The era of genome-wide association studies (GWAS) has led to the discovery of numerous genetic variants associated with disease. Better understanding of whether these or other variants interact leading to differential risk compared with individual marker effects will increase our understanding of the genetic architecture of disease, which may be investigated using the family-based study design. We present M-TDT (the multi-locus transmission disequilibrium test), a tool for detecting family-based multi-locus multi-allelic effects for qualitative or quantitative traits, extended from the original transmission disequilibrium test (TDT). Tests to handle the comparison between additive and epistatic models, lack of independence between markers and multiple offspring are described. Performance of M-TDT is compared with a multifactor dimensionality reduction (MDR) approach designed for investigating families in the hypothesis-free genome-wide setting (the multifactor dimensionality reduction pedigree disequilibrium test, MDR-PDT). Other methods derived from the TDT or MDR to investigate genetic interaction in the family-based design are also discussed. The case of three independent biallelic loci is illustrated using simulations for one- to three-locus alternative hypotheses. M-TDT identified joint-locus effects and distinguished effectively between additive and epistatic models. We showed a practical example of M-TDT based on three genes already known to be implicated in malaria susceptibility. Our findings demonstrate the value of M-TDT in a hypothesis-driven context to test for multi-way epistasis underlying common disease etiology, whereas MDR-PDT-based methods are more appropriate in a hypothesis-free genome-wide setting.

SUMMARY: RNA sequencing data are becoming a major method of choice to study transcriptomes, including the mapping of gene expression quantitative trait loci (eQTLs). RNA sample contamination or swapping is a serious problem for downstream analysis and may result in false discovery and lose power to detect the true biological relationships. When genetic data are available, for example, in eQTL studies or samples have been previously genotyped or DNA sequenced, it is possible to combine genetic data and RNA-seq data to detect sample contamination and resolve sample swapping problems. In this article, we introduce a tool (IDCheck) that allows easy assessment of concordance between genotype (from SNP arrays or DNA sequencing) and gene expression (RNA-seq) samples. IDCheck compares the identity of RNA-seq reads and SNP genotypes using a likelihood-based method. Based on maximum likelihood estimates of relevant parameters, we can detect sample contamination and identify correct sample pairs when swapping occurs. Our tool provides an efficient and convenient way to evaluate and resolve these problems. AVAILABILITY: A complete description of the software is included on the application home page. The software is freely available in the public domain at http://eqtl.rc.fas.harvard.edu/idcheck/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.