The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

Genomic atlas of the human plasma proteome.

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations.

SUMMARY: PhenoScanner is a curated database of publicly available results from large-scale genetic association studies in humans. This online tool facilitates 'phenome scans', where genetic variants are cross-referenced for association with many phenotypes of different types. Here we present a major update of PhenoScanner ('PhenoScanner V2'), including over 150 million genetic variants and more than 65 billion associations (compared to 350 million associations in PhenoScanner V1) with diseases and traits, gene expression, metabolite and protein levels, and epigenetic markers. The query options have been extended to include searches by genes, genomic regions and phenotypes, as well as for genetic variants. All variants are positionally annotated using the Variant Effect Predictor and the phenotypes are mapped to Experimental Factor Ontology terms. Linkage disequilibrium statistics from the 1000 Genomes project can be used to search for phenotype associations with proxy variants. AVAILABILITY AND IMPLEMENTATION: PhenoScanner V2 is available at www.phenoscanner.medschl.cam.ac.uk.

PhenoScanner: a database of human genotype-phenotype associations.

PhenoScanner is a curated database of publicly available results from large-scale genetic association studies. This tool aims to facilitate 'phenome scans', the cross-referencing of genetic variants with many phenotypes, to help aid understanding of disease pathways and biology. The database currently contains over 350 million association results and over 10 million unique genetic variants, mostly single nucleotide polymorphisms. It is accompanied by a web-based tool that queries the database for associations with user-specified variants, providing results according to the same effect and non-effect alleles for each input variant. The tool provides the option of searching for trait associations with proxies of the input variants, calculated using the European samples from 1000 Genomes and Hapmap. AVAILABILITY AND IMPLEMENTATION: PhenoScanner is available at www.phenoscanner.medschl.cam.ac.uk CONTACT: jrs95@medschl.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

A Role for Non-B DNA Forming Sequences in Mediating Microlesions Causing Human Inherited Disease.

Missense/nonsense mutations and microdeletions/microinsertions (<21 bp) represent ∼ 76% of all mutations causing human inherited disease, and their occurrence has been associated with sequence motifs (direct, inverted, and mirror repeats; G-quartets) capable of adopting non-B DNA structures. We found that a significant proportion (∼ 21%) of both microdeletions and microinsertions occur within direct repeats, and are explicable by slipped misalignment. A novel mutational mechanism, DNA triplex formation followed by DNA repair, may explain ∼ 5% of microdeletions and microinsertions at mirror repeats. Further, G-quartets, direct, and inverted repeats also appear to play a prominent role in mediating missense mutations, whereas only direct and inverted repeats mediate nonsense mutations. We suggest a mutational mechanism involving slipped strand mispairing, slipped structure formation, and DNA repair, to explain ∼ 15% of missense and ∼ 12% of nonsense mutations yielding perfect direct repeats from imperfect repeats, or the extension of existing direct repeats. Similar proportions of missense and nonsense mutations were explicable by hairpin/loop formation and DNA repair, yielding perfect inverted repeats from imperfect repeats. We also propose a model for single base-pair substitution based on one-electron oxidation reactions at G-quadruplex DNA. Overall, the proposed mechanisms provide support for a role for non-B DNA structures in human gene mutagenesis.

Remotely acting SMCHD1 gene regulatory elements: in silico prediction and identification of potential regulatory variants in patients with FSHD.

BACKGROUND: Facioscapulohumeral dystrophy (FSHD) is commonly associated with contraction of the D4Z4 macro-satellite repeat on chromosome 4q35 (FSHD1) or mutations in the SMCHD1 gene (FSHD2). Recent studies have shown that the clinical manifestation of FSHD1 can be modified by mutations in the SMCHD1 gene within a given family. The absence of either D4Z4 contraction or SMCHD1 mutations in a small cohort of patients suggests that the disease could also be due to disruption of gene regulation. In this study, we postulated that mutations responsible for exerting a modifier effect on FSHD might reside within remotely acting regulatory elements that have the potential to interact at a distance with their cognate gene promoter via chromatin looping. To explore this postulate, genome-wide Hi-C data were used to identify genomic fragments displaying the strongest interaction with the SMCHD1 gene. These fragments were then narrowed down to shorter regions using ENCODE and FANTOM data on transcription factor binding sites and epigenetic marks characteristic of promoters, enhancers and silencers. RESULTS: We identified two regions, located respectively ~14 and ~85 kb upstream of the SMCHD1 gene, which were then sequenced in 229 FSHD/FSHD-like patients (200 with D4Z4 repeat units <11). Three heterozygous sequence variants were found ~14 kb upstream of the SMCHD1 gene. One of these variants was found to be of potential functional significance based on DNA methylation analysis. Further functional ascertainment will be required in order to establish the clinical/functional significance of the variants found. CONCLUSIONS: In this study, we propose an improved approach to predict the possible locations of remotely acting regulatory elements that might influence the transcriptional regulation of their associated gene(s). It represents a new way to screen for disease-relevant mutations beyond the immediate vicinity of the specific disease gene. It promises to be useful for investigating disorders in which mutations could occur in remotely acting regulatory elements.