Biology subjects

Vukcevic, D.

Publications and source records attributed to Vukcevic, D..

3 recordsLinked to original sources

bioRxiv2017

Genome-wide genetic data on ~500,000 UK Biobank participants

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.

genetics↗

bioRxiv2017

Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.

genetics↗

bioRxiv2016

Practical Use of Methods for Imputation of HLA Alleles from SNP Genotype Data

The human leukocyte antigen (HLA) genes play an essential role in immune function. Typing of HLA alleles is critical for transplantation and is informative for many disease associations. The high cost of accurate lab-based HLA typing has precluded its use in large-scale disease-association studies. The development of statistical methods to type alleles using linkage disequilibrium with nearby SNPs, called HLA imputation, has allowed large cohorts of individuals to be typed accurately, so that massive numbers of affected individuals and controls may be studied. This has resulted in many important findings. Several HLA imputation methods have been widely used, however their relative performance has not been adequately addressed. We have conducted a comprehensive study to evaluate the most widely used HLA imputation methods. We assembled a multi-ethnic panel of 10,561 individuals with SNP genotype data and lab-based typing of alleles at 11 HLA genes at two-field resolution, and used it to train and validate each method. Use of this panel leads to imputation accuracy far superior to what is currently publicly available. We present a highly-accurate new imputation method, HLA*IMP:03. We address the question of optimal use of HLA imputations in tests of genetic association, showing that it is usually not necessary to apply a probability threshold to achieve maximal power. We also investigated the effect on accuracy of SNP density and population stratification at the continental level and show that neither of these are a significant concern.

genetics↗