Weighted likelihood inference of genomic autozygosity patterns in dense genotype data.
Weighted likelihood inference of genomic autozygosity patterns in dense genotype data.
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DOI：
10.1186/s12864-017-4312-3
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发表时间：
2017-12-01
期刊：
影响因子：
4.4
通讯作者：
Pemberton TJ
中科院分区：
文献类型：
--
作者：
Blant A;Kwong M;Szpiech ZA;Pemberton TJ
当个体从双亲共有的祖先通过血统继承相同的单倍型为纯合时，出现基因组自体接合区域（罗阿）。在过去的十年中，它们对于理解进化史以及复杂疾病和特征的遗传基础变得越来越重要。然而，在密集基因型数据中推断罗阿的方法并没有随着基因组技术的进步而发展，基因组技术现在使我们能够快速创建大的高分辨率基因型数据集，限制了我们研究其组成罗阿模式的能力。我们报告了一种加权似然法推断罗阿在密集的基因型数据，占基因分型的位置之间的自相关性和未观察到的突变和重组事件的可能性，以及在全基因组序列（WGS）数据中的个体基因型调用的置信度的变化。在两种人口统计学情景下的前向时间遗传模拟反映了近亲繁殖及其对适应性的影响是感兴趣的情况，表明这种方法比现有的最先进的方法更有效地推断罗阿，其标记密度与WGS和人类和非人类研究中使用的流行微阵列基因分型平台一致。此外，我们提出的证据表明，这种方法是能够区分罗阿产生通过近亲结婚产生的罗阿。使用1000个基因组计划第3期数据的子集，我们表明，相对于WGS，中间和长罗阿被捕获与流行的微阵列平台鲁棒，而短罗阿的检测是更可变的，并提高标记密度。从WGS数据推断的全球罗阿模式与先前基于微阵列基因型数据报道的模式雅阁。最后，我们强调了这种方法的潜力，以检测基因组区域富集的一组相对于另一组的基础上比较每一个人的同源性的可能性，而不是推断的罗阿频率的同源性信号。这种加权似然罗阿推理方法可以帮助人口和疾病遗传学家与各种各样的数据类型和物种，探索罗阿模式，并确定基因组区域与不同的罗阿信号组之间，从而推进我们的进化历史和隐性变异的表型变异和疾病的作用的理解。本文的在线版本（doi：10.1186/s12864-017-4312-3）包含补充材料，可供授权用户使用。
Genomic regions of autozygosity (ROA) arise when an individual is homozygous for haplotypes inherited identical-by-descent from ancestors shared by both parents. Over the past decade, they have gained importance for understanding evolutionary history and the genetic basis of complex diseases and traits. However, methods to infer ROA in dense genotype data have not evolved in step with advances in genome technology that now enable us to rapidly create large high-resolution genotype datasets, limiting our ability to investigate their constituent ROA patterns. We report a weighted likelihood approach for inferring ROA in dense genotype data that accounts for autocorrelation among genotyped positions and the possibilities of unobserved mutation and recombination events, and variability in the confidence of individual genotype calls in whole genome sequence (WGS) data. Forward-time genetic simulations under two demographic scenarios that reflect situations where inbreeding and its effect on fitness are of interest suggest this approach is better powered than existing state-of-the-art methods to infer ROA at marker densities consistent with WGS and popular microarray genotyping platforms used in human and non-human studies. Moreover, we present evidence that suggests this approach is able to distinguish ROA arising via consanguinity from ROA arising via endogamy. Using subsets of The 1000 Genomes Project Phase 3 data we show that, relative to WGS, intermediate and long ROA are captured robustly with popular microarray platforms, while detection of short ROA is more variable and improves with marker density. Worldwide ROA patterns inferred from WGS data are found to accord well with those previously reported on the basis of microarray genotype data. Finally, we highlight the potential of this approach to detect genomic regions enriched for autozygosity signals in one group relative to another based upon comparisons of per-individual autozygosity likelihoods instead of inferred ROA frequencies. This weighted likelihood ROA inference approach can assist population- and disease-geneticists working with a wide variety of data types and species to explore ROA patterns and to identify genomic regions with differential ROA signals among groups, thereby advancing our understanding of evolutionary history and the role of recessive variation in phenotypic variation and disease. The online version of this article (doi:10.1186/s12864-017-4312-3) contains supplementary material, which is available to authorized users.
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