Functional analysis of variance for association studies.
Functional analysis of variance for association studies.
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DOI：
10.1371/journal.pone.0105074
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Lu Q
中科院分区：
文献类型：
--
作者：
Vsevolozhskaya OA;Zaykin DV;Greenwood MC;Wei C;Lu Q
While progress has been made in identifying common genetic variants associated with human diseases, for most of common complex diseases, the identified genetic variants only account for a small proportion of heritability. Challenges remain in finding additional unknown genetic variants predisposing to complex diseases. With the advance in next-generation sequencing technologies, sequencing studies have become commonplace in genetic research. The ongoing exome-sequencing and whole-genome-sequencing studies generate a massive amount of sequencing variants and allow researchers to comprehensively investigate their role in human diseases. The discovery of new disease-associated variants can be enhanced by utilizing powerful and computationally efficient statistical methods. In this paper, we propose a functional analysis of variance (FANOVA) method for testing an association of sequence variants in a genomic region with a qualitative trait. The FANOVA has a number of advantages: (1) it tests for a joint effect of gene variants, including both common and rare; (2) it fully utilizes linkage disequilibrium and genetic position information; and (3) allows for either protective or risk-increasing causal variants. Through simulations, we show that FANOVA outperform two popularly used methods – SKAT and a previously proposed method based on functional linear models (FLM), – especially if a sample size of a study is small and/or sequence variants have low to moderate effects. We conduct an empirical study by applying three methods (FANOVA, SKAT and FLM) to sequencing data from Dallas Heart Study. While SKAT and FLM respectively detected ANGPTL 4 and ANGPTL 3 associated with obesity, FANOVA was able to identify both genes associated with obesity.
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影响因子：
2.1
作者：
Fan, Ruzong;Wang, Yifan;Mills, James L.;Wilson, Alexander F.;Bailey-Wilson, Joan E.;Xiong, Momiao
通讯作者：
Xiong, Momiao
影响因子：
5.8
作者：
Legry, Vanessa;Bokor, Szilvia;Meirhaeghe, Aline
通讯作者：
Meirhaeghe, Aline
影响因子：
3.7
作者：
Boos, DD;Zhang, J
通讯作者：
Zhang, J
与人类血液低密度脂蛋白胆固醇、高密度脂蛋白胆固醇或甘油三酯相关的六个新位点。
影响因子：
30.8
作者：
Kathiresan, Sekar;Melander, Olle;Guiducci, Candace;Surti, Aarti;Burtt, Noel P.;Rieder, Mark J.;Cooper, Gregory M.;Roos, Charlotta;Voight, Benjamin F.;Havulinna, Aki S.;Wahlstrand, Bjorn;Hedner, Thomas;Corella, Dolores;Tai, E. Shyong;Ordovas, Jose M.;Berglund, Goran;Vartiainen, Erkki;Jousilahti, Pekka;Hedblad, Bo;Taskinen, Marja-Riitta;Newton-Cheh, Christopher;Salomaa, Veikko;Peltonen, Leena;Groop, Leif;Altshuler, David M.;Orho-Melander, Marju
通讯作者：
Orho-Melander, Marju
影响因子：
1.8
作者：
Cuevas, A;Febrero, M;Fraiman, R
通讯作者：
Fraiman, R