Genome-wide association studies (GWASs) are a popular tool for detecting association between genetic variants or single nucleotide polymorphisms (SNPs) and complex traits. Family data introduce complexity due to the non-independence of the family members. Methods for non-independent data are well established, but when the GWAS contains distinct family types, explicit modeling of between-family-type differences in the dependence structure comes at the cost of significantly increased computational burden. The situation is exacerbated with binary traits. In this paper, we perform several simulation studies to compare multiple candidate methods to perform single SNP association analysis with binary traits. We consider generalized estimating equations (GEE), generalized linear mixed models (GLMMs), or generalized least square (GLS) approaches. We study the influence of different working correlation structures for GEE on the GWAS findings and also the performance of different analysis method(s) to conduct a GWAS with binary trait data in families. We discuss the merits of each approach with attention to their applicability in a GWAS. We also compare the performances of the methods on the alcoholism data from the Minnesota Center for Twin and Family Research (MCTFR) study.

全基因组关联研究 (GWAS) 是一种流行的工具，用于检测遗传变异或单核苷酸多态性 (SNP) 与复杂性状之间的关联。由于家庭成员的非独立性，家庭数据引入了复杂性。非独立数据的方法已经很好地建立，但是当 GWAS 包含不同的家庭类型时，依赖结构中家庭类型之间差异的显式建模是以显着增加计算负担为代价的。这种情况因二元特征而恶化。在本文中，我们进行了多项模拟研究，以比较多种候选方法，以执行具有二元性状的单一 SNP 关联分析。我们考虑广义估计方程 (GEE)、广义线性混合模型 (GLMM) 或广义最小二乘 (GLS) 方法。我们研究了 GEE 的不同工作相关结构对 GWAS 结果的影响，以及不同分析方法的性能，以使用家庭中的二元性状数据进行 GWAS。我们讨论了每种方法的优点，并关注它们在 GWAS 中的适用性。我们还比较了明尼苏达双胞胎和家庭研究中心 (MCTFR) 研究中酒精中毒数据的方法的性能。