Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test

IF 3.6 1区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE Genetics Selection Evolution Pub Date : 2024-02-06 DOI:10.1186/s12711-024-00879-6
Jigme Dorji, Antonio Reverter, Pamela A. Alexandre, Amanda J. Chamberlain, Christy J. Vander-Jagt, James Kijas, Laercio R. Porto-Neto
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Abstract

The study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test. Our study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate. Considering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.
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利用基于群体的似然比检验为 7000 万个牛变体确定祖先等位基因
通过对祖先等位基因的研究,可以深入了解一个种群的进化历史、选择和遗传结构。在牛的遗传分析中,祖先等位基因被广泛应用,包括检测选择特征、确定品种祖先和识别混血。拥有一份全面的祖先等位基因列表有望提高这些遗传分析的准确性。然而,牛的祖先等位基因列表,尤其是在全基因组序列水平上,还远远不够完整。事实上,目前最大的祖先等位基因列表(约 4200 万)只占牛检测到的变异总数的不到 28%。为了解决这个问题并开发用于进化研究的基因组资源,我们利用基于种群的似然比检验,通过比较先验推导的全基因组序列变异与外种群,确定了牛的祖先等位基因。我们的研究确定并提供了迄今为止最大的牛祖先等位基因列表(7010 万个),其中包括 X 染色体上的 230 万个等位基因。在只考虑高概率祖先等位基因(2980 万个位置)和另外 2350 万个高置信度祖先等位基因的情况下,确定的祖先等位基因与之前研究的结果高度一致(97.6%),扩大了可用的参考列表,提高了涉及祖先等位基因的遗传分析的准确性。研究结果与之前的研究高度一致,这意味着我们使用基因组序列变异和似然比检验来确定祖先等位基因的方法是合适的。考虑到不同研究中祖先等位基因的高度一致性,本研究中确定的祖先等位基因,包括以前未列出的等位基因,尤其是那些估计概率较高的等位基因,可用于进一步的遗传分析,且准确性较高。我们利用物种中的预定变异和似然比检验来确定祖先等位基因的方法适用于可获得序列水平基因型的其他物种。
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来源期刊
Genetics Selection Evolution
Genetics Selection Evolution 生物-奶制品与动物科学
CiteScore
6.50
自引率
9.80%
发文量
74
审稿时长
1 months
期刊介绍: Genetics Selection Evolution invites basic, applied and methodological content that will aid the current understanding and the utilization of genetic variability in domestic animal species. Although the focus is on domestic animal species, research on other species is invited if it contributes to the understanding of the use of genetic variability in domestic animals. Genetics Selection Evolution publishes results from all levels of study, from the gene to the quantitative trait, from the individual to the population, the breed or the species. Contributions concerning both the biological approach, from molecular genetics to quantitative genetics, as well as the mathematical approach, from population genetics to statistics, are welcome. Specific areas of interest include but are not limited to: gene and QTL identification, mapping and characterization, analysis of new phenotypes, high-throughput SNP data analysis, functional genomics, cytogenetics, genetic diversity of populations and breeds, genetic evaluation, applied and experimental selection, genomic selection, selection efficiency, and statistical methodology for the genetic analysis of phenotypes with quantitative and mixed inheritance.
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