In this week's Nature Genetics, a team led by scientists from Amgen's DeCode Genetics describes a weighting scheme for sequence variants based on their annotation that increases the power of whole-genome association studies. While the standard approach to GWAS has been to assign equal prior probability of association to all sequence variants tested, some are more likely to affect protein function and therefore are likely to be more causative. To address this issue, the researchers propose a weighted Bonferroni adjustment that controls for the family-wise error rate (FWER) using the enrichment of sequence annotations among association signals as weights. They show that this tweak increases the power to detect associations over the standard Bonferroni correction.
And in Nature Biotechnology, two Carnegie Mellon University scientists report on a new method for the rapid search of thousands of short-read sequencing experiments. Called Sequence Bloom Trees (SBTs), the approach searches large data archives for all experiments that involve a given sequence. They demonstrate SBTs by using tem to search 2,652 human blood, breast, and brain RNA-seq experiments for all 214,293 known transcripts in under four days using less than 239 MB of RAM and a single CPU.