Evaluation of imputation performance based on the SNP panel density and the reference population size in Korean native chicken(R).

IF 2.4 2区 农林科学 Q1 AGRICULTURE, DAIRY & ANIMAL SCIENCE
Minjun Kim, Hyo Jun Choo, Sunghyun Cho, Doo Ho Lee, Jun Heon Lee, Dongwon Seo
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引用次数: 0

Abstract

Objective: This study aimed to identify the optimal single nucleotide polymorphism (SNP) panel density for accurate imputation in the Korean native chicken (KNC) and Yeonsan Ogye (YO) populations. The primary focus was on evaluating how the reference population size and SNP density influence imputation performance and accuracy.

Methods: Data were collected from five purebred lines of KNC and the YO population, comprising a total of 256 KNC and 199 YO chickens. Imputed genotype ratio and accuracy were evaluated across various scenarios using SNP densities of 2.5K, 5K, 10K, and 50K in both populations. Additionally, for the YO dataset, reference population sizes of 50, 100, and 150 were analyzed to assess their impact on imputation outcomes.

Results: Higher SNP densities notably improved imputation performance. Specifically, when SNP panel density reached 10K or greater, the ratio of imputed SNPs exceeded 70% and the accuracy increased substantially, regardless of the reference population size. However, imputation efficiency decreased markedly when either the reference or test population size was around 50 individuals.

Conclusion: A test SNP density of at least 10K was determined to be essential for accurate genotype imputation. Additionally, imputation efficiency was observed to decline when either the reference or test population included around 50 individuals. These findings provide important data that can guide the genetic improvement of indigenous livestock populations.

基于韩国土鸡SNP面板密度和参考种群大小的归算性能评价(R)。
目的:本研究旨在确定韩国土鸡(KNC)和延山乌溪(YO)群体准确插补的最佳单核苷酸多态性(SNP)板密度。主要重点是评估参考种群大小和SNP密度如何影响代入性能和准确性。方法:收集5个KNC纯种系和YO群体的数据,共256只KNC鸡和199只YO鸡。在两个人群中使用2.5K、5K、10K和50K的SNP密度评估不同情况下的输入基因型比率和准确性。此外,对于YO数据集,分析了50、100和150的参考人口规模,以评估其对估算结果的影响。结果:较高的SNP密度显著提高了代入性能。具体而言,当SNP面板密度达到10K或更大时,无论参考种群大小如何,输入SNP的比例都超过70%,准确性大幅提高。然而,当参考种群或试验种群的数量在50左右时,归责效率显著降低。结论:至少10K的测试SNP密度被确定为准确的基因型插入所必需的。此外,当参考种群或测试种群包含约50只个体时,观察到归算效率下降。这些发现为指导本地牲畜种群的遗传改良提供了重要数据。
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来源期刊
Animal Bioscience
Animal Bioscience AGRICULTURE, DAIRY & ANIMAL SCIENCE-
CiteScore
5.00
自引率
0.00%
发文量
223
审稿时长
3 months
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