Family-based association tests for ordinal traits adjusting for covariates

We present a class of family-based 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 1 error and power of our proposed test with existing tests. The empirical result suggests that our test produces reasonable type 1 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 (P-values ≤0.001) 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.