HapMap Exome
HapMap Exome
We generated exome sequence data from 11 HapMap samples: NA12891, NA12892, NA19238, NA12878, NA15510, NA18507, NA18517, NA18555, NA18956, NA19129 and NA19240, most of which have been included as part of the 1000 Genomes Project. In-solution exome capture was performed using Roche NimbleGen EZ Exome SeqCap v2 (44 Mbp including 36 Mbp exon target). NA12891 and NA12892 were sequenced on Illumina GAIIx platform with 76 bp paired-end reads with the remainder sequenced using Illumina HiSeq2000 platform (50 bp paired-end reads).
Large amounts of exome data are currently being generated from controls and disease individuals. While several analytical methods have been developed to routinely detect point mutations and small INDELs up to 10 bp, larger events are more difficult to discover because of their computational expense. We developed an algorithm to detect smaller INDELs and structural variants ranging in length from 1 bp up to 1 Mbp within exome sequence datasets using a split-read approach. The method requires paired-end mapping data and searches specifically for clusters of one-end anchored placements using mrsFAST alignments. Our approach decomposes the unanchored end into smaller subsequences and maps them locally to identify the size, content, and location of the structural variant within exons. Using this algorithm, we construct a computational pipeline and apply it to exome datasets we generated from samples sequenced as part of the 1000 Genomes Project. Comparing to genome sequence data, we show good specificity (70%) and high sensitivity (87%) including the discovery and validation of a significant number of novel indels and structural variants within protein-coding sequence. Using parent-child trio exome sequencing datasets, we show how split-read and read-depth approaches can be combined to enrich for de novo mutational events. Our method can detect both indels and structural variants irrespective of the size of an event and can recover events within low complexity and repetitive regions missed by other methods.