On an extended interpretation of linkage disequilibrium in genetic case-control association studies.

We are concerned with statistical inference for 2 × C × K contingency tables in the context of genetic case-control association studies. Multivariate methods based on asymptotic Gaussianity of vectors of test statistics require information about the asymptotic correlation structure among these test statistics under the global null hypothesis. In the case of C=2, we show that for a wide variety of test statistics this asymptotic correlation structure is given by the standardized linkage disequilibrium matrix of the K loci under investigation. Three popular choices of test statistics are discussed for illustration. In the case of C=3, the standardized composite linkage disequilibrium matrix is the limiting correlation matrix of the K locus-specific Cochran-Armitage trend test statistics.

Estimating exome genotyping accuracy by comparing to data from large scale sequencing projects.

With exome sequencing becoming a tool for mutation detection in routine diagnostics there is an increasing need for platform-independent methods of quality control. We present a genotype-weighted metric that allows comparison of all the variant calls of an exome to a high-quality reference dataset of an ethnically matched population. The exome-wide genotyping accuracy is estimated from the distance to this reference set, and does not require any further knowledge about data generation or the bioinformatics involved. The distances of our metric are visualized by non-metric multidimensional scaling and serve as an intuitive, standardizable score for the quality assessment of exome data.

The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process.

With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.