The promise and deceit of genomic selection component analyses.
The promise and deceit of genomic selection component analyses.
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基因组选择成分分析的希望和欺骗。
DOI：
10.1098/rspb.2021.1812
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Kelly,JohnK
中科院分区：
文献类型：
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作者：
Kelly,JohnK
选择成分分析（SCA）将个体基因型与适合度成分（如生存力、繁殖力和交配成功率）联系起来。SCA是基于群体遗传模型和产量选择估计直接预测等位基因频率的变化。本文探讨了gSCA的统计特性：将SCA应用于田间采样个体的SNP全基因组评分的实验。计算机模拟表明，涉及几千个基因型样本的gSCA可以检测到等位基因频率的变化的幅度，已经记录在不同类群的田间实验。为了检测选择，来自个体的大样本的低水平测序的不精确基因分型提供比较小样本的精确基因分型大得多的功效。模拟还证明了“单倍型匹配”的有效性，这是一种将来自有限的全基因组序列集合（参考图）的联合收割机信息与测量适合度的更大的田间个体样本相结合的方法。合并测序被证明是增加统计功效的另一种方法。最后，我讨论了选择估计的解释与Beavis效应，高估选择强度在显着的位点。
Selection component analyses (SCA) relate individual genotype to fitness components such as viability, fecundity and mating success. SCA are based on population genetic models and yield selection estimates directly in terms of predicted allele frequency change. This paper explores the statistical properties of gSCA: experiments that apply SCA to genome-wide scoring of SNPs in field sampled individuals. Computer simulations indicate that gSCA involving a few thousand genotyped samples can detect allele frequency changes of the magnitude that has been documented in field experiments on diverse taxa. To detect selection, imprecise genotyping from low-level sequencing of large samples of individuals provides much greater power than precise genotyping of smaller samples. The simulations also demonstrate the efficacy of ‘haplotype matching’, a method to combine information from a limited collection of whole genome sequence (the reference panel) with the much larger sample of field individuals that are measured for fitness. Pooled sequencing is demonstrated as another way to increase statistical power. Finally, I discuss the interpretation of selection estimates in relation to the Beavis effect, the overestimation of selection intensities at significant loci.