The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.

The UK Biobank project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40-69 at recruitment. A rich variety of phenotypic and health-related information is available on each participant, making the resource unprecedented in its size and scope. Here we describe the genome-wide genotype data (~805,000 markers) collected on all individuals in the cohort and its quality control procedures. Genotype data on this scale offers novel opportunities for assessing quality issues, although the wide range of ancestries of the individuals in the cohort also creates particular challenges. We also conducted a set of analyses that reveal properties of the genetic data -such as population structure and relatedness -that can be important for downstream analyses. In addition, we phased and imputed genotypes into the dataset, using computationally efficient methods combined with the Haplotype Reference Consortium (HRC) and UK10K haplotype resource. This increases the number of testable variants by over 100-fold to ~96 million variants. We also imputed classical allelic variation at 11 human leukocyte antigen (HLA) genes, and as a quality control check of this imputation, we replicate signals of known associations between HLA alleles and many common diseases. We describe tools that allow efficient genome-wide association studies (GWAS) of multiple traits and fast phenome-wide association studies (PheWAS), which work together with a new compressed file format that has been used to distribute the dataset. As a further check of the genotyped and imputed datasets, we performed a test-case genome-wide association scan on a well-studied human trait, standing height.

Genetic data often exhibit patterns broadly consistent with “isolation by distance” – a phenomenon where genetic similarity decays with geographic distance. In a heterogeneous habitat this may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate differentiation between neighboring groups. We use the concept of “effective migration” to model the relationship between genetics and geography: in this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to visualize variation in effective migration across the habitat from geographically indexed genetic data. Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities. We illustrate its potential and limitations using simulations and data from elephant, human and A. thaliana populations. The resulting visualizations highlight important spatial features of population structure that are difficult to discern using existing methods for summarizing genetic variation.

SummaryBackgroundUnderstanding the genetic basis of airflow obstruction and smoking behaviour is key to determining the pathophysiology of chronic obstructive pulmonary disease (COPD). We used UK Biobank data to study the genetic causes of smoking behaviour and lung health.MethodsWe sampled individuals of European ancestry from UK Biobank, from the middle and extremes of the forced expiratory volume in 1 s (FEV1) distribution among heavy smokers (mean 35 pack-years) and never smokers. We developed a custom array for UK Biobank to provide optimum genome-wide coverage of common and low-frequency variants, dense coverage of genomic regions already implicated in lung health and disease, and to assay rare coding variants relevant to the UK population. We investigated whether there were shared genetic causes between different phenotypes defined by extremes of FEV1. We also looked for novel variants associated with extremes of FEV1 and smoking behaviour and assessed regions of the genome that had already shown evidence for a role in lung health and disease. We set genome-wide significance at p<5 × 10−8.FindingsUK Biobank participants were recruited from March 15, 2006, to July 7, 2010. Sample selection for the UK BiLEVE study started on Nov 22, 2012, and was completed on Dec 20, 2012. We selected 50 008 unique samples: 10 002 individuals with low FEV1, 10 000 with average FEV1, and 5002 with high FEV1 from each of the heavy smoker and never smoker groups. We noted a substantial sharing of genetic causes of low FEV1 between heavy smokers and never smokers (p=2·29 × 10−16) and between individuals with and without doctor-diagnosed asthma (p=6·06 × 10−11). We discovered six novel genome-wide significant signals of association with extremes of FEV1, including signals at four novel loci (KANSL1, TSEN54, TET2, and RBM19/TBX5) and independent signals at two previously reported loci (NPNT and HLA-DQB1/HLA-DQA2). These variants also showed association with COPD, including in individuals with no history of smoking. The number of copies of a 150 kb region containing the 5′ end of KANSL1, a gene that is important for epigenetic gene regulation, was associated with extremes of FEV1. We also discovered five new genome-wide significant signals for smoking behaviour, including a variant in NCAM1 (chromosome 11) and a variant on chromosome 2 (between TEX41 and PABPC1P2) that has a trans effect on expression of NCAM1 in brain tissue.InterpretationBy sampling from the extremes of the lung function distribution in UK Biobank, we identified novel genetic causes of lung function and smoking behaviour. These results provide new insight into the specific mechanisms underlying airflow obstruction, COPD, and tobacco addiction, and show substantial shared genetic architecture underlying airflow obstruction across individuals, irrespective of smoking behaviour and other airway disease.FundingMedical Research Council.

Genetic data often exhibit patterns broadly consistent with "isolation by distance" -a phenomenon where genetic similarity decays with geographic distance. In a heterogeneous habitat this may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate differentiation between neighboring groups. We use the concept of "effective migration" to model the relationship between genetics and geography: in this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to visualize variation in effective migration across the habitat from geographically indexed genetic data. Our approach uses a population genetic model to relate effective migration rates to expected genetic dissimilarities. We illustrate its potential and limitations using simulations and data from elephant, human and A. thaliana populations. The resulting visualizations highlight important spatial features of population structure that are difficult to discern using existing methods for summarizing genetic variation.

In many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer maps of population sizes and migration rates associated with different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates associated with different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when using a similar method that ignores haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ∼3,000 years in Europe.

One aspect in which retrieving named entities is different from retrieving documents is that the items to be retrieved -persons, locations, organizations -are only indirectly described by documents throughout the collection. Much work has been dedicated to finding references to named entities, in particular to the problems of named entity extraction and disambiguation. However, just as important for retrieval performance is how these snippets of text are combined to build named entity representations.We focus on the TREC expert search task where the goal is to identify people who are knowledgeable on a specific topic. Existing language modeling techniques for expert finding assume that terms and person entities are conditionally independent given a document. We present theoretical and experimental evidence that this simplifying assumption ignores information on how named entities relate to document content. To address this issue, we propose a new document representation which emphasizes text in proximity to entities and thus incorporates sequential information implicit in text. Our experiments demonstrate that the proposed model significantly improves retrieval performance. The main contribution of this work is an effective formal method for explicitly modeling the dependency between the named entities and terms which appear in a document.

Abstract