Evolution and Functional Impact of Rare Coding Variation from Deep Sequencing of Human Exomes

By Jacob A. Tennessen, Abigail W. Bigham, T. O’Connor, Wenqing Fu, Eimear E. Kenny, Simon Gravel, Sean R Mcgee, R. Do, Xiaomin Liu, Goo Jun and 13 more. 2012

1,698 citations recorded by the source corpus.

A Deep Look Into Our Genes Recent debates have focused on the degree of genetic variation and its impact upon health at the genomic level in humans (see the Perspective by Casals and Bertranpetit). Tennessen et al. (p. 64, published online 17 May), looking at all of the protein-coding genes in the human genome, and Nelson et al. (p. 100, published online 17 May), looking at genes that encode drug targets, address this question through deep sequencing efforts on samples from multiple individuals. The findings suggest that most human variation is rare, not shared between populations, and that rare variants are likely to play a role in human health. Most functionally consequential variants in protein-coding genes are rare and, thus, difficult to find. As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, background): “…rare alleles that cause Mendelian disease (Bamshad et al., 2011), scanning for potentially medically actionable alleles in an individual’s genome (Ashley et al., 2010), and proﬁling the spectrum of rare variation uncovered by deep sequencing of large populations (Tennessen et al., 2012).”
The human splicing code reveals new insights into the genetic determinants of disease (2014, methodology): “Our approach contrasts with techniques that use functional annotations of the genome (2, 8, 46), tools that are trained using existing disease annotations and thus suffer from overfitting to known mutations or severe selection bias (7–9, 47, 48); genome-wide association studies (GWAS) (49, 50); and ”
Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity (2017, neutral): “Sequencing and exome capture methods have been previously described 33,34 .”
GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations (2013, methodology): “Each variant in an input VCF file is extensively annotated through automatic (via Tabix [18] and pysam [19]) comparisons to a comprehensive and growing set of genomic annotation files including: dbSNP [20], ENCODE [6], ClinVar, 1000 Genomes [21], the Exome Sequencing Project [22], KEGG [23], GERP sc”
SIFT missense predictions for genomes (2015, background): “It has already been used in numerous disease, mutation and genetic studies 14–16 .”
Rare-variant association analysis: study designs and statistical tests. (2014, background): “28,29 The T2D-GENES Consortium has sequenced the exomes of ~10,000 individuals across ﬁve ancestry groups with the aim of identifying genetic variants associated with T2D and metabolic phenotypes.”
Ensembl 2016 (2015, methodology): “The second update (October 2015) incorporated variants from the Exome Aggregation Consortium (ExAC) (38) and NHLBI Exome Sequencing Project (39).”
The UniProtKB guide to the human proteome (2016, background): “Recent advances in sequencing technology generate large datasets of variants that provide a comprehensive view of human genetic variation (1).”