms and the variants from LPS predicted to be less consequential are in relatively high linkage disequilibrium with the underlying causal variants as a whole and sufficiently spread throughout the genome to capture larger proportions of additive genetic variation.
使用一个肉牛群体（n=2343）评估了从低覆盖度序列（LPS）推断的变异对出生重（BWT）和断奶后增重（PWG）的方差组分和遗传参数估计的影响。根据功能影响选择变异，并根据预测的功能影响将其分为四个组（低、修饰、中度、高），并根据突变的后果（如错义突变和非翻译区变异）重新分为六个组（G1-G6）。每个子集都用于构建单变量动物模型的基因组关系矩阵（GRM）。进行了多次分析以比较不同子集单独和集体解释的加性遗传变异比例，并将这些估计值与单个 GRM 和阵列（例如，GeneSeek Genomic Profiler 100K）基因型中的所有 LPS 变异进行基准测试。当所有变异都包含在单个 GRM 中时，BWT 和 PWG 的遗传力估计值分别为 0.43±0.05 和 0.38±0.05。BWT 的遗传力估计值范围从 0.10 到 0.42，具体取决于所包含的变异子集。同样，PWG 的估计值范围从 0.05 到 0.38。结果表明，修饰和 G1（非翻译区）亚组中的变异产生了最高的遗传力估计值，与包含所有变异相似，而仅包含高、G4（非编码转录外显子）和 G6（起始和停止丢失/增益）类别的变异的 GRM 产生的估计值最低。所有变异的组合提供了与芯片基因型相似的遗传力估计值，并与芯片数据结合使用时提供了最小到没有额外信息。这表明芯片上的单核苷酸多态性和 LPS 中预测为不太重要的变异与整个因果变异处于相对高的连锁不平衡状态，并且在整个基因组中分布足够广泛，以捕获更大比例的加性遗传变异。
