Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families.
Flexible and robust methods for rare-variant testing of quantitative traits in trios and nuclear families.
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DOI：
10.1002/gepi.21839
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发表时间：
2014-09
影响因子：
2.1
通讯作者：
Epstein, Michael P.
中科院分区：
文献类型：
--
作者：
Jiang, Yunxuan;Conneely, Karen N.;Epstein, Michael P.
大多数复杂性状的稀有变异关联检验仅适用于基于人群或病例对照的重测序研究。对于以家系为基础的重测序研究，很少有罕见变异关联检验，这是不幸的，因为家系具有许多吸引人的特征，用于此类分析。由于遗传负荷增加，基于家族的研究可能比基于人群的研究更强大，并进一步实现罕见变异关联检验，该检验在设计上对人群分层造成的混淆具有鲁棒性。考虑到这一点，我们提出了一个罕见的变异关联测试的数量性状的家庭;该测试集成了QDT方法的Abecasis等。到基于核的SNP关联测试KMFAM的Schifano等。由此产生的家族内测试享有罕见变异关联测试的核心框架的许多好处，包括快速评估p值和保存的权力时，一个地区窝藏罕见的因果变异，在不同的方向上的表型。此外，根据设计，该家族内检验对人群分层造成的混杂具有稳健性。虽然家族内关联测试通常不如使用所有遗传信息的同行强大，但我们表明，我们可以使用简单的筛选程序恢复大部分这种能力（同时仍然确保对人群分层的鲁棒性）。我们的方法可以容纳协变量，并允许缺失的亲本基因型数据，我们已经编写了软件，在R中实现该方法供公众使用。
Most rare-variant association tests for complex traits are applicable only to population-based or case-control resequencing studies. There are fewer rare-variant association tests for family-based resequencing studies, which is unfortunate since pedigrees possess many attractive characteristics for such analyses. Family-based studies can be more powerful than their population-based counterparts due to increased genetic load and further enable the implementation of rare-variant association tests that, by design, are robust to confounding due to population stratification. With this in mind, we propose a rare-variant association test for quantitative traits in families; this test integrates the QTDT approach of Abecasis et al. into the kernel-based SNP association test KMFAM of Schifano et al.. The resulting within-family test enjoys the many benefits of the kernel framework for rare-variant association testing, including rapid evaluation of p-values and preservation of power when a region harbors rare causal variation that acts in different directions on phenotype. Additionally, by design, this within-family test is robust to confounding due to population stratification. While within-family association tests are generally less powerful than their counterparts that use all genetic information, we show that we can recover much of this power (while still ensuring robustness to population stratification) using a straightforward screening procedure. Our method accommodates covariates and allows for missing parental genotype data, and we have written software implementing the approach in R for public use.
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影响因子：
3.5
作者：
Do R;Kathiresan S;Abecasis GR
通讯作者：
Abecasis GR
影响因子：
3.7
作者：
Cruchaga C;Haller G;Chakraverty S;Mayo K;Vallania FL;Mitra RD;Faber K;Williamson J;Bird T;Diaz-Arrastia R;Foroud TM;Boeve BF;Graff-Radford NR;St Jean P;Lawson M;Ehm MG;Mayeux R;Goate AM;NIA-LOAD/NCRAD Family Study Consortium
通讯作者：
NIA-LOAD/NCRAD Family Study Consortium
影响因子：
9.8
作者：
Norton, Nadine;Li, Duanxiang;Hershberger, Ray E.
通讯作者：
Hershberger, Ray E.
影响因子：
5.2
作者：
Ionita-Laza, Iuliana;Lee, Seunggeun;Lin, Xihong
通讯作者：
Lin, Xihong
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.