Abstract: Genome-wide association studies may allow for detection of variants that are statistically significantly associated with disease risk. However, inferring which are the genes underlying these variant associations may be difficult. The presently disclosed approaches utilize machine learning techniques to predict genes from genome-wide association study summary statistics that substantially improves causal gene identification in terms of both precision and recall compared to other techniques.

Abstract: Embodiments for calling variations in a sample polynucleotide sequence compared to a reference polynucleotide sequence are provided. Aspects of the embodiments include executing an application on at least one computer that locates local areas in the reference polynucleotide sequence where a likelihood exists that one or more bases of the sample polynucleotide sequence are changed from corresponding bases in the reference polynucleotide sequence, where the likelihood is determined at least in part based on mapped mated reads of the sample polynucleotide sequence; generating at least one sequence hypothesis for each of the local areas, and optimizing the at least one sequence hypothesis for at least a portion of the local areas to find one or more optimized sequence hypotheses of high probability for the local areas; and analyzing the optimized sequence hypotheses to identify a series of variation calls in the sample polynucleotide sequence.

Abstract: The invention provides methods of analyzing genes for differential relative allelic expression patterns. Haplotype blocks throughout the genomes of individuals are analyzed to identify haplotype patterns that are associated with specific differential relative allelic expression patterns. Haplotype blocks that contain associated haplotype patterns may be further investigated to identify genes or variants of genes involved in differential relative allelic expression patterns.