Inferring Selective Constraint from Population Genomic Data Suggests Recent Regulatory Turnover in the Human Brain.

ABSTRACT: The comparative genomics revolution of the past decade has enabled the discovery of functional elements in the human genome via sequence comparison. While that is so, an important class of elements, those specific to humans, is entirely missed by searching for sequence conservation across species. Here we present an analysis based on variation data among human genomes that utilizes a supervised machine learning approach for the identification of human-specific purifying selection in the genome. Using only allele frequency information from the complete low-coverage 1000 Genomes Project data set in conjunction with a support vector machine trained from known functional and nonfunctional portions of the genome, we are able to accurately identify portions of the genome constrained by purifying

SUBMITTER: Schrider DR 

PROVIDER: S-EPMC4700959 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature