The exhaustive genomic scan approach, with an application to rare-variant association analysis.

Region-based genome-wide scans are usually performed by use of a priori chosen analysis regions. Such an approach will likely miss the region comprising the strongest signal and, thus, may result in increased type II error rates and decreased power. Here, we propose a genomic exhaustive scan approach that analyzes all possible subsequences and does not rely on a prior definition of the analysis regions. As a prime instance, we present a computationally ultraefficient implementation using the rare-variant collapsing test for phenotypic association, the genomic exhaustive collapsing scan (GECS). Our implementation allows for the identification of regions comprising the strongest signals in large, genome-wide rare-variant association studies while controlling the family-wise error rate via permutation. Application of GECS to two genomic data sets revealed several novel significantly associated regions for age-related macular degeneration and for schizophrenia. Our approach also offers a high potential to improve genome-wide scans for selection, methylation, and other analyses.

Pathway-induced allelic spectra of diseases in the presence of strong genetic effects.

Complex diseases are frequently modeled as following an additive model that excludes both intra- and inter-locus interaction, while at the same time reports on non-additive biological structures are ample, prominently featuring numerous metabolic and signaling pathways. Using extensive forward population simulations, we explored the impact of three basic pathway motifs on the relationship between epidemiological parameters, including disease prevalence, relative risk, sibling recurrence risk as well as causal variant number and allele frequency. We found that some but not all pathway motifs can shift the relationships between these parameters in comparison to the classical additive liability threshold model. The strongest deviations were observed with linear, cascade-like motifs that form an integral part of many reported pathways. We also modeled maturity-onset diabetes of the young (MODY) as a combination of different basic pathway motifs and observed a good concordance in epidemiological parameter values between our simulated data under this model and those reported in the literature. Given the widespread nature of pathways, including those in the etiology of human diseases, our results re-emphasize the need for non-additive interaction modeling of genetic variants to become an additional standard approach in analyzing human genetic data.

Securing the use of existing sample collections for future human genetic research.

Hundreds of thousands of individuals have been genotyped in the past decades using genotyping arrays, representing both a valuable data resource for future biomedical research and a substantial investment in human genetic research. However, novel chip designs and their altered sets of single-nucleotide polymorphisms (SNPs) pose the question of how well established data resources, such as large samples of healthy controls genotyped on legacy arrays, can be combined with newer samples genotyped on those novel arrays using genotype imputation. We exemplarily investigated this question based on genotype data of 30 European and 30 African unrelated samples from the 1000 Genomes project and on markers present on two legacy SNP arrays, namely Affymetrix's Human SNP 6.0 and Illumina's 550k array, and three newer arrays, namely two Axiom arrays from Affymetrix and an OmniExpress array from Illumina. We cross-compared the imputation accuracy as well as efficacy and assessed genotype concordance among these arrays. Although the accuracy of genotype prediction was uniformly high across all arrays, the imputation efficacy, that is, the proportion of successfully imputed markers, differed considerably between array combinations in both sample sets, with legacy arrays showing a trend towards lower efficacy values compared with newer arrays when serving as imputation basis. We conclude that, given the substantial losses of markers covered by the legacy arrays, the re-genotyping of existing samples sets, in particular those of healthy population controls, would be a worthwhile endeavor to secure their continued use in the future.