SummaryMany common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

Background - There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment. Methods - Using the UK Biobank (UKB) resource, we developed our own polygenic risk score (PRS) for coronary artery disease (CAD). We used an additional 60,000 UKB individuals to develop an integrated risk tool (IRT) that combined our PRS with established risk tools (either the American Heart Association/American College of Cardiology's Pooled Cohort Equations (PCE) or UK's QRISK3), and we tested our IRT in an additional, independent, set of 186,451 UKB individuals. Results - The novel CAD PRS shows superior predictive power for CAD events, compared to other published PRSs and is largely uncorrelated with PCE and QRISK3. When combined with PCE into an integrated risk tool, it has superior predictive accuracy. Overall, 10.4% of incident CAD cases were misclassified as low risk by PCE and correctly classified as high risk by the IRT, compared to 4.4% misclassified by the IRT and correctly classified by PCE. The overall net reclassification improvement for the IRT was 5.9% (95% CI 4.7-7.0). When individuals were stratified into age-by-sex subgroups the improvement was larger for all subgroups (range 8.3%-15.4%), with best performance in 40-54yo men (15.4%, 95% CI 11.6-19.3). Comparable results were found using a different risk tool (QRISK3), and also a broader definition of cardiovascular disease. Use of the IRT is estimated to avoid up to 12,000 deaths in the USA over a 5-year period. Conclusions - An integrated risk tool that includes polygenic risk outperforms current risk stratification tools and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person's polygenic risk.

The variation in weight within a shared environment is largely attributable to genetic factors. Whilst many genes/loci confer susceptibility to obesity, little is known about the genetic architecture of healthy thinness. Here, we characterise the heritability of thinness which we found was comparable to that of severe obesity (h2 = 28.07 vs 32.33% respectively), although with incomplete genetic overlap (r = -0.49, 95% CI [-0.17, -0.82], p = 0.003). In a genome-wide association analysis of thinness (n = 1,471) vs severe obesity (n = 1,456), we identified 10 loci previously associated with obesity, and demonstrate enrichment for established BMI-associated loci (pbinomial = 3.05x10-5). Simulation analyses showed that different association results between the extremes were likely in agreement with additive effects across the BMI distribution, suggesting different effects on thinness and obesity could be due to their different degrees of extremeness. In further analyses, we detected a novel obesity and BMI-associated locus at PKHD1 (rs2784243, obese vs. thin p = 5.99x10-6, obese vs. controls p = 2.13x10-6 pBMI = 2.3x10-13), associations at loci recently discovered with much larger sample sizes (e.g. FAM150B and PRDM6-CEP120), and novel variants driving associations at previously established signals (e.g. rs205262 at the SNRPC/C6orf106 locus and rs112446794 at the PRDM6-CEP120 locus). Our ability to replicate loci found with much larger sample sizes demonstrates the value of clinical extremes and suggest that characterisation of the genetics of thinness may provide a more nuanced understanding of the genetic architecture of body weight regulation and may inform the identification of potential anti-obesity targets.

The mammalian olfactory system displays species-specific adaptations to different ecological niches. To investigate the evolutionary dynamics of olfactory sensory neuron (OSN) subtypes across mammalian evolution, we applied RNA sequencing of whole olfactory mucosa samples from mouse, rat, dog, marmoset, macaque, and human. We find that OSN subtypes, representative of all known mouse chemosensory receptor gene families, are present in all analyzed species. Further, we show that OSN subtypes expressing canonical olfactory receptors are distributed across a large dynamic range and that homologous subtypes can be either highly abundant across all species or species/order specific. Highly abundant mouse and human OSN subtypes detect odorants with similar sensory profiles and sense ecologically relevant odorants, such as mouse semiochemicals or human key food odorants. Together, our results allow for a better understanding of the evolution of mammalian olfaction in mammals and provide insights into the possible functions of highly abundant OSN subtypes.

BackgroundEscherichia coli strains lacking the phosphoenolpyruvate: carbohydrate phosphotransferase system (PTS), which is the major bacterial component involved in glucose transport and its phosphorylation, accumulate high amounts of phosphoenolpyruvate that can be diverted to the synthesis of commercially relevant products. However, these strains grow slowly in glucose as sole carbon source due to its inefficient transport and metabolism. Strain PB12, with 400% increased growth rate, was isolated after a 120 hours adaptive laboratory evolution process for the selection of faster growing derivatives in glucose. Analysis of the genetic changes that occurred in the PB12 strain that lacks PTS will allow a better understanding of the basis of its growth adaptation and, therefore, in the design of improved metabolic engineering strategies for enhancing carbon diversion into the aromatic pathways.ResultsWhole genome analyses using two different sequencing methodologies: the Roche NimbleGen Inc. comparative genome sequencing technique, and high throughput sequencing with Illumina Inc. GAIIx, allowed the identification of the genetic changes that occurred in the PB12 strain. Both methods detected 23 non-synonymous and 22 synonymous point mutations. Several non-synonymous mutations mapped in regulatory genes (arcB, barA, rpoD, rna) and in other putative regulatory loci (yjjU, rssA and ypdA). In addition, a chromosomal deletion of 10,328 bp was detected that removed 12 genes, among them, the rppH, mutH and galR genes. Characterization of some of these mutated and deleted genes with their functions and possible functions, are presented.ConclusionsThe deletion of the contiguous rppH, mutH and galR genes that occurred simultaneously, is apparently the main reason for the faster growth of the evolved PB12 strain. In support of this interpretation is the fact that inactivation of the rppH gene in the parental PB11 strain substantially increased its growth rate, very likely by increasing glycolytic mRNA genes stability. Furthermore, galR inactivation allowed glucose transport by GalP into the cell. The deletion of mutH in an already stressed strain that lacks PTS is apparently responsible for the very high mutation rate observed.

We present and assess the UK Biobank (UKB) Polygenic Risk Score (PRS) Release, a set of PRSs for 28 diseases and 25 quantitative traits being made available on the individuals in UKB. We also release a benchmarking software tool to enable like-for-like performance evaluation for different PRSs for the same disease or trait. Extensive benchmarking shows the PRSs in the UKB Release to outperform a broad set of 81 published PRSs. For many of the diseases and traits we also validate the PRS algorithms in other cohorts. The availability of PRSs for 53 traits on the same set of individuals also allows a systematic assessment of their properties, and the increased power of these PRSs increases the evidence for their potential clinical benefit.

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Understanding the genetic structure of Native American populations is important to clarify their diversity, demographic history, and to identify genetic factors relevant for biomedical traits. Here, we show a demographic history reconstruction from 12 Native American whole genomes belonging to six distinct ethnic groups representing the three main described genetic clusters of Mexico (Northern, Southern, and Maya). Effective population size estimates of all Native American groups remained below 2,000 individuals for up to 10,000 years ago. The proportion of missense variants predicted as damaging is higher for undescribed (~ 30%) than for previously reported variants (~ 15%). Several variants previously associated with biological traits are highly frequent in the Native American genomes. These findings suggest that the demographic and adaptive processes that occurred in these groups shaped their genetic architecture and could have implications in biological processes of the Native Americans and Mestizos of today.