A comparison of strategies for analyzing dichotomous outcomes in genome-wide association studies with general pedigrees.
A comparison of strategies for analyzing dichotomous outcomes in genome-wide association studies with general pedigrees.
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DOI：
10.1002/gepi.20614
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发表时间：
2011-11
影响因子：
2.1
通讯作者：
Yang, Qiong
中科院分区：
文献类型：
--
作者：
Chen, Ming-Huei;Liu, Xuan;Wei, Fengrong;Larson, Martin G.;Fox, Caroline S.;Vasan, Ramachandran S.;Yang, Qiong
全基因组关联研究（GWAS）经常在具有相关个体的一般或孤立人群中进行。然而，对于哪种策略最适合于分析一般家系中的二分表型缺乏共识。使用模拟研究，我们比较了几种策略，包括广义估计方程（GEE）策略与各种工作相关结构，广义线性混合模型（GLMM）和方差分量策略（表示为LMEBIN），将二分结果视为连续的，特别注意其性能与罕见变异，罕见疾病和小样本量。在我们的模拟中，当样本量不小时，对于I型错误，只有GEE和LMEBIN在大多数情况下保持名义I型错误，但具有非常罕见疾病和遗传变异的GEE除外。GEE和LMEBIN具有相似的统计功效，并且在患病率较低时略优于GLMM。在计算效率方面，三明治方差估计的GEE优于GLMM和LMEBIN。我们将这些策略应用于Frachial Heart研究中痛风的GWAS。根据我们的结果，我们建议在GWAS中使用GEE ind-san用于常见变异，GEE ind-fij或LMEBIN用于GWAS中具有一般家系的二分结局的罕见变异。
Genome-wide association studies (GWAS) have been frequently conducted on general or isolated populations with related individuals. However, there is a lack of consensus on which strategy is most appropriate for analyzing dichotomous phenotypes in general pedigrees. Using simulation studies, we compared several strategies including generalized estimating equations (GEE) strategies with various working correlation structures, generalized linear mixed model (GLMM) and a variance component strategy (denoted LMEBIN) that treats dichotomous outcomes as continuous with special attentions to their performance with rare variants, rare diseases and small sample sizes. In our simulations, when the sample size is not small, for type I error, only GEE and LMEBIN maintain nominal type I error in most cases with exceptions for GEE with very rare disease and genetic variants. GEE and LMEBIN have similar statistical power and slightly outperform GLMM when the prevalence is low. In terms of computational efficiency, GEE with sandwich variance estimator outperforms GLMM and LMEBIN. We apply the strategies to GWAS of gout in the Framingham Heart Study. Based on our results, we would recommend using GEE ind-san in the GWAS for common variants and GEE ind-fij or LMEBIN for rare variants for GWAS of dichotomous outcomes with general pedigrees.
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DOI：
10.1161/circgenetics.109.934455
发表时间：
2010-12
期刊：
Circulation. Cardiovascular genetics
影响因子：
--
作者：
Yang Q;Köttgen A;Dehghan A;Smith AV;Glazer NL;Chen MH;Chasman DI;Aspelund T;Eiriksdottir G;Harris TB;Launer L;Nalls M;Hernandez D;Arking DE;Boerwinkle E;Grove ML;Li M;Linda Kao WH;Chonchol M;Haritunians T;Li G;Lumley T;Psaty BM;Shlipak M;Hwang SJ;Larson MG;O'Donnell CJ;Upadhyay A;van Duijn CM;Hofman A;Rivadeneira F;Stricker B;Uitterlinden AG;Paré G;Parker AN;Ridker PM;Siscovick DS;Gudnason V;Witteman JC;Fox CS;Coresh J
通讯作者：
Coresh J
影响因子：
2.1
作者：
Browning, SR;Briley, JD;Wagner, MJ
通讯作者：
Wagner, MJ
影响因子：
1.6
作者：
Bates, DM;DebRoy, S
通讯作者：
DebRoy, S
DOI：
10.1080/03610918808812718
发表时间：
1988-01-01
影响因子：
0.9
作者：
PAIK, MC
通讯作者：
PAIK, MC
DOI：
10.1198/016214501753382309
发表时间：
2001-12-01
影响因子：
3.7
作者：
Kauermann, G;Carroll, RJ
通讯作者：
Carroll, RJ