Analyses in 2016

Andrew Lane and colleagues analyze somatic alterations across 21 tumor types for evidence of sex bias. They find that an excess of genes on the non-pseudoautosomal region of the X chromosome harbor loss-of-function mutations more frequently in males, suggesting that biallelic expression of these genes in females contributes to reduced cancer incidence in females across a variety of tumor types.

Donald Conrad and colleagues present a method, PSAP, for prioritizing potential Mendelian disease-causing variants in single human exomes using pathogenicity scores and observed allele frequencies in the unaffected population. They apply PSAP to cohorts of undiagnosed disease exomes and identify candidate disease variants for future study.

Jung Kyoon Choi and colleagues identify sets of regulatory mutations in breast and lung cancer samples that converge on the same gene target across individual samples. They use features of these mutation sets to develop a method for predicting functionally recurrent regulatory mutations that may function as drivers in cancer.

Nicole Soranzo, Alexander Reiner, Paul Auer and colleagues use whole-genome sequencing data to impute the genotypes of over 35,000 individuals and perform a genome-wide association study for 20 quantitative cardiometabolic and hematological traits. They find 17 new associations and apply fine-mapping analysis to resolve causal variants for a number of the loci.

Peter Scacheri and colleagues identify ‘outside’ SNPs that physically interact with GWAS risk SNPs as part of a target gene's regulatory circuitry. Their findings suggest a model whereby outside variants and GWAS SNPs that physically interact collude to influence target transcript levels as well as clinical risk.

Ben Lehner and colleagues analyze data from matched exomes and transcriptomes from tumors across 27 cancer types to elucidate rules linking premature termination codon location to nonsense-mediated mRNA decay (NMD). They propose a model that explains variability in NMD efficiency and find evidence of positive and negative selection on NMD-initiating mutations in tumors.

Douglas Ruderfer, Shaun Purcell and colleagues characterized the rates and properties of rare genic copy number variants in exome sequencing data from nearly 60,000 individuals in the Exome Aggregation Consortium. These data are available through an integrated database that spans the spectrum of human genetic variation, aiding in the interpretation of personal genomes and population-based disease studies.

Yun Chen, Albin Sandelin, Torben Heick Jensen and colleagues describe general rules governing the expression of reverse-oriented promoter upstream transcripts (PROMPTs) based on the orientation and proximity of promoter pairs. They characterize how the distance between promoters affects the expression of PROMPTs and the usage of alternate mRNA transcription start sites.

Albert Tenesa and colleagues report an analysis of the heritability of 12 complex diseases in 1,555,906 individuals from the UK Biobank. They find that SNP heritability explains a higher proportion of estimated heritability when shared familial environmental factors are taken into account.

Andrea Califano, Mariano Alvarez and colleagues present an approach, VIPER, for inferring protein activity in single cancer samples based on expression of a protein's downstream targets. The authors use VIPER to predict aberrant protein activity independent of mutational status and validate drug sensitivity predictions using in vitro assays.

Li Ding, Feng Chen and colleagues report a pan-cancer analysis using a new computational tool, HotSpot3D, to identify mutational hotspots in the encoded three-dimensional protein structure, which suggest their functional involvement in cancer. They use a mutation–drug cluster analysis to predict more than 800 potentially druggable mutations.

Shirley Liu, Jun Liu and colleagues present a computational method to infer the CDR3 sequences of tumor-infiltrating T cells from RNA-seq data. They apply the method to 9,142 samples across 29 cancer types and show that it can be used to simultaneously identify immunogenic neoantigens and tumor-reactive T cell clonotypes.

Jacob Gratten and colleagues use population genetic models to assess the genetic relationship between paternal age and risk of psychiatric illness. These models suggest that age-related mutations are unlikely to explain much of the increased risk of psychiatric disorders in children of older fathers.

Joseph Pickrell and colleagues analyze genome-wide association data for 42 human phenotypes or diseases and identify several hundred loci influencing multiple traits. They also find several traits with overlapping genetic architectures as well as pairs of traits showing evidence of a causal relationship.

Gad Getz and colleagues analyze mutational patterns in urothelial cancer and find a strong association between mutations in the nucleotide excision repair gene ERCC2 and a distinct mutational signature. They also find that the activity of this signature is associated with smoking, independently of ERCC2 mutational status.

Chris Tyler-Smith, Carlos Bustamante and colleagues report an analysis of 1,244 human Y chromosomes from the 1000 Genomes Project. They find that copy number variants have a higher predicted functional impact than other variant classes and infer bursts of male population expansion corresponding to historical periods of migration and technological innovations.

Sean Whalen and colleagues present a computational method, TargetFinder, for reconstructing three-dimensional regulatory landscapes using one-dimensional genomic features. TargetFinder identifies the minimal set of features necessary to predict individual interacting enhancer–promoter pairs and accurately distinguishes them from non-interacting pairs.

Jian Yang and colleagues propose a method that integrates summary data from GWAS and eQTL studies to identify genes whose expression levels are associated with complex traits because of pleiotropy. They apply the method to five human complex traits and prioritize 126 genes for future functional studies.

Varun Aggarwala and Benjamin Voight analyze human polymorphism data and develop an expanded sequence context model that explains >81% of variability in substitution probabilities, highlighting mutation-promoting motifs. Using their model, they present substitution intolerance scores for genes and a new intolerance score for amino acids, and demonstrate clinical use of the model in neuropsychiatric diseases.

Shamil Sunyaev, Alexander Gimelbrant and colleagues report an analysis of the genetic variability in human monoallelically expressed genes. They find that genes with monoallelic expression show greater genetic diversity than biallelically expressed genes and that this diversity is associated with greater allelic age.