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"Barrett, Jeffrey C."
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Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank
Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases (
nightingalehealth.com/atlas
). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.
The authors report a systematic analyses of blood biomarkers for metabolism against the whole spectrum of diseases in 100,000 individuals and reveals a prominent role of numerous metabolic biomarkers as risk markers beyond heart disease and diabetes.
Journal Article
An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci
Genome-wide association studies (GWASs) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. In the present study, we present an open resource that provides systematic fine mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease–disease and disease–molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine mapped to a single-coding causal variant and colocalized with a single gene. We trained a machine-learning model using the fine-mapped genetics and functional genomics data and 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring genes, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (odds ratio = 8.1, 95% confidence interval = 5.7, 11.5). These results are publicly available through a web portal (
http://genetics.opentargets.org
), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.
Open Targets Genetics is a community resource that provides systematic fine mapping at human GWAS loci, enabling users to prioritize genes at disease-associated regions and assess their potential as drug targets.
Journal Article
Common genetic variants contribute to risk of rare severe neurodevelopmental disorders
There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants
1
. However, patients with the same genetic defect can have different clinical presentations
2
–
4
, and some individuals who carry known disease-causing variants can appear unaffected
5
. Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic.
A genome-wide association study of approximately 7,000 patients with neurodevelopmental disorders demonstrates that overall risk and clinical presentation in putative monogenic disorders is also influenced by common genetic variants present in the general population.
Journal Article
De novo mutations in regulatory elements in neurodevelopmental disorders
We previously estimated that 42% of patients with severe developmental disorders carry pathogenic
de novo
mutations in coding sequences. The role of
de novo
mutations in regulatory elements affecting genes associated with developmental disorders, or other genes, has been essentially unexplored. We identified
de novo
mutations in three classes of putative regulatory elements in almost 8,000 patients with developmental disorders. Here we show that
de novo
mutations in highly evolutionarily conserved fetal brain-active elements are significantly and specifically enriched in neurodevelopmental disorders. We identified a significant twofold enrichment of recurrently mutated elements. We estimate that, genome-wide, 1–3% of patients without a diagnostic coding variant carry pathogenic
de novo
mutations in fetal brain-active regulatory elements and that only 0.15% of all possible mutations within highly conserved fetal brain-active elements cause neurodevelopmental disorders with a dominant mechanism. Our findings represent a robust estimate of the contribution of
de novo
mutations in regulatory elements to this genetically heterogeneous set of disorders, and emphasize the importance of combining functional and evolutionary evidence to identify regulatory causes of genetic disorders.
Analysis of rare
de novo
mutations in gene regulatory elements suggests that 1–3% of patients with neurodevelopmental disorders carry such mutations in elements that are active in the fetal brain.
Regulatory mutations in developmental disorders
Matthew Hurles and colleagues, as part of the Deciphering Developmental Disorders (DDD) project, examine the contribution of rare
de novo
mutations (DNMs) in regulatory elements—sections of DNA that regulate the expression of genes—to developmental disorders. The authors analysed targeted sequencing data for three classes of potential regulatory elements in 7,930 individuals with a severe, undiagnosed developmental disorder. They find an enrichment of DNMs in highly evolutionarily conserved elements that are active in the fetal brain. The authors previously estimated that 42% of patients with severe developmental disorders carry pathogenic DNMs in coding sequences. They now estimate that 1%–3% of the remaining patients carry pathogenic DNMs in fetal brain-active regulatory elements.
Journal Article
Evaluating coverage of genome-wide association studies
Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.
Journal Article
Exploring the genetic architecture of inflammatory bowel disease by whole-genome sequencing identifies association at ADCY7
Carl Anderson, Jeffrey Barrett and colleagues use whole-genome sequencing and imputation to explore the genetic architecture of inflammatory bowel disease. They identify a low-frequency missense variant in
ADCY7
that doubles risk of ulcerative colitis and detect a burden of very rare, damaging missense variants in known Crohn's disease risk genes.
To further resolve the genetic architecture of the inflammatory bowel diseases ulcerative colitis and Crohn's disease, we sequenced the whole genomes of 4,280 patients at low coverage and compared them to 3,652 previously sequenced population controls across 73.5 million variants. We then imputed from these sequences into new and existing genome-wide association study cohorts and tested for association at ∼12 million variants in a total of 16,432 cases and 18,843 controls. We discovered a 0.6% frequency missense variant in
ADCY7
that doubles the risk of ulcerative colitis. Despite good statistical power, we did not identify any other new low-frequency risk variants and found that such variants explained little heritability. We detected a burden of very rare, damaging missense variants in known Crohn's disease risk genes, suggesting that more comprehensive sequencing studies will continue to improve understanding of the biology of complex diseases.
Journal Article
Synthetic Associations Are Unlikely to Account for Many Common Disease Genome-Wide Association Signals
Furthermore, the proportion of GWAS signals attributable to synthetic associations has profound implications for the design of GWAS follow-up studies. [...]while Dickson et al. argue that synthetic associations are an \"obvious theoretical possibility,\" it is worthwhile to broadly assess, in light of other theoretical and empirical evidence, the prevalence of synthetic associations in complex human disease. Large-scale sequencing studies of thousands of cases and controls will be required to fully understand the genetic architecture of complex disease. Since the prevalence of synthetic association acutely affects the design of these studies (i.e., in terms of the sample composition and width of genomic region sequenced) it is worth carefully evaluating its contribution to missing heritability.
Journal Article
Pervasive Sharing of Genetic Effects in Autoimmune Disease
Genome-wide association (GWA) studies have identified numerous, replicable, genetic associations between common single nucleotide polymorphisms (SNPs) and risk of common autoimmune and inflammatory (immune-mediated) diseases, some of which are shared between two diseases. Along with epidemiological and clinical evidence, this suggests that some genetic risk factors may be shared across diseases-as is the case with alleles in the Major Histocompatibility Locus. In this work we evaluate the extent of this sharing for 107 immune disease-risk SNPs in seven diseases: celiac disease, Crohn's disease, multiple sclerosis, psoriasis, rheumatoid arthritis, systemic lupus erythematosus, and type 1 diabetes. We have developed a novel statistic for Cross Phenotype Meta-Analysis (CPMA) which detects association of a SNP to multiple, but not necessarily all, phenotypes. With it, we find evidence that 47/107 (44%) immune-mediated disease risk SNPs are associated to multiple-but not all-immune-mediated diseases (SNP-wise P(CPMA)<0.01). We also show that distinct groups of interacting proteins are encoded near SNPs which predispose to the same subsets of diseases; we propose these as the mechanistic basis of shared disease risk. We are thus able to leverage genetic data across diseases to construct biological hypotheses about the underlying mechanism of pathogenesis.
Journal Article
Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci
Jason Cooper and colleagues identify four new risk loci for type 1 diabetes through a meta-analysis of data from three genome-wide association studies, with replication in additional case-control and family-based samples, providing further insights into the genetic risk factors underlying this disease.
We carried out a meta-analysis of data from three genome-wide association (GWA) studies of type 1 diabetes (T1D), testing 305,090 SNPs in 3,561 T1D cases and 4,646 controls of European ancestry. We obtained further support for 4q27 (
IL2-IL21
,
P
= 1.9 × 10
−8
) and, after genotyping an additional 6,225 cases, 6,946 controls and 2,828 families, convincing evidence for four previously unknown and distinct risk loci in chromosome regions 6q15 (
BACH2
,
P
= 4.7 × 10
−12
), 10p15 (
PRKCQ
,
P
= 3.7 × 10
−9
), 15q24 (
CTSH
,
P
= 3.2 × 10
−15
) and 22q13 (
C1QTNF6
,
P
= 2.0 × 10
−8
).
Journal Article