RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.
RAFFI: Accurate and fast familial relationship inference in large scale biobank studies using RaPID.
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DOI：
10.1371/journal.pgen.1009315
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发表时间：
2021-01
期刊：
影响因子：
4.5
通讯作者：
Zhi D
中科院分区：
文献类型：
--
作者：
Naseri A;Shi J;Lin X;Zhang S;Zhi D
从队列的全基因组遗传数据中推断关系是全基因组关联研究的重要先决条件。通常，通过计算所有个体对之间的亲属关系系数 (phi) 和 IBD 共享为零的全基因组概率 (π0) 来推断关系。当前的领先方法基于成对比较，可能无法扩展到非常大的队列（例如，样本量> 100万）。在这里，我们提出了一种有效的关系推理方法 RAFFI。 RAFFI 利用高效的 RaPID 方法首先调用 IBD 片段，然后根据检测到的 IBD 片段估计 phi 和 π0。这一推论是通过数据驱动的方法实现的，该方法根据定相质量和基因分型质量调整估计。通过模拟，我们表明 RAFFI 对定相/基因分型错误、混合事件和不同的标记密度具有鲁棒性，并且与当前领先的方法 KING 相比，具有更高的准确性，尤其是对于较远亲而言。当应用于约 50 万个体的英国生物银行分阶段数据时，RAFFI 比 KING 快大约 18 倍。我们预计 RAFFI 将为更大的群体提供快速、准确的相关性推断。推断家庭关系具有广泛的应用。基于家族的全基因组关联研究和基于人群的 GWAS 都需要遗传关系。推断关系对于未知的家庭结构至关重要，并且可用于纠正由于虚假父子关系、样本转换或未登记收养而导致的谱系信息。当前推断关系的方法对于包含数百万个人的大型群体来说是不可扩展的。在这里，我们提出了一种快速灵活的方法，称为 RAFFI，使用相同的下降 (IBD) 段。 IBD 片段是从共同祖先继承的不间断 DNA 片段。通常通过计算所有成对个体之间的亲属关系系数和零 IBD 共享的全基因组概率来推断关系。第一步，我们使用 RaPID 搜索 IBD 片段，这避免了单倍型面板中所有个体的成对比较。在第二步中，我们计算亲属关系系数以推断关系。为了使我们的方法对基因分型和定相误差具有鲁棒性，调整了不同相关程度的亲属关系系数的阈值。因此，由于定相错误或基因分型错误率的错误指定而导致的 IBD 片段的较低检测能力将不包括关系的推断。
Inference of relationships from whole-genome genetic data of a cohort is a crucial prerequisite for genome-wide association studies. Typically, relationships are inferred by computing the kinship coefficients (ϕ) and the genome-wide probability of zero IBD sharing (π0) among all pairs of individuals. Current leading methods are based on pairwise comparisons, which may not scale up to very large cohorts (e.g., sample size >1 million). Here, we propose an efficient relationship inference method, RAFFI. RAFFI leverages the efficient RaPID method to call IBD segments first, then estimate the ϕ and π0 from detected IBD segments. This inference is achieved by a data-driven approach that adjusts the estimation based on phasing quality and genotyping quality. Using simulations, we showed that RAFFI is robust against phasing/genotyping errors, admix events, and varying marker densities, and achieves higher accuracy compared to KING, the current leading method, especially for more distant relatives. When applied to the phased UK Biobank data with ~500K individuals, RAFFI is approximately 18 times faster than KING. We expect RAFFI will offer fast and accurate relatedness inference for even larger cohorts. Inferring familial relationships has a wide range of applications. Family-based genome-wide association studies and population-based GWAS both require genetic relationships. Inferring relationship is essential for unknown familial structures and can be used to correct pedigree information due to false paternity, sample switches, or unregistered adoption. Current approaches for inferring relationships are not scalable for large cohorts comprising millions of individuals. Here, we present a fast and flexible method, called RAFFI, using Identical by Descent (IBD) segments. IBD segments are uninterrupted DNA segments inherited from a common ancestor. Relationships are usually inferred by computing the kinship coefficients and the genome-wide probability of zero IBD sharing among all pairs of individuals. In the first step, we search for IBD segments using RaPID which avoids a pairwise comparison of all individuals in a haplotype panel. In the second step, we compute the kinship coefficients to infer the relationships. To make our method robust against genotyping and phasing error, the thresholds of kinship coefficients for different degrees of relatedness are adjusted. As a result, the lower detection power of IBD segments due to phasing errors or misspecification of the genotyping error rate will not comprise the inference of relationships.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
30.8
作者：
Loh, Po-Ru;Danecek, Petr;Palamara, Pier Francesco;Fuchsberger, Christian;Reshef, Yakir A.;Finucane, Hilary K.;Schoenherr, Sebastian;Forer, Lukas;McCarthy, Shane;Abecasis, Goncalo R.;Durbin, Richard;Price, Alkes L.
通讯作者：
Price, Alkes L.
影响因子：
9.8
作者：
Mooney, Jazlyn A.;Huber, Christian D.;Lohmueller, Kirk E.
通讯作者：
Lohmueller, Kirk E.
影响因子：
7
作者：
Gusev, Alexander;Lowe, Jennifer K.;Pe'er, Itsik
通讯作者：
Pe'er, Itsik