Gene-level integrated metric of negative selection (GIMS) prioritizes candidate genes for nephrotic syndrome.

Publication Type:

Journal Article


PLoS One, Volume 8, Issue 11, p.e81062 (2013)


asb, Databases, Genetic, Genes, Recessive, Genetics, Population, Genome, Human, Genomics, Glomerulosclerosis, Focal Segmental, Humans, Kidney Glomerulus, Mutation, Nephrotic Syndrome, Podocytes, Selection, Genetic


Nephrotic syndrome (NS) gene discovery efforts are now occurring in small kindreds and cohorts of sporadic cases. Power to identify causal variants in these groups beyond a statistical significance threshold is challenging due to small sample size and/or lack of family information. There is a need to develop novel methods to identify NS-associated variants. One way to determine putative functional relevance of a gene is to measure its strength of negative selection, as variants in genes under strong negative selection are more likely to be deleterious. We created a gene-level, integrated metric of negative selection (GIMS) score for 20,079 genes by combining multiple comparative genomics and population genetics measures. To understand the utility of GIMS for NS gene discovery, we examined this score in a diverse set of NS-relevant gene sets. These included genes known to cause monogenic forms of NS in humans as well as genes expressed in the cells of the glomerulus and, particularly, the podocyte. We found strong negative selection in the following NS-relevant gene sets: (1) autosomal-dominant Mendelian focal segmental glomerulosclerosis (FSGS) genes (p = 0.03 compared to reference), (2) glomerular expressed genes (p = 4×10(-23)), and (3) predicted podocyte genes (p = 3×10(-9)). Eight genes causing autosomal dominant forms of FSGS had a stronger combined score of negative selection and podocyte enrichment as compared to all other genes (p = 1 x 10(-3)). As a whole, recessive FSGS genes were not enriched for negative selection. Thus, we also created a transcript-level, integrated metric of negative selection (TIMS) to quantify negative selection on an isoform level. These revealed transcripts of known autosomal recessive disease-causing genes that were nonetheless under strong selection. We suggest that a filtering strategy that includes measuring negative selection on a gene or isoform level could aid in identifying NS-related genes. Our GIMS and TIMS scores are available at