Genome-wide significance testing of variation from single case exomes.

Abstract

Standard techniques from genetic epidemiology are ill-suited to formally assess the significance of variants identified from a single case. We developed a statistical inference framework for identifying unusual functional variation from a single genome, what we refer to as the “n-of-one” problem. Using this approach we assess our ability to identify the causal genotypes in over 5 million simulated cases of Mendelian disease, identifying 39% of disease genotypes as the most damaging unit in a typical exome background. We apply our approach to 129 n-of-one families from the Undiagnosed Diseases Program, nominating 60% of 30 disease genes determined to be diagnostic by a standard clinical workup. Our method can currently produce well calibrated p-values when applied to single genomes, can facilitate integration of multiple data types for n-of-one analyses, and, with further work, could become a widely used epidemiological method like linkage analysis or GWAS.