## Abstract Familyโbased study designs have an important role in the search for association between disease phenotypes and genetic markers. Unlike traditional caseโcontrol methods, familyโbased tests use withinโfamily data to avoid identification of spurious associations that may result from popula
Family-based association tests for ordinal traits adjusting for covariates
โ Scribed by Xueqin Wang; Yuanqing Ye; Heping Zhang
- Publisher
- John Wiley and Sons
- Year
- 2006
- Tongue
- English
- Weight
- 140 KB
- Volume
- 30
- Category
- Article
- ISSN
- 0741-0395
No coin nor oath required. For personal study only.
โฆ Synopsis
Abstract
We present a class of familyโbased association tests (FBATs) for ordinal traits that adjust for the effects of covariates. For complex diseases, especially mental health conditions including nicotine dependence and substance use, the outcome variables are often recorded in an ordinal rather than quantitative scale. The naturally recorded ordinal traits are commonly analyzed either as quantitative traits or are dichotomized. It has been demonstrated repeatedly in recent studies that these commonly used approaches to dealing with ordinal traits are inadequate and result in loss of power. In this report, we make use of conditional likelihood to derive score test statistics that belong to a general class of FBATs. We conducted simulation studies to compare the type I error and power of our proposed test with existing tests. The empirical result suggests that our test produces reasonable type I errors and has power far exceeding (often doubling) those of existing tests. We applied our proposed test to a data set on alcohol dependence and found that six single nucleotide polymorphisms (SNPs) are significantly associated (Pโvalues โค0.001) with alcohol dependence after adjusting for gender and age. Three of the SNPs (rs619, rs1972373, and rs1571423) or their tightly linked regions have been suggested in the literature from the analysis of the same data, demonstrating the consistent findings between various methods. The other three SNPs (rs485874, rs718251, and rs1869907) are identified for the first time using this data set, underscoring the potential power of our proposed test. Genet. Epidemiol. ยฉ 2006 WileyโLiss, Inc.
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