Adjusting heterogeneous ascertainment bias for genetic association analysis with extended families

Abstract

Background: In family-based association analysis, each family is typically ascertained from a single proband, which renders the effects of ascertainment bias heterogeneous among family members. This is contrary to case-control studies, and may introduce sample or ascertainment bias. Statistical efficiency is affected by ascertainment bias, and careful adjustment can lead to substantial improvements in statistical power. However, genetic association analysis has often been conducted using family-based designs, without addressing the fact that each proband in a family has had a great influence on the probability for each family member to be affected.

Method: We propose a powerful and efficient statistic for genetic association analysis that considered the heterogeneity of ascertainment bias among family members, under the assumption that both prevalence and heritability of disease are available. With extensive simulation studies, we showed that the proposed method performed better than the existing methods, particularly for diseases with large heritability.

Results: We applied the proposed method to the genome-wide association analysis of Alzheimer's disease. Four significant associations with the proposed method were found.

Conclusion: Our significant findings illustrated the practical importance of this new analysis method.

Fig. 1

Two sample pedigree structures from AD data; (a) individual 9 of family 1 was selected as a "proband" and individual 3–8 of family 1 were selcted as "non-probands" (b) individual 3 of family 2 was selected as a "proband" and individual 4–6 of family 2 were selcted as "non-probands"

Fig. 2

Family tree. There are two different types of family structures, including (a) nuclear family and (b) extended family, which were considered in our simulation study

Fig. 3

QQ plots for FQLS ₁ and FQLS ₂ under the null hypothesis. QQplots for FQLS ₁ and FQLS ₂ are obtaind when h2 is 0.2((a), (b)), 0.5((c), (d)), or 0.8((e), (f)). P-values were calculated based on 5000 replicates when the number of families was 900. The genetic effect β was assumed to be 0, and the minor allele frequency was 0.2

Fig. 4

Multidimensional scaling plots from samples for the GWAS for AD. Founders were selectively used, and multidimensional scaling plots were obtained with the first and second PC scores

Fig. 5

QQ plots of results from GWAS for AD. QQ plots are provided with the results from (a) WL and (b) FQLS ₂