Efficient Bayesian mixed-model analysis increases association power in large cohorts

Assessing potential shared genetic aetiology between body mass index and sleep duration in 142,209 individuals

Observational studies find an association between increased body mass index (BMI) and short self-reported sleep duration in adults. However, the underlying biological mechanisms that underpin these associations are unclear. Recent findings from the UK Biobank suggest a weak genetic correlation between BMI and self-reported sleep duration. However, the potential shared genetic aetiology between these traits has not been examined using a comprehensive approach. To investigate this, we created a polygenic risk score (PRS) of BMI and examined its association with self-reported sleep duration in a combination of individual participant data and summary-level data, with a total sample size of 142,209 individuals. Although we observed a nonsignificant genetic correlation between BMI and sleep duration, using LD score regression (rg  = -0.067 [SE = 0.039], P = 0.092) we found that a PRS of BMI is associated with a decrease in sleep duration (unstandardized coefficient = -1.75 min [SE = 0.67], P = 6.13 × 10-7 ), but explained only 0.02% of the variance in sleep duration. Our findings suggest that BMI and self-reported sleep duration possess a small amount of shared genetic aetiology and other mechanisms must underpin these associations.

Genome-wide association analyses identify new loci influencing intraocular pressure

Elevated intraocular pressure (IOP) is a significant risk factor for glaucoma, the leading cause of irreversible blindness worldwide. While previous studies have identified numerous genetic variants associated with IOP, these loci only explain a fraction of IOP heritability. Recently established of biobank repositories have resulted in large amounts of data, enabling the identification of the remaining heritability for complex traits. Here, we describe the largest genome-wide association study of IOP to date using participants of European ancestry from the UK Biobank. We identified 671 directly genotyped variants that are significantly associated with IOP (P < 5 × 10-8). In addition to 103 novel loci, the top ranked novel IOP genes are LMX1B, NR1H3, MADD and SEPT9. We replicated these findings in an external population and examined the pleiotropic nature of these loci. These discoveries not only further our understanding of the genetic architecture of IOP, but also shed new light on the biological processes underlying glaucoma.

G2P: a Genome-Wide-Association-Study simulation tool for genotype simulation, phenotype simulation and power evaluation

Motivation

Plenty of Genome-Wide-Association-Study (GWAS) methods have been developed for mapping genetic markers that associated with human diseases and agricultural economic traits. Computer simulation is a nice tool to test the performances of various GWAS methods under certain scenarios. Existing tools are either inefficient in terms of computation and memory efficiency or inconvenient to use to simulate big, realistic genotype data and phenotype data to evaluate available GWAS methods.

Results

Here, we present a GWAS simulation tool named G2P that can be used to simulate genotype data, phenotype data and perform power evaluation of GWAS methods. G2P is a user-friendly tool with all functions is provided in both graphical user interface and pipeline manners and it is available for Windows, Mac and Linux environments. Furthermore, G2P achieves maximum efficiency in terms of both memory usage and simulation speed; with G2P, the simulation of genotype data that includes 1 000 000 samples and 2 000 000 markers can be accomplished in 5 h.

Availability and implementation

The G2P software, user manual, and example datasets are freely available at GitHub: https://github.com/XiaoleiLiuBio/G2P.

Supplementary information

Supplementary data are available at Bioinformatics online.

Effect of increased body mass index on risk of diagnosis or death from cancer

Background

Whether body mass index (BMI) is causally associated with the risk of being diagnosed with or dying from any cancer remains unclear. Weight reduction has clinical importance for cancer control only if weight gain causes cancer development or death. We aimed to answer the question 'does genetically predicted BMI influence my risk of being diagnosed with or dying from any cancer'.

Methods

We used a Mendelian randomisation (MR) approach to estimate causal effect of BMI in 46,155 white-British participants aged between 40 and 69 years at recruitment (median age at follow-up 61 years) from the UK Biobank, who developed any type of cancer, among whom 6998 died from cancer. To derive MR instruments for BMI, we selected up to 390,628 cancer-free participants.

Results

For each standard deviation (4.78 units) increase in genetically predicted BMI, we estimated a causal odds ratio (COR) of 1.07 (1.02–1.12) and 1.28 (1.16–1.41) for overall cancer risk and mortality, respectively. The corresponding estimates were similar for males and females, and smokers and non-smokers.

Conclusions

Higher genetically predicted BMI increases the risk of being diagnosed with or dying from any cancer. These data suggest that increased overall weight may causally increase overall cancer incidence and mortality among Europeans.

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

Efficient cross-trait penalized regression increases prediction accuracy in large cohorts using secondary phenotypes

We introduce cross-trait penalized regression (CTPR), a powerful and practical approach for multi-trait polygenic risk prediction in large cohorts. Specifically, we propose a novel cross-trait penalty function with the Lasso and the minimax concave penalty (MCP) to incorporate the shared genetic effects across multiple traits for large-sample GWAS data. Our approach extracts information from the secondary traits that is beneficial for predicting the primary trait based on individual-level genotypes and/or summary statistics. Our novel implementation of a parallel computing algorithm makes it feasible to apply our method to biobank-scale GWAS data. We illustrate our method using large-scale GWAS data (~1M SNPs) from the UK Biobank (N = 456,837). We show that our multi-trait method outperforms the recently proposed multi-trait analysis of GWAS (MTAG) for predictive performance. The prediction accuracy for height by the aid of BMI improves from R² = 35.8% (MTAG) to 42.5% (MCP + CTPR) or 42.8% (Lasso + CTPR) with UK Biobank data.

Information of genetic architectures of complex traits can be leveraged for predicting phenotypes. Here, the authors develop CTPR (Cross-Trait Penalized Regression), a method for multi-trait polygenic risk prediction using individual-level genotypes and/or summary statistics from large cohorts.

An atlas of genetic influences on osteoporosis in humans and mice

Osteoporosis is a common aging-related disease diagnosed primarily using bone mineral density (BMD). We assessed genetic determinants of BMD as estimated by heel quantitative ultrasound in 426,824 individuals, identifying 518 genome-wide significant loci (301 novel), explaining 20% of its variance. We identified 13 bone fracture loci, all associated with estimated BMD (eBMD), in ~1.2 million individuals. We then identified target genes enriched for genes known to influence bone density and strength (maximum odds ratio (OR) = 58, P = 1 × 10-75) from cell-specific features, including chromatin conformation and accessible chromatin sites. We next performed rapid-throughput skeletal phenotyping of 126 knockout mice with disruptions in predicted target genes and found an increased abnormal skeletal phenotype frequency compared to 526 unselected lines (P < 0.0001). In-depth analysis of one gene, DAAM2, showed a disproportionate decrease in bone strength relative to mineralization. This genetic atlas provides evidence linking associated SNPs to causal genes, offers new insight into osteoporosis pathophysiology, and highlights opportunities for drug development.

Fast and flexible linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis

OBJECTIVE

Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

RESEARCH DESIGN AND METHODS

In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

RESULTS

The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; P < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

CONCLUSIONS

An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.

Genome-wide gene-based analyses of weight loss interventions identify a potential role for NKX6.3 in metabolism

Hundreds of genetic variants have been associated with Body Mass Index (BMI) through genome-wide association studies (GWAS) using observational cohorts. However, the genetic contribution to efficient weight loss in response to dietary intervention remains unknown. We perform a GWAS in two large low-caloric diet intervention cohorts of obese participants. Two loci close to NKX6.3/MIR486 and RBSG4 are identified in the Canadian discovery cohort (n = 1166) and replicated in the DiOGenes cohort (n = 789). Modulation of HGTX (NKX6.3 ortholog) levels in Drosophila melanogaster leads to significantly altered triglyceride levels. Additional tissue-specific experiments demonstrate an action through the oenocytes, fly hepatocyte-like cells that regulate lipid metabolism. Our results identify genetic variants associated with the efficacy of weight loss in obese subjects and identify a role for NKX6.3 in lipid metabolism, and thereby possibly weight control.

Individuals show large variability in their capacity to lose weight and maintain this weight. Here, the authors perform GWAS in two weight loss intervention cohorts and identify two genetic loci associated with weight loss that are taken forward for Bayesian fine-mapping and functional assessment in flies.

BLINK: a package for the next level of genome-wide association studies with both individuals and markers in the millions

Big datasets, accumulated from biomedical and agronomic studies, provide the potential to identify genes that control complex human diseases and agriculturally important traits through genome-wide association studies (GWAS). However, big datasets also lead to extreme computational challenges, especially when sophisticated statistical models are employed to simultaneously reduce false positives and false negatives. The newly developed fixed and random model circulating probability unification (FarmCPU) method uses a bin method under the assumption that quantitative trait nucleotides (QTNs) are evenly distributed throughout the genome. The estimated QTNs are used to separate a mixed linear model into a computationally efficient fixed effect model (FEM) and a computationally expensive random effect model (REM), which are then used iteratively. To completely eliminate the computationally expensive REM, we replaced REM with FEM by using Bayesian information criteria. To eliminate the requirement that QTNs be evenly distributed throughout the genome, we replaced the bin method with linkage disequilibrium information. The new method is called Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). Both real and simulated data analyses demonstrated that BLINK improves statistical power compared to FarmCPU, in addition to remarkably reducing computing time. Now, a dataset with one million individuals and one-half million markers can be analyzed within three hours, instead of one week using FarmCPU.

Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms

Being a morning person is a behavioural indicator of a person’s underlying circadian rhythm. Using genome-wide data from 697,828 UK Biobank and 23andMe participants we increase the number of genetic loci associated with being a morning person from 24 to 351. Using data from 85,760 individuals with activity-monitor derived measures of sleep timing we find that the chronotype loci associate with sleep timing: the mean sleep timing of the 5% of individuals carrying the most morningness alleles is 25 min earlier than the 5% carrying the fewest. The loci are enriched for genes involved in circadian regulation, cAMP, glutamate and insulin signalling pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary. Using Mendelian Randomisation, we show that being a morning person is causally associated with better mental health but does not affect BMI or risk of Type 2 diabetes. This study offers insights into circadian biology and its links to disease in humans.

GWAS have previously found 24 genomic loci associated with chronotype, an individual’s preference for early or late sleep timing. Here, the authors identify 327 additional loci in a sample of 697,828 individuals and further explore the relationships of chronotype with metabolic and psychiatric diseases.

Characterizing mutagenic effects of recombination through a sequence-level genetic map

Genetic diversity arises from recombination and de novo mutation (DNM). Using a combination of microarray genotype and whole-genome sequence data on parent-child pairs, we identified 4,531,535 crossover recombinations and 200,435 DNMs. The resulting genetic map has a resolution of 682 base pairs. Crossovers exhibit a mutagenic effect, with overrepresentation of DNMs within 1 kilobase of crossovers in males and females. In females, a higher mutation rate is observed up to 40 kilobases from crossovers, particularly for complex crossovers, which increase with maternal age. We identified 35 loci associated with the recombination rate or the location of crossovers, demonstrating extensive genetic control of meiotic recombination, and our results highlight genes linked to the formation of the synaptonemal complex as determinants of crossovers.

A large-scale population study of early life factors influencing left-handedness

Hand preference is a conspicuous variation in human behaviour, with a worldwide proportion of around 90% of people preferring to use the right hand for many tasks, and 10% the left hand. We used the large cohort of the UK biobank (~500,000 participants) to study possible relations between early life factors and adult hand preference. The probability of being left-handed was affected by the year and location of birth, likely due to cultural effects. In addition, hand preference was affected by birthweight, being part of a multiple birth, season of birth, breastfeeding, and sex, with each effect remaining significant after accounting for all others. Analysis of genome-wide genotype data showed that left-handedness was very weakly heritable, but shared no genetic basis with birthweight. Although on average left-handers and right-handers differed for a number of early life factors, all together these factors had only a minimal predictive value for individual hand preference.

Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles.

Editorial note

This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

A guide to deep learning in healthcare

Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.

Genome-wide association study of offspring birth weight in 86 577 women identifies five novel loci and highlights maternal genetic effects that are independent of fetal genetics

Genome-wide association studies of birth weight have focused on fetal genetics, whereas relatively little is known about the role of maternal genetic variation. We aimed to identify maternal genetic variants associated with birth weight that could highlight potentially relevant maternal determinants of fetal growth. We meta-analysed data on up to 8.7 million SNPs in up to 86 577 women of European descent from the Early Growth Genetics (EGG) Consortium and the UK Biobank. We used structural equation modelling (SEM) and analyses of mother–child pairs to quantify the separate maternal and fetal genetic effects. Maternal SNPs at 10 loci (MTNR1B, HMGA2, SH2B3, KCNAB1, L3MBTL3, GCK, EBF1, TCF7L2, ACTL9, CYP3A7) were associated with offspring birth weight at P < 5 × 10⁻⁸. In SEM analyses, at least 7 of the 10 associations were consistent with effects of the maternal genotype acting via the intrauterine environment, rather than via effects of shared alleles with the fetus. Variants, or correlated proxies, at many of the loci had been previously associated with adult traits, including fasting glucose (MTNR1B, GCK and TCF7L2) and sex hormone levels (CYP3A7), and one (EBF1) with gestational duration. The identified associations indicate that genetic effects on maternal glucose, cytochrome P450 activity and gestational duration, and potentially on maternal blood pressure and immune function, are relevant for fetal growth. Further characterization of these associations in mechanistic and causal analyses will enhance understanding of the potentially modifiable maternal determinants of fetal growth, with the goal of reducing the morbidity and mortality associated with low and high birth weights.

A comparison of popular TDT-generalizations for family-based association analysis

The transmission disequilibrium test (TDT) is the gold standard for testing the association between a genetic variant and disease in samples consisting of affected individuals and their parents. In practice, more complex pedigree structures, that is siblings with no parents, or three-generational pedigrees with possibly missing genotypes, are common. There are several generalizations of the TDT that are suitable for use with arbitrary pedigree structures. We consider three such frequently used generalizations, family-based association test, pedigree disequilibrium test, and generalized disequilibrium test, that have accompanying software and compare them regarding validity and power in the single variant setting. We use simulations to study the effects of population admixture, populations whose genotypes are not in Hardy-Weinberg equilibrium (HWE), different pedigree structures, and the presence of linkage. Whereas our results show that some TDT generalizations can have a substantially increased Type 1 error, these tests are often used in substantive research without caveats about the validity of their Type 1 error. For the association analysis of rare variants in sequencing studies, region-based extensions of the TDT generalizations, that rely on the postulated robustness of the single variant tests, have been proposed. We discuss the implications of our results for these region-based extensions.

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.

A Perception on Genome-Wide Genetic Analysis of Metabolic Traits in Arab Populations

Despite dedicated nation-wide efforts to raise awareness against the harmful effects of fast-food consumption and sedentary lifestyle, the Arab population continues to struggle with an increased risk for metabolic disorders. Unlike the European population, the Arab population lacks well-established genetic risk determinants for metabolic disorders, and the transferability of established risk loci to this population has not been satisfactorily demonstrated. The most recent findings have identified over 240 genetic risk loci (with ~400 independent association signals) for type 2 diabetes, but thus far only 25 risk loci (ADAMTS9, ALX4, BCL11A, CDKAL1, CDKN2A/B, COL8A1, DUSP9, FTO, GCK, GNPDA2, HMG20A, HNF1A, HNF1B, HNF4A, IGF2BP2, JAZF1, KCNJ11, KCNQ1, MC4R, PPARγ, SLC30A8, TCF7L2, TFAP2B, TP53INP1, and WFS1) have been replicated in Arab populations. To our knowledge, large-scale population- or family-based association studies are non-existent in this region. Recently, we conducted genome-wide association studies on Arab individuals from Kuwait to delineate the genetic determinants for quantitative traits associated with anthropometry, lipid profile, insulin resistance, and blood pressure levels. Although these studies led to the identification of novel recessive variants, they failed to reproduce the established loci. However, they provided insights into the genetic architecture of the population, the applicability of genetic models based on recessive mode of inheritance, the presence of genetic signatures of inbreeding due to the practice of consanguinity, and the pleiotropic effects of rare disorders on complex metabolic disorders. This perspective presents analysis strategies and study designs for identifying genetic risk variants associated with diabetes and related traits in Arab populations.

Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism

Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.

Genome-Wide and Abdominal MRI Data Provide Evidence That a Genetically Determined Favorable Adiposity Phenotype Is Characterized by Lower Ectopic Liver Fat and Lower Risk of Type 2 Diabetes, Heart Disease, and Hypertension

Recent genetic studies have identified alleles associated with opposite effects on adiposity and risk of type 2 diabetes. We aimed to identify more of these variants and test the hypothesis that such favorable adiposity alleles are associated with higher subcutaneous fat and lower ectopic fat. We combined MRI data with genome-wide association studies of body fat percentage (%) and metabolic traits. We report 14 alleles, including 7 newly characterized alleles, associated with higher adiposity but a favorable metabolic profile. Consistent with previous studies, individuals carrying more favorable adiposity alleles had higher body fat % and higher BMI but lower risk of type 2 diabetes, heart disease, and hypertension. These individuals also had higher subcutaneous fat but lower liver fat and a lower visceral-to-subcutaneous adipose tissue ratio. Individual alleles associated with higher body fat % but lower liver fat and lower risk of type 2 diabetes included those in PPARG, GRB14, and IRS1, whereas the allele in ANKRD55 was paradoxically associated with higher visceral fat but lower risk of type 2 diabetes. Most identified favorable adiposity alleles are associated with higher subcutaneous and lower liver fat, a mechanism consistent with the beneficial effects of storing excess triglycerides in metabolically low-risk depots.

High-Throughput Sequencing in Respiratory, Critical Care, and Sleep Medicine Research. An Official American Thoracic Society Workshop Report

High-throughput, "next-generation" sequencing methods are now being broadly applied across all fields of biomedical research, including respiratory disease, critical care, and sleep medicine. Although there are numerous review articles and best practice guidelines related to sequencing methods and data analysis, there are fewer resources summarizing issues related to study design and interpretation, especially as applied to common, complex, nonmalignant diseases. To address these gaps, a single-day workshop was held at the American Thoracic Society meeting in May 2017, led by the American Thoracic Society Section on Genetics and Genomics. The aim of this workshop was to review the design, analysis, interpretation, and functional follow-up of high-throughput sequencing studies in respiratory, critical care, and sleep medicine research. This workshop brought together experts in multiple fields, including genetic epidemiology, biobanking, bioinformatics, and research ethics, along with physician-scientists with expertise in a range of relevant diseases. The workshop focused on application of DNA and RNA sequencing research in common chronic diseases and did not cover sequencing studies in lung cancer, monogenic diseases (e.g., cystic fibrosis), or microbiome sequencing. Participants reviewed and discussed study design, data analysis and presentation, interpretation, functional follow-up, and reporting of results. This report summarizes the main conclusions of the workshop, specifically addressing the application of these methods in respiratory, critical care, and sleep medicine research. This workshop report may serve as a resource for our research community as well as for journal editors and reviewers of sequencing-based manuscript submissions in our research field.

Genetic architecture of human thinness compared to severe obesity

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.

Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry

More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index (WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than the bottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.

Population structure in genetic studies: Confounding factors and mixed models

A genome-wide association study (GWAS) seeks to identify genetic variants that contribute to the development and progression of a specific disease. Over the past 10 years, new approaches using mixed models have emerged to mitigate the deleterious effects of population structure and relatedness in association studies. However, developing GWAS techniques to accurately test for association while correcting for population structure is a computational and statistical challenge. Using laboratory mouse strains as an example, our review characterizes the problem of population structure in association studies and describes how it can cause false positive associations. We then motivate mixed models in the context of unmodeled factors.

Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors

IMPORTANCE

Body fat distribution, usually measured using waist-to-hip ratio (WHR), is an important contributor to cardiometabolic disease independent of body mass index (BMI). Whether mechanisms that increase WHR via lower gluteofemoral (hip) or via higher abdominal (waist) fat distribution affect cardiometabolic risk is unknown.

OBJECTIVE

To identify genetic variants associated with higher WHR specifically via lower gluteofemoral or higher abdominal fat distribution and estimate their association with cardiometabolic risk.

DESIGN, SETTING, AND PARTICIPANTS

Genome-wide association studies (GWAS) for WHR combined data from the UK Biobank cohort and summary statistics from previous GWAS (data collection: 2006-2018). Specific polygenic scores for higher WHR via lower gluteofemoral or via higher abdominal fat distribution were derived using WHR-associated genetic variants showing specific association with hip or waist circumference. Associations of polygenic scores with outcomes were estimated in 3 population-based cohorts, a case-cohort study, and summary statistics from 6 GWAS (data collection: 1991-2018).

EXPOSURES

More than 2.4 million common genetic variants (GWAS); polygenic scores for higher WHR (follow-up analyses).

MAIN OUTCOMES AND MEASURES

BMI-adjusted WHR and unadjusted WHR (GWAS); compartmental fat mass measured by dual-energy x-ray absorptiometry, systolic and diastolic blood pressure, low-density lipoprotein cholesterol, triglycerides, fasting glucose, fasting insulin, type 2 diabetes, and coronary disease risk (follow-up analyses).

RESULTS

Among 452 302 UK Biobank participants of European ancestry, the mean (SD) age was 57 (8) years and the mean (SD) WHR was 0.87 (0.09). In genome-wide analyses, 202 independent genetic variants were associated with higher BMI-adjusted WHR (n = 660 648) and unadjusted WHR (n = 663 598). In dual-energy x-ray absorptiometry analyses (n = 18 330), the hip- and waist-specific polygenic scores for higher WHR were specifically associated with lower gluteofemoral and higher abdominal fat, respectively. In follow-up analyses (n = 636 607), both polygenic scores were associated with higher blood pressure and triglyceride levels and higher risk of diabetes (waist-specific score: odds ratio [OR], 1.57 [95% CI, 1.34-1.83], absolute risk increase per 1000 participant-years [ARI], 4.4 [95% CI, 2.7-6.5], P < .001; hip-specific score: OR, 2.54 [95% CI, 2.17-2.96], ARI, 12.0 [95% CI, 9.1-15.3], P < .001) and coronary disease (waist-specific score: OR, 1.60 [95% CI, 1.39-1.84], ARI, 2.3 [95% CI, 1.5-3.3], P < .001; hip-specific score: OR, 1.76 [95% CI, 1.53-2.02], ARI, 3.0 [95% CI, 2.1-4.0], P < .001), per 1-SD increase in BMI-adjusted WHR.

CONCLUSIONS AND RELEVANCE

Distinct genetic mechanisms may be linked to gluteofemoral and abdominal fat distribution that are the basis for the calculation of the WHR. These findings may improve risk assessment and treatment of diabetes and coronary disease.

Dissection of genetic variation and evidence for pleiotropy in male pattern baldness

Male pattern baldness (MPB) is a sex-limited, age-related, complex trait. We study MPB genetics in 205,327 European males from the UK Biobank. Here we show that MPB is strongly heritable and polygenic, with pedigree-heritability of 0.62 (SE = 0.03) estimated from close relatives, and SNP-heritability of 0.39 (SE = 0.01) from conventionally-unrelated males. We detect 624 near-independent genome-wide loci, contributing SNP-heritability of 0.25 (SE = 0.01), of which 26 X-chromosome loci explain 11.6%. Autosomal genetic variance is enriched for common variants and regions of lower linkage disequilibrium. We identify plausible genetic correlations between MPB and multiple sex-limited markers of earlier puberty, increased bone mineral density (r_g = 0.15) and pancreatic β-cell function (r_g = 0.12). Correlations with reproductive traits imply an effect on fitness, consistent with an estimated linear selection gradient of -0.018 per MPB standard deviation. Overall, we provide genetic insights into MPB: a phenotype of interest in its own right, with value as a model sex-limited, complex trait.

Male pattern baldness (MPB) is a polygenic trait that affects the majority of European men. Here, Yap et al. estimate heritability, partitioned by autosomes and the X-chromosome, of MPB in the UK Biobank cohort, perform GWAS for MPB and find genetic correlation with other sex-specific traits.

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.

GWAS identifies 14 loci for device-measured physical activity and sleep duration

Physical activity and sleep duration are established risk factors for many diseases, but their aetiology is poorly understood, partly due to relying on self-reported evidence. Here we report a genome-wide association study (GWAS) of device-measured physical activity and sleep duration in 91,105 UK Biobank participants, finding 14 significant loci (7 novel). These loci account for 0.06% of activity and 0.39% of sleep duration variation. Genome-wide estimates of ~ 15% phenotypic variation indicate high polygenicity. Heritability is higher in women than men for overall activity (23 vs. 20%, p = 1.5 × 10-4) and sedentary behaviours (18 vs. 15%, p = 9.7 × 10-4). Heritability partitioning, enrichment and pathway analyses indicate the central nervous system plays a role in activity behaviours. Two-sample Mendelian randomisation suggests that increased activity might causally lower diastolic blood pressure (beta mmHg/SD: -0.91, SE = 0.18, p = 8.2 × 10-7), and odds of hypertension (Odds ratio/SD: 0.84, SE = 0.03, p = 4.9 × 10-8). Our results advocate the value of physical activity for reducing blood pressure.

SumHer better estimates the SNP heritability of complex traits from summary statistics

We present SumHer, software for estimating confounding bias, SNP heritability, enrichments of heritability and genetic correlations using summary statistics from genome-wide association studies. The key difference between SumHer and the existing software LD Score Regression (LDSC) is that SumHer allows the user to specify the heritability model. We apply SumHer to results from 24 large-scale association studies (average sample size 121,000) using our recommended heritability model. We show that these studies tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci was under-reported by about a quarter. We also estimate enrichments for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further six categories with above threefold enrichment. By contrast, our analysis using SumHer finds that none of the categories have enrichment above twofold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

The critical needs and challenges for genetic architecture studies in Africa

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date. Additionally, African investigators need to be meaningfully included, as greater inclusivity and enhanced research capacity afford enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages, challenges, and examples of genetic architecture studies of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

A study of Kibbutzim in Israel reveals risk factors for cardiometabolic traits and subtle population structure

Genetic studies in isolated populations often increase power for identifying loci associated with complex diseases and traits. We present here the Kibbutzim Family Study (KFS), aimed at investigating the genetic basis of cardiometabolic traits in extended Israeli families characterized by long-term social stability and a homogeneous environment. Extensive information on cardiometabolic traits, as well as genome-wide genotypes, were collected on 901 individuals. We observed that most KFS participants were of Ashkenazi Jewish (AJ) genetic origin, confirmed a recent severe bottleneck in the AJ recent history, and detected a subtle within-AJ population structure. Focusing on genetic variants relatively common in the KFS but very rare in Europeans, we observed that AJ-enriched variants appear in cancer-related pathways more than expected by chance. We conducted an association study of the AJ-enriched variants against 16 cardiometabolic traits, and found seven loci (24 variants) to be significantly associated. The strongest association, which we also replicated in an independent study, was between a variant upstream of MSRA (frequency ≈1% in the KFS and nearly absent in Europeans) and weight (P = 3.6∙10-8). In conclusion, the KFS is a valuable resource for the study of the population genetics of Israel as well as the genetics of cardiometabolic traits.

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

OBJECTIVE

We sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk.

METHODS

We evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.

RESULTS

We observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with p < 4.4 × 10-4 in the previous AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio [OR] per SD = 1.40, p = 1.45 × 10-48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per SD = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1).

CONCLUSIONS

Genetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.

A linear mixed-model approach to study multivariate gene-environment interactions

Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.

Local PCA Shows How the Effect of Population Structure Differs Along the Genome

Population structure leads to systematic patterns in measures of mean relatedness between individuals in large genomic data sets, which are often discovered and visualized using dimension reduction techniques such as principal component analysis (PCA). Mean relatedness is an average of the relationships across locus-specific genealogical trees, which can be strongly affected on intermediate genomic scales by linked selection and other factors. We show how to use local PCA to describe this intermediate-scale heterogeneity in patterns of relatedness, and apply the method to genomic data from three species, finding in each that the effect of population structure can vary substantially across only a few megabases. In a global human data set, localized heterogeneity is likely explained by polymorphic chromosomal inversions. In a range-wide data set of Medicago truncatula, factors that produce heterogeneity are shared between chromosomes, correlate with local gene density, and may be caused by linked selection, such as background selection or local adaptation. In a data set of primarily African Drosophila melanogaster, large-scale heterogeneity across each chromosome arm is explained by known chromosomal inversions thought to be under recent selection and, after removing samples carrying inversions, remaining heterogeneity is correlated with recombination rate and gene density, again suggesting a role for linked selection. The visualization method provides a flexible new way to discover biological drivers of genetic variation, and its application to data highlights the strong effects that linked selection and chromosomal inversions can have on observed patterns of genetic variation.

Understanding the role of bitter taste perception in coffee, tea and alcohol consumption through Mendelian randomization

Consumption of coffee, tea and alcohol might be shaped by individual differences in bitter taste perception but inconsistent observational findings provide little insight regarding causality. We conducted Mendelian randomization analyses using genetic variants associated with the perception of bitter substances (rs1726866 for propylthiouracil [PROP], rs10772420 for quinine and rs2597979 for caffeine) to evaluate the intake of coffee, tea and alcohol among up to 438,870 UK Biobank participants. A standard deviation (SD) higher in genetically predicted bitterness of caffeine was associated with increased coffee intake (0.146 [95%CI: 0.103, 0.189] cups/day), whereas a SD higher in those of PROP and quinine was associated with decreased coffee intake (-0.021 [-0.031, -0.011] and -0.081 [-0.108, -0.054] cups/day respectively). Higher caffeine perception was also associated with increased risk of being a heavy (>4 cups/day) coffee drinker (OR 1.207 [1.126, 1.294]). Opposite pattern of associations was observed for tea possibly due to the inverse relationship between both beverages. Alcohol intake was only negatively associated with PROP perception (-0.141 [-1.88, -0.94] times/month per SD increase in PROP bitterness). Our results reveal that bitter perception is causally associated with intake of coffee, tea and alcohol, suggesting a role of bitter taste in the development of bitter beverage consumption.

Genome-wide associations for benign prostatic hyperplasia reveal a genetic correlation with serum levels of PSA

Benign prostatic hyperplasia and associated lower urinary tract symptoms (BPH/LUTS) are common conditions affecting the majority of elderly males. Here we report the results of a genome-wide association study of symptomatic BPH/LUTS in 20,621 patients and 280,541 controls of European ancestry, from Iceland and the UK. We discovered 23 genome-wide significant variants, located at 14 loci. There is little or no overlap between the BPH/LUTS variants and published prostate cancer risk variants. However, 15 of the variants reported here also associate with serum levels of prostate specific antigen (PSA) (at a Bonferroni corrected P < 0.0022). Furthermore, there is a strong genetic correlation, r_g = 0.77 (P = 2.6 × 10⁻¹¹), between PSA and BPH/LUTS, and one standard deviation increase in a polygenic risk score (PRS) for BPH/LUTS increases PSA levels by 12.9% (P = 1.6×10⁻⁵⁵). These results shed a light on the genetic background of BPH/LUTS and its substantial influence on PSA levels.

Elderly males are often affected by benign prostatic hyperplasia and associated lower urinary tract symptoms (BPH/LUTS), but their link to prostate cancer risk is not well defined. Here, a genome-wide association study of BPH/LUTS patients from Iceland and the UK found 23 significant variants at 14 loci, and 15 of these variants associate with prostate specific antigen, which is linked to prostate cancer risk.

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps

We expanded GWAS discovery for type 2 diabetes (T2D) by combining data from 898,130 European-descent individuals (9% cases), after imputation to high-density reference panels. With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).

Functional architecture of low-frequency variants highlights strength of negative selection across coding and non-coding annotations

Common variant heritability has been widely reported to be concentrated in variants within cell-type-specific non-coding functional annotations, but little is known about low-frequency variant functional architectures. We partitioned the heritability of both low-frequency (0.5%≤ minor allele frequency <5%) and common (minor allele frequency ≥5%) variants in 40 UK Biobank traits across a broad set of functional annotations. We determined that non-synonymous coding variants explain 17 ± 1% of low-frequency variant heritability ([Formula: see text]) versus 2.1 ± 0.2% of common variant heritability ([Formula: see text]). Cell-type-specific non-coding annotations that were significantly enriched for [Formula: see text] of corresponding traits were similarly enriched for [Formula: see text] for most traits, but more enriched for brain-related annotations and traits. For example, H3K4me3 marks in brain dorsolateral prefrontal cortex explain 57 ± 12% of [Formula: see text] versus 12 ± 2% of [Formula: see text] for neuroticism. Forward simulations confirmed that low-frequency variant enrichment depends on the mean selection coefficient of causal variants in the annotation, and can be used to predict effect size variance of causal rare variants (minor allele frequency <0.5%).

Is There Causal Relationship of Smoking and Alcohol Consumption with Bone Mineral Density? A Mendelian Randomization Study

Observational studies examining associations of smoking and alcohol consumption with bone mineral density (BMD) have generated inconsistent results and suffer from several methodological limitations. We aim to evaluate whether there are causal associations between smoking, alcohol consumption, and BMD using a Mendelian randomization (MR) design. Genetic variants associated with smoking status (n = 142), no. of cigarettes smoked per day (CPD) (n = 3), smoking initiation (n = 1), and alcohol consumption (n = 6) identified in published genome-wide association studies (GWAS) were used as instruments. Summary statistics data of 32735, 28498, 8143, and 445921 European subjects included in The GEnetic Factors for Osteoporosis Consortium or UK Biobank were used to generate associations of genetically predicted smoking or alcohol consumption with femoral neck (FN-BMD), lumbar spine (LS-BMD), forearm (FA-BMD), and heel BMD, respectively, by using the inverse-variance weighted method. The BMD was measured using either ultrasound (for heel) or Dual-energy X-ray Absorptiometry (for others). In our analyses, smoking status tended to be negatively associated with several types of BMD (heel BMD: β = - 0.053, p = 0.003; FN-BMD: β = - 0.139, p = 0.053; FA-BMD: β = - 0.264, p = 0.077), although the association with LS-BMD was null. Smoking initiation was significantly inversely associated with heel BMD (β = - 0.201, p = 3.60 × 10^-8). CPD was associated with a lower FN-BMD (β = - 0.014, p = 0.047) only. There was no clear association of genetically predicted alcohol consumption with BMD. Our study provided some evidence of a potential association between genetically predicted smoking and lower BMD, especially for heel BMD, but not for alcohol consumption. Considering the inconsistent findings with the different types of BMD and limitations of the current work, further studies are needed to better characterize the exact relationship between smoking, alcohol consumption, and BMD.

A homozygous loss-of-function mutation leading to CYBC1 deficiency causes chronic granulomatous disease

Mutations in genes encoding subunits of the phagocyte NADPH oxidase complex are recognized to cause chronic granulomatous disease (CGD), a severe primary immunodeficiency. Here we describe how deficiency of CYBC1, a previously uncharacterized protein in humans (C17orf62), leads to reduced expression of NADPH oxidase’s main subunit (gp91^phox) and results in CGD. Analyzing two brothers diagnosed with CGD we identify a homozygous loss-of-function mutation, p.Tyr2Ter, in CYBC1. Imputation of p.Tyr2Ter into 155K chip-genotyped Icelanders reveals six additional homozygotes, all with signs of CGD, manifesting as colitis, rare infections, or a severely impaired PMA-induced neutrophil oxidative burst. Homozygosity for p.Tyr2Ter consequently associates with inflammatory bowel disease (IBD) in Iceland (P = 8.3 × 10⁻⁸; OR = 67.6), as well as reduced height (P = 3.3 × 10⁻⁴; −8.5 cm). Overall, we find that CYBC1 deficiency results in CGD characterized by colitis and a distinct profile of infections indicative of macrophage dysfunction.

Mutations in genes encoding NAPDH oxidase subunits are known to be causative for the primary immunodeficiency chronic granulomatous disease (CGD). Here, the authors identify CYBC1 mutations in patients with CGD and show that CYBC1 is important for formation of the NADPH complex and respiratory burst.

Genome-wide association studies for corneal and refractive astigmatism in UK Biobank demonstrate a shared role for myopia susceptibility loci

Previous studies have suggested that naturally occurring genetic variation contributes to the risk of astigmatism. The purpose of this investigation was to identify genetic markers associated with corneal and refractive astigmatism in a large-scale European ancestry cohort (UK Biobank) who underwent keratometry and autorefraction at an assessment centre. Genome-wide association studies for corneal and refractive astigmatism were performed in individuals of European ancestry (N = 86,335 and 88,005 respectively), with the mean corneal astigmatism or refractive astigmatism in fellow eyes analysed as a quantitative trait (dependent variable). Genetic correlation between the two traits was calculated using LD Score regression. Gene-based and gene-set tests were carried out using MAGMA. Single marker-based association tests for corneal astigmatism identified four genome-wide significant loci (P < 5 × 10^-8) near the genes ZC3H11B (1q41), LINC00340 (6p22.3), HERC2/OCA2 (15q13.1) and NPLOC4/TSPAN10 (17q25.3). Three of these loci also demonstrated genome-wide significant association with refractive astigmatism: LINC00340, HERC2/OCA2 and NPLOC4/TSPAN10. The genetic correlation between corneal and refractive astigmatism was 0.85 (standard error = 0.068, P = 1.37 × 10^-35). Here, we have undertaken the largest genome-wide association studies for corneal and refractive astigmatism to date and identified four novel loci for corneal astigmatism, two of which were also novel loci for refractive astigmatism. These loci have previously demonstrated association with axial length (ZC3H11B), myopia (NPLOC4), spherical equivalent refractive error (LINC00340) and eye colour (HERC2). The shared role of these novel candidate genes for astigmatism lends further support to the shared genetic susceptibility of myopia and astigmatism.

Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.

The UK Biobank resource with deep phenotyping and genomic data

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.

Common genetic variants contribute to risk of rare severe neurodevelopmental disorders

There are thousands of rare human disorders that are caused by single deleterious, protein-coding genetic variants¹. However, patients with the same genetic defect can have different clinical presentations^2-4, and some individuals who carry known disease-causing variants can appear unaffected⁵. Here, to understand what explains these differences, we study a cohort of 6,987 children assessed by clinical geneticists to have severe neurodevelopmental disorders such as global developmental delay and autism, often in combination with abnormalities of other organ systems. Although the genetic causes of these neurodevelopmental disorders are expected to be almost entirely monogenic, we show that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome-wide common variant burden by showing, in an independent sample of 728 trios (comprising a child plus both parents) from the same cohort, that this burden is over-transmitted from parents to children with neurodevelopmental disorders. Our common-variant signal is significantly positively correlated with genetic predisposition to lower educational attainment, decreased intelligence and risk of schizophrenia. We found that common-variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, which suggests that common-variant risk affects patients both with and without a monogenic diagnosis. In addition, previously published common-variant scores for autism, height, birth weight and intracranial volume were all correlated with these traits within our cohort, which suggests that phenotypic expression in individuals with monogenic disorders is affected by the same variants as in the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in neurodevelopmental disorders that are typically considered to be monogenic.