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Jeffrey Barrett, Carl Anderson and colleagues report the results of a large genome-wide association study of inflammatory bowel disease. They identify 25 new genome-wide significant loci, 3 of which contain integrin genes, and find that the associated variants at several of these loci are correlated with expression changes in response to immune stimulus. Genetic association studies have identified 215 risk loci for inflammatory bowel disease 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , thereby uncovering fundamental aspects of its molecular biology. We performed a genome-wide association study of 25,305 individuals and conducted a meta-analysis with published summary statistics, yielding a total sample size of 59,957 subjects. We identified 25 new susceptibility loci, 3 of which contain integrin genes that encode proteins in pathways that have been identified as important therapeutic targets in inflammatory bowel disease. The associated variants are correlated with expression changes in response to immune stimulus at two of these genes ( ITGA4 and ITGB8 ) and at previously implicated loci ( ITGAL and ICAM1 ). In all four cases, the expression-increasing allele also increases disease risk. We also identified likely causal missense variants in a gene implicated in primary immune deficiency, PLCG2 , and a negative regulator of inflammation, SLAMF8 . Our results demonstrate that new associations at common variants continue to identify genes relevant to therapeutic target identification and prioritization.

Statistical imputation of classical HLA alleles in case-control studies has become established as a valuable tool for identifying and fine-mapping signals of disease association in the MHC. Imputation into diverse populations has, however, remained challenging, mainly because of the additional haplotypic heterogeneity introduced by combining reference panels of different sources. We present an HLA type imputation model, HLA*IMP:02, designed to operate on a multi-population reference panel. HLA*IMP:02 is based on a graphical representation of haplotype structure. We present a probabilistic algorithm to build such models for the HLA region, accommodating genotyping error, haplotypic heterogeneity and the need for maximum accuracy at the HLA loci, generalizing the work of Browning and Browning (2007) and Ron et al. (1998). HLA*IMP:02 achieves an average 4-digit imputation accuracy on diverse European panels of 97% (call rate 97%). On non-European samples, 2-digit performance is over 90% for most loci and ethnicities where data available. HLA*IMP:02 supports imputation of HLA-DPB1 and HLA-DRB3-5, is highly tolerant of missing data in the imputation panel and works on standard genotype data from popular genotyping chips. It is publicly available in source code and as a user-friendly web service framework.

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.

Most research on genetic screening and precision oncology is based on individuals of European ancestry. We applied the National Health Service (NHS) England's cancer variant prioritisation workflow to evaluate the performance of these approaches in ethinically and ancestrally diverse populations. The second aim of the study was to assess the representativeness of the 100 000 Genomes Project cancer cohort of the population of England. In this cross-sectional analysis, whole-genome sequencing data from patients with cancer recruited into the 100 000 Genomes Project between February 2015 to December 2018 were analysed. Clinical information, including tumour stage and grade, was gathered from the NHS England National Cancer Registration and Analysis Service. Patients with cancer types with fewer than five individuals, haematological cancers, childhood cancers, unknown primary carcinomas, patients with indeterminate sex, and patients missing somatic mutations in genes were excluded. To assess ethnicity representation in the 100 000 Genomes Project, we calculated the recruitment ratios for self-reported ethnicities for patients with cancer recruited to the 100 000 Genomes Project and patients with cancer in England. We also analysed differences in classification rates for potentially pathogenic variants to assess ancestry-related differences in germline and somatic mutations of different ancestry groups. 14 775 patients with cancer were recruited between February, 2015, and December, 2018, into the 100 000 Genomes Project. There was no evidence of under-representation of diverse ethnic groups in the 100 000 Genomes Project when compared with the national statistics. The recruitment rate ratio for breast cancer was 2·2 (95% CI 1·6–3·0) for Black versus White women in the 100 000 Genomes Project compared with 0·81 (0·79–0·83) for Black versus White women in the national data (fold-change in rate ratios 2·7; 95% CI 2·0–3·7, p<0·0001), suggesting higher representation of Black women in the 100 000 Genomes Project than expected given the ethnicity-specific incidence rates in England. Compared with national rates, the 100 000 Genomes Project also had higher recruitment rates of Black versus White men with prostate cancer (fold-change in rate ratios 3·7; 1·8–7·5, p=0·0004), Black versus White men with bladder cancer (fold change in rate ratios 6·1; 2·0–18·8, p=0·0016), and Asian versus White women with breast cancer (fold change in rate ratios 1·4; 1·2–1·7, p=0·0008). Ancestry had a significant association with the likelihood of carrying a variant classified as a potentially pathogenic (likelihood ratio test p=0·0011). Potentially pathogenic variants were identified in 23 (4·6%) of 500 South Asian (adjusted model odds ratio [OR] 1·88, 95% CI 1·21–2·93, p=0·0052) and 24 (5·3%) of 453 African ancestry patients (OR 2·24, 1·44–3·48, p=0·0003) compared with 263 (2·2%) of 11 955 in European-ancestry patients. However, we found that fewer tumour mutations in actionable genes were identified for patients of non-European ancestry compared with patients of European ancestry when adjusting for sex and cancer type (likelihood ratio test p<0·0001). The was an excess of germline variants classified as potentially pathogenic variants in patients with non-European ancestry, which might impede the diagnostic process. Improved variant prioritisation workflows and more research in diverse groups are needed to ensure equitable implementation of genomics in cancer care. The UK Department of Health and Social Care and the EU's Horizon 2020 Research and Innovation Programme.

Genome-wide association studies of primary sclerosing cholangitis have identified 23 susceptibility loci. The majority of these loci reside in non-coding regions of the genome and are thought to exert their effect by perturbing the regulation of nearby genes. Here, we aim to identify these genes to improve the biological understanding of primary sclerosing cholangitis, and nominate potential drug targets. We first build an eQTL map for six primary sclerosing cholangitis-relevant T-cell subsets obtained from the peripheral blood of primary sclerosing cholangitis and ulcerative colitis patients. These maps identify 10,459 unique eGenes, 87% of which are shared across all six primary sclerosing cholangitis T-cell types. We then search for colocalisations between primary sclerosing cholangitis loci and eQTLs and undertake Bayesian fine-mapping to identify disease-causing variants. In this work, colocalisation analyses nominate likely primary sclerosing cholangitis effector genes and biological mechanisms at five non-coding (UBASH3A, PRKD2, ETS2 and AP003774.1/CCDC88B) and one coding (SH2B3) primary sclerosing cholangitis loci. Through fine-mapping we identify likely causal variants for a third of all primary sclerosing cholangitis-associated loci, including two to single variant resolution. Here, Goode et al. build a T-cell eQTL map from primary sclerosing cholangitis and ulcerative colitis patients. Integrating eQTL and GWAS, they nominate effector genes at six genetic risk loci for primary sclerosing cholangitis.

Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α = 2.5 × 10(-6)) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.

Isolated populations can empower the identification of rare variation associated with complex traits through next generation association studies, but the generalizability of such findings remains unknown. Here we genotype 1,267 individuals from a Greek population isolate on the Illumina HumanExome Beadchip, in search of functional coding variants associated with lipids traits. We find genome-wide significant evidence for association between R19X, a functional variant in APOC3 , with increased high-density lipoprotein and decreased triglycerides levels. Approximately 3.8% of individuals are heterozygous for this cardioprotective variant, which was previously thought to be private to the Amish founder population. R19X is rare (<0.05% frequency) in outbred European populations. The increased frequency of R19X enables discovery of this lipid traits signal at genome-wide significance in a small sample size. This work exemplifies the value of isolated populations in successfully detecting transferable rare variant associations of high medical relevance. Isolated populations may empower genetic association studies of complex traits. Here, the authors identify a rare cardioprotective APOC3 variant in a Greek population isolate and highlight the value of using population isolates to detect rare variants that confer disease risk.

Mutations in the germline generates all evolutionary genetic variation and is a cause of genetic disease. Parental age is the primary determinant of the number of new germline mutations in an individual’s genome 1 , 2 . Here we analysed the genome-wide sequences of 21,879 families with rare genetic diseases and identified 12 individuals with a hypermutated genome with between two and seven times more de novo single-nucleotide variants than expected. In most families (9 out of 12), the excess mutations came from the father. Two families had genetic drivers of germline hypermutation, with fathers carrying damaging genetic variation in DNA-repair genes. For five of the families, paternal exposure to chemotherapeutic agents before conception was probably a key driver of hypermutation. Our results suggest that the germline is well protected from mutagenic effects, hypermutation is rare, the number of excess mutations is relatively modest and most individuals with a hypermutated genome will not have a genetic disease. A study of 21,879 families with rare genetic diseases identifies 12 with 2- to 7-fold excess of germline mutations, most of which are due to DNA repair defects or exposure to mutagenic chemotherapy, although most individuals with a hypermutated genome will not have a genetic disease.

We built a reference panel with 342 million autosomal variants using 78,195 individuals from the Genomics England (GEL) dataset, achieving a phasing switch error rate of 0.18% for European samples and imputation quality of r 2  = 0.75 for variants with minor allele frequencies as low as 2 × 10 −4 in white British samples. The GEL-imputed UK Biobank genome-wide association analysis identified 70% of associations found by direct exome sequencing ( P  < 2.18 × 10 −11 ), while extending testing of rare variants to the entire genome. Coding variants dominated the rare-variant genome-wide association results, implying less disruptive effects of rare non-coding variants. A Genomics England haplotype reference panel constructed using sequence data from 78,195 individuals improves phasing and imputation accuracy. Imputation of the UK Biobank using this panel enables genome-wide rare-variant association analyses.

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. This Perspective considers the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science.