Genetics of 38 blood and urine biomarkers in the UK Biobank, bioRxiv, 2019-06-05
AbstractClinical laboratory tests are a critical component of the continuum of care and provide a means for rapid diagnosis and monitoring of chronic disease. In this study, we systematically evaluated the genetic basis of 38 blood and urine laboratory tests measured in 358,072 participants in the UK Biobank and identified 1,857 independent loci associated with at least one laboratory test, including 488 large-effect protein truncating, missense, and copy-number variants. We tested these loci for enrichment in specific single cell types in kidney, liver, and pancreas relevant to disease aetiology. We then causally linked the biomarkers to medically relevant phenotypes through genetic correlation and Mendelian randomization. Finally, we developed polygenic risk scores (PRS) for each biomarker and built multi-PRS models using all 38 PRSs simultaneously. We found substantially improved prediction of incidence in FinnGen (n=135,500) with the multi-PRS relative to single-disease PRSs for renal failure, myocardial infarction, liver fat percentage, and alcoholic cirrhosis. Together, our results show the genetic basis of these biomarkers, which tissues contribute to the biomarker function, the causal influences of the biomarkers, and how we can use this to predict disease.
biorxiv genetics 100-200-users 2019Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk, Nature Genetics, 2019-05-27
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,790 autism spectrum disorder (ASD) simplex families reveals a role in disease for noncoding mutations—ASD probands harbor both transcriptional- and post-transcriptional-regulation-disrupting de novo mutations of significantly higher functional impact than those in unaffected siblings. Further analysis suggests involvement of noncoding mutations in synaptic transmission and neuronal development and, taken together with previous studies, reveals a convergent genetic landscape of coding and noncoding mutations in ASD. We demonstrate that sequences carrying prioritized mutations identified in probands possess allele-specific regulatory activity, and we highlight a link between noncoding mutations and heterogeneity in the IQ of ASD probands. Our predictive genomics framework illuminates the role of noncoding mutations in ASD and prioritizes mutations with high impact for further study, and is broadly applicable to complex human diseases.
nature genetics genetics 200-500-users 2019The genetic makeup of the electrocardiogram, bioRxiv, 2019-05-25
AbstractSince its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.
biorxiv genetics 0-100-users 2019Can education be personalised using pupils’ genetic data?, bioRxiv, 2019-05-24
AbstractThe predictive power of polygenic scores for some traits now rivals that of more classical phenotypic measures, and as such they have been promoted as a potential tool for genetically informed policy. However, how predictive polygenic scores are conditional on other easily available phenotypic data is not well understood. Using data from a UK cohort study, the Avon Longitudinal Study of Parents and Children, we investigated how well polygenic scores for education predict individuals’ realised attainment over and above phenotypic data available to schools. Across our sample children’s polygenic scores predicted their educational outcomes almost as well as parent’s socioeconomic position or education. There was high overlap between the polygenic score and attainment distributions, leading to weak predictive accuracy at the individual level. Furthermore, conditional on prior attainment the polygenic score was not predictive of later attainment. Our results suggest that polygenic scores are informative for identifying group level differences, but they currently have limited use in predicting individual attainment.
biorxiv genetics 100-200-users 2019