A Pragmatic Test for Detecting Association between a Dichotomous Trait and the Genotypes of Affected Families, Controls and Independent Cases

Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

Haplotype-based association methods have been developed to understand the genetic architecture of complex diseases. Compared to single-variant-based methods, haplotype methods are thought to be more biologically relevant, since there are typically multiple non-independent genetic variants involved in complex diseases, and the use of haplotypes implicitly accounts for non-independence caused by linkage disequilibrium. In recent years, with the focus moving from common to rare variants, haplotype-based methods have also evolved accordingly to uncover the roles of rare haplotypes. One particular approach is regularization-based, with the use of Bayesian least absolute shrinkage and selection operator (Lasso) as an example. This type of methods has been developed for either case-control population data (the logistic Bayesian Lasso (LBL)) or family data (family-triad-based logistic Bayesian Lasso (famLBL)). In some situations, both family data and case-control data are available; therefore, it would be a waste of resources if only one of them could be analyzed. To make full usage of available data to increase power, we propose a unified approach that can combine both case-control and family data (combined logistic Bayesian Lasso (cLBL)). Through simulations, we characterized the performance of cLBL and showed the advantage of cLBL over existing methods. We further applied cLBL to the Framingham Heart Study data to demonstrate its utility in real data applications.

Replication, reanalysis, and gene expression: ME2 and genetic generalized epilepsy

OBJECTIVE

Genetic generalized epilepsy (GGE) consists of epileptic syndromes with overlapping symptoms and is considered to be largely genetic. Previous cosegregation and association studies have pointed to malic enzyme 2 (ME2) as a candidate susceptibility gene for adolescent-onset GGE. In this article, we present new evidence supporting ME2's involvement in GGE.

METHODS

To definitively test ME2's influence on GGE, we used 3 different approaches. First, we compared a newly recruited GGE cohort with an ethnically matched reference sample from 1000 Genomes Project, using an efficient test of association (POPFAM+). Second, we used POPFAM+ to reanalyze a previously collected data set, wherein the original controls were replaced with ethnically matched reference samples to minimize the confounding effect of population stratification. Third, in a post hoc analysis of expression data from healthy human prefrontal cortex, we identified single nucleotide polymorphisms (SNPs) influencing ME2 messenger RNA (mRNA) expression; and then we tested those same SNPs for association with GGE in a large case-control cohort.

RESULTS

First, in the analysis of our newly recruited GGE Cohort, we found a strong association between an ME2 SNP and GGE (P = 0.0006 at rs608781). Second, in the reanalysis of previously collected data, we confirmed the Greenberg et al (2005) finding of a GGE-associated ME2 risk haplotype. Third, in the post hoc ME2 expression analysis, we found evidence for a possible link between GGE and ME2 gene expression in human brain.

SIGNIFICANCE

Overall, our research, and the research of others, provides compelling evidence that ME2 influences susceptibility to adolescent-onset GGE.