Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

Mendelian randomization analysis using mixture models for robust and efficient estimation of causal effects

Mendelian randomization (MR) has emerged as a major tool for the investigation of causal relationship among traits, utilizing results from large-scale genome-wide association studies. Bias due to horizontal pleiotropy, however, remains a major concern. We propose a novel approach for robust and efficient MR analysis using large number of genetic instruments, based on a novel spike-detection algorithm under a normal-mixture model for underlying effect-size distributions. Simulations show that the new method, MRMix, provides nearly unbiased or/and less biased estimates of causal effects compared to alternative methods and can achieve higher efficiency than comparably robust estimators. Application of MRMix to publicly available datasets leads to notable observations, including identification of causal effects of BMI and age-at-menarche on the risk of breast cancer; no causal effect of HDL and triglycerides on the risk of coronary artery disease; a strong detrimental effect of BMI on the risk of major depressive disorder.

Mendelian randomization (MR) is a powerful and widely used method for causal inference leveraging genetic information. Here, the authors develop MRMix, an MR method using mixture models for more robust and efficient estimation of causal effects.

Polygenic prediction via Bayesian regression and continuous shrinkage priors

Polygenic risk scores (PRS) have shown promise in predicting human complex traits and diseases. Here, we present PRS-CS, a polygenic prediction method that infers posterior effect sizes of single nucleotide polymorphisms (SNPs) using genome-wide association summary statistics and an external linkage disequilibrium (LD) reference panel. PRS-CS utilizes a high-dimensional Bayesian regression framework, and is distinct from previous work by placing a continuous shrinkage (CS) prior on SNP effect sizes, which is robust to varying genetic architectures, provides substantial computational advantages, and enables multivariate modeling of local LD patterns. Simulation studies using data from the UK Biobank show that PRS-CS outperforms existing methods across a wide range of genetic architectures, especially when the training sample size is large. We apply PRS-CS to predict six common complex diseases and six quantitative traits in the Partners HealthCare Biobank, and further demonstrate the improvement of PRS-CS in prediction accuracy over alternative methods.

A Candidate-Gene Approach Identifies Novel Associations Between Common Variants in/Near Syndromic Obesity Genes and BMI in Pediatric and Adult European Populations

We hypothesized that monogenic syndromic obesity genes are also involved in the polygenic variation of BMI. Single-marker, tag single nucleotide polymorphism (tagSNP) and gene-based analysis were performed on common variants near 54 syndromic obesity genes. We used publicly available data from meta-analyses of European BMI genome-wide association studies conducted by the Genetic Investigation of ANthropometric Traits (GIANT) Consortium and the UK Biobank (UKB) (N = 681,275 adults). A total of 33 loci were identified, of which 19 of 33 (57.6%) were located at SNPs previously identified by the GIANT Consortium and UKB meta-analysis, 11 of 33 (33.3%) were located at novel SNPs, and 3 of 33 (9.1%) were novel genes identified with gene-based analysis. Both single-marker and tagSNP analyses mapped the previously identified 19 SNPs by the GIANT Consortium and UKB meta-analysis. Gene-based analysis confirmed 15 of 19 (78.9%) of the novel SNPs' associated genes. Of the 11 novel loci, 8 were identified with single-marker analysis and the remaining 3 were identified with tagSNP analysis. Gene-based analysis confirmed 4 of 11 (36.3%) of these loci. Meta-analysis with the Early Growth Genetics (EGG) Consortium (N = 35,668 children) was conducted post hoc for top SNPs, confirming 17 of 33 (51.5%) loci, of which 5 were novel. This study supports evidence for a continuum between rare monogenic syndromic and common polygenic forms of obesity.

Complex Trait Prediction from Genome Data: Contrasting EBV in Livestock to PRS in Humans: Genomic Prediction

In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population. In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent "related," and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease. We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.

From R.A. Fisher's 1918 Paper to GWAS a Century Later

The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher's 1918 paper in the context of how and why it is relevant in today's genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.

Why structure matters

Great care is needed when interpreting claims about the genetic basis of human variation based on data from genome-wide association studies.

Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies

Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.

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 genome-wide association analysis identifies 16 novel susceptibility loci for carpal tunnel syndrome

Carpal tunnel syndrome (CTS) is a common and disabling condition of the hand caused by entrapment of the median nerve at the level of the wrist. It is the commonest entrapment neuropathy, with estimates of prevalence ranging between 5-10%. Here, we undertake a genome-wide association study (GWAS) of an entrapment neuropathy, using 12,312 CTS cases and 389,344 controls identified in UK Biobank. We discover 16 susceptibility loci for CTS with p < 5 × 10^-8. We identify likely causal genes in the pathogenesis of CTS, including ADAMTS17, ADAMTS10 and EFEMP1, and using RNA sequencing demonstrate expression of these genes in surgically resected tenosynovium from CTS patients. We perform Mendelian randomisation and demonstrate a causal relationship between short stature and higher risk of CTS. We suggest that variants within genes implicated in growth and extracellular matrix architecture contribute to the genetic predisposition to CTS by altering the environment through which the median nerve transits.

The Sorghum QTL Atlas: a powerful tool for trait dissection, comparative genomics and crop improvement

We describe the development and application of the Sorghum QTL Atlas, a high-resolution, open-access research platform to facilitate candidate gene identification across three cereal species, sorghum, maize and rice. The mechanisms governing the genetic control of many quantitative traits are only poorly understood and have yet to be fully exploited. Over the last two decades, over a thousand QTL and GWAS studies have been published in the major cereal crops including sorghum, maize and rice. A large body of information has been generated on the genetic basis of quantitative traits, their genomic location, allelic effects and epistatic interactions. However, such QTL information has not been widely applied by cereal improvement programs and genetic researchers worldwide. In part this is due to the heterogeneous nature of QTL studies which leads QTL reliability variation from study to study. Using approaches to adjust the QTL confidence interval, this platform provides access to the most updated sorghum QTL information than any database available, spanning 23 years of research since 1995. The QTL database provides information on the predicted gene models underlying the QTL CI, across all sorghum genome assembly gene sets and maize and rice genome assemblies and also provides information on the diversity of the underlying genes and information on signatures of selection in sorghum. The resulting high-resolution, open-access research platform facilitates candidate gene identification across 3 cereal species, sorghum, maize and rice. Using a number of trait examples, we demonstrate the power and resolution of the resource to facilitate comparative genomics approaches to provide a bridge between genomics and applied breeding.

New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

Reduced lung function predicts mortality and is key to the diagnosis of chronic obstructive pulmonary disease (COPD). In a genome-wide association study in 400,102 individuals of European ancestry, we define 279 lung function signals, 139 of which are new. In combination, these variants strongly predict COPD in independent populations. Furthermore, the combined effect of these variants showed generalizability across smokers and never smokers, and across ancestral groups. We highlight biological pathways, known and potential drug targets for COPD and, in phenome-wide association studies, autoimmune-related and other pleiotropic effects of lung function-associated variants. This new genetic evidence has potential to improve future preventive and therapeutic strategies for COPD.

Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine

The global epidemic of obesity is a major public health problem today. Obesity increases the risk of many chronic diseases, such as type 2 diabetes, coronary heart disease, and certain types of cancer, and is associated with lower life expectancy. The body mass index (BMI), which is currently used to classify obesity, is only an imperfect measure of abnormal or excessive body fat accumulation. Studies have shown that waist circumference as a measure of fat distribution may improve disease prediction. More elaborate techniques such as magnetic resonance imaging are increasingly available to assess body fat distribution, but these measures are not readily available in routine clinical practice, and health-relevant cut-offs not yet been established. The measurement of biomarkers that reflect the underlying biological mechanisms for the increased disease risk may be an alternative approach to characterize the relevant obesity phenotype. The insulin/insulin-like growth factor (IGF) axis and chronic low-grade inflammation have been identified as major pathways. In addition, specific adipokines such as leptin, adiponectin and resistin have been related to obesity-associated health outcomes. This biomarker research, which is currently further developed with the application of high throughput methods, gives important insights in obesity-related disease etiology and pathophysiological pathways and may be used to better characterize obese persons at high risk of disease development and target disease-causing biomarkers in personalized prevention strategies.

Evaluation of GDF15 as a therapeutic target of cardiometabolic diseases in human: A Mendelian randomization study

Background

Growth differentiation factor 15 (GDF15) is a key regulator of body weight in animals by regulating food intake. Its receptor, glial cell-derived neurotrophic factor receptor alpha-like (GFRAL), was identified recently. Pre-clinical studies showed that it is a promising therapeutic target for cardiometabolic diseases and anorexia/cachexia. Although many pharmaceutical companies are developing drugs targeting GFRAL, whether the findings from animal studies can be extrapolated to man is unknown. Mendelian randomization (MR) is useful in investigating the relationship between risk factors and disease outcomes. We aimed to use a two-sample MR approach to evaluate the clinical usefulness of targeting GDF15 for cardiometabolic diseases.

Methods

Genetic instruments and summary statistics for MR analyses were obtained from a large genome-wide association study (GWAS) of GDF15 and cardiometabolic outcomes (n = 27,394 to 644,875), including body mass index, waist-hip ratio, waist circumference, whole-body lean mass, fat percentage, Type 2 Diabetes, fasting glucose, glycated haemoglobin, fasting insulin, LDL-cholesterol, HDL-cholesterol, total cholesterol, triglycerides, coronary artery disease, and estimated BMD (eBMD). Conventional inverse variance weighted (IVW) method was adopted to obtain the causal estimates of GDF-15 with different outcomes; weighted median and MR-egger were used for sensitivity analyses.

Findings

There was null association between GDF15 levels and anthropometric outcomes. One SD increase in genetically-determined GDF15 was significantly associated with reduced HDL-C (beta: -0.048SD; SE: 0.014; P = .001) but the result was not significant in sensitivity analyses. A consistent significant causal association was observed between GDF15 and eBMD in IVW (beta: 0.026 SD; SE: 0.005; P < .001) and subsequent sensitivity analyses.

Interpretation

This study sheds lights on the potential of drugs targeting the GDF15/GFRAL axis. It suggested that the effect of targeting GDF15/GFRAL axis for weight control in human may be different from the effects observed in animal studies. GDF15 treatment may improve BMD in humans.

Fund

No specific funding was received for this study.

Variation in the repulsive guidance molecule family in human populations

Repulsive guidance molecules, RGMA, RGMB, and RGMC, are related proteins discovered independently through different experimental paradigms. They are encoded by single copy genes in mammalian and other vertebrate genomes, and are ~50% identical in amino acid sequence. The importance of RGM actions in human physiology has not been realized, as most research has focused on non-human models, although mutations in RGMC are the cause of the severe iron storage disorder, juvenile hemochromatosis. Here I show that repositories of human genomic and population genetic data can be used as starting points for discovery and for developing new testable hypotheses about each of these paralogs in human biology and disease susceptibility. Information was extracted, aggregated, and analyzed from the Ensembl and UCSC Genome Browsers, the Exome Aggregation Consortium, the Genotype-Tissue Expression project portal, the cBio portal for Cancer Genomics, and the National Cancer Institute Genomic Data Commons data site. Results identify extensive variation in gene expression patterns, substantial alternative RNA splicing, and possible missense alterations and other modifications in the coding regions of each of the three genes, with many putative mutations being detected in individuals with different types of cancers. Moreover, selected amino acid substitutions are highly prevalent in the world population, with minor allele frequencies of up to 37% for RGMA and up to 8% for RGMB. These results indicate that protein sequence variation is common in the human RGM family, and raises the possibility that individual variants will have a significant population impact on human physiology and/or disease predisposition.

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.

Dissecting the genetic relationship between cardiovascular risk factors and Alzheimer's disease

Cardiovascular (CV)- and lifestyle-associated risk factors (RFs) are increasingly recognized as important for Alzheimer's disease (AD) pathogenesis. Beyond the ε4 allele of apolipoprotein E (APOE), comparatively little is known about whether CV-associated genes also increase risk for AD. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) jointly associated with AD and one or more CV-associated RFs, namely body mass index (BMI), type 2 diabetes (T2D), coronary artery disease (CAD), waist hip ratio (WHR), total cholesterol (TC), triglycerides (TG), low-density (LDL) and high-density lipoprotein (HDL). In fold enrichment plots, we observed robust genetic enrichment in AD as a function of plasma lipids (TG, TC, LDL, and HDL); we found minimal AD genetic enrichment conditional on BMI, T2D, CAD, and WHR. Beyond APOE, at conjunction FDR < 0.05 we identified 90 SNPs on 19 different chromosomes that were jointly associated with AD and CV-associated outcomes. In meta-analyses across three independent cohorts, we found four novel loci within MBLAC1 (chromosome 7, meta-p = 1.44 × 10^-9), MINK1 (chromosome 17, meta-p = 1.98 × 10^-7) and two chromosome 11 SNPs within the MTCH2/SPI1 region (closest gene = DDB2, meta-p = 7.01 × 10^-7 and closest gene = MYBPC3, meta-p = 5.62 × 10^-8). In a large 'AD-by-proxy' cohort from the UK Biobank, we replicated three of the four novel AD/CV pleiotropic SNPs, namely variants within MINK1, MBLAC1, and DDB2. Expression of MBLAC1, SPI1, MINK1 and DDB2 was differentially altered within postmortem AD brains. Beyond APOE, we show that the polygenic component of AD is enriched for lipid-associated RFs. We pinpoint a subset of cardiovascular-associated genes that strongly increase the risk for AD. Our collective findings support a disease model in which cardiovascular biology is integral to the development of clinical AD in a subset of individuals.

Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains

Most variants associated with complex phenotypes in genome-wide association studies (GWAS) do not directly index coding changes affecting protein structure. Instead they are hypothesized to influence gene regulation, with common variants associated with disease being enriched in regulatory domains including enhancers and regions of open chromatin. There is interest, therefore, in using epigenomic annotation data to identify the specific regulatory mechanisms involved and prioritize risk variants. We quantified lysine H3K27 acetylation (H3K27ac) - a robust mark of active enhancers and promoters that is strongly correlated with gene expression and transcription factor binding - across the genome in entorhinal cortex samples using chromatin immunoprecipitation followed by highly parallel sequencing (ChIP-seq). H3K27ac peaks were called using high quality reads combined across all samples and formed the basis of partitioned heritability analysis using LD score regression along with publicly-available GWAS results for seven psychiatric and neurodegenerative traits. Heritability for all seven brain traits was significantly enriched in these H3K27ac peaks (enrichment ranging from 1.09-2.13) compared to regions of the genome containing other active regulatory and functional elements across multiple cell types and tissues. The strongest enrichments were for amyotrophic lateral sclerosis (ALS) (enrichment = 2.19; 95% CI = 2.12-2.27), autism (enrichment = 2.11; 95% CI = 2.05-2.16) and major depressive disorder (enrichment = 2.04; 95% CI = 1.92-2.16). Much lower enrichments were observed for 14 non-brain disorders, although we identified enrichment in cortical H3K27ac domains for body mass index (enrichment = 1.16; 95% CI = 1.13-1.19), ever smoked (enrichment = 2.07; 95% CI = 2.04-2.10), HDL (enrichment = 1.53; 95% CI = 1.45-1.62) and trigylcerides (enrichment = 1.33; 95% CI = 1.24-1.42). These results indicate that risk alleles for brain disorders are preferentially located in regions of regulatory/enhancer function in the cortex, further supporting the hypothesis that genetic variants for these phenotypes influence gene regulation in the brain.

Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered.

Mendelian randomization provides support for obesity as a risk factor for meningioma

Little is known about the causes of meningioma. Obesity and obesity-related traits have been reported in several epidemiological observational studies to be risk factors for meningioma. We performed an analysis of genetic variants associated with obesity-related traits to assess the relationship with meningioma risk using Mendelian randomization (MR), an approach unaffected by biases from temporal variability and reverse causation that might have affected earlier investigations. We considered 11 obesity-related traits, identified genetic instruments for these factors, and assessed their association with meningioma risk using data from a genome-wide association study comprising 1,606 meningioma patients and 9,823 controls. To evaluate the causal relationship between the obesity-related traits and meningioma risk, we consider the estimated odds ratio (OR) of meningioma for each genetic instrument. We identified positive associations between body mass index (odds ratio [OR_SD] = 1.27, 95% confidence interval [CI] = 1.03–1.56, P = 0.028) and body fat percentage (OR_SD = 1.28, 95% CI = 1.01–1.63, P = 0.042) with meningioma risk, albeit non-significant after correction for multiple testing. Associations for basal metabolic rate, diastolic blood pressure, fasting glucose, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, systolic blood pressure, total cholesterol, triglycerides and waist circumference with risk of meningioma were non-significant. Our analysis provides additional support for obesity being associated with an increased risk of meningioma.

Conducting a Reproducible Mendelian Randomization Analysis Using the R Analytic Statistical Environment

Mendelian randomization (MR) is defined as the utilization of genetic variants as instrumental variables to assess the causal relationship between an exposure and an outcome. By leveraging genetic polymorphisms as proxy for an exposure, the causal effect of an exposure on an outcome can be assessed while addressing susceptibility to biases prone to conventional observational studies, including confounding and reverse causation, where the outcome causes the exposure. Analogous to a randomized controlled trial where patients are randomly assigned to subgroups based on different treatments, in an MR analysis, the random allocation of alleles during meiosis from parent to offspring assigns individuals to different subgroups based on genetic variants. Recent methods use summary statistics from genome-wide association studies to perform MR, bypassing the need for individual-level data. Here, we provide a straightforward protocol for using summary-level data to perform MR and provide guidance for utilizing available software. © 2019 by John Wiley & Sons, Inc.

Evidence of a causal relationship between body mass index and psoriasis: A mendelian randomization study

BACKGROUND

Psoriasis is a common inflammatory skin disease that has been reported to be associated with obesity. We aimed to investigate a possible causal relationship between body mass index (BMI) and psoriasis.

METHODS AND FINDINGS

Following a review of published epidemiological evidence of the association between obesity and psoriasis, mendelian randomization (MR) was used to test for a causal relationship with BMI. We used a genetic instrument comprising 97 single-nucleotide polymorphisms (SNPs) associated with BMI as a proxy for BMI (expected to be much less confounded than measured BMI). One-sample MR was conducted using individual-level data (396,495 individuals) from the UK Biobank and the Nord-Trøndelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (356,926 individuals) from published BMI and psoriasis genome-wide association studies (GWASs). The one-sample and two-sample MR estimates were meta-analysed using a fixed-effect model. To test for a potential reverse causal effect, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis were used to test whether genetic risk for this skin disease has a causal effect on BMI. Published observational data showed an association of higher BMI with psoriasis. A mean difference in BMI of 1.26 kg/m2 (95% CI 1.02-1.51) between psoriasis cases and controls was observed in adults, while a 1.55 kg/m2 mean difference (95% CI 1.13-1.98) was observed in children. The observational association was confirmed in UK Biobank and HUNT data sets. Overall, a 1 kg/m2 increase in BMI was associated with 4% higher odds of psoriasis (meta-analysis odds ratio [OR] = 1.04; 95% CI 1.03-1.04; P = 1.73 × 10(-60)). MR analyses provided evidence that higher BMI causally increases the odds of psoriasis (by 9% per 1 unit increase in BMI; OR = 1.09 (1.06-1.12) per 1 kg/m2; P = 4.67 × 10(-9)). In contrast, MR estimates gave little support to a possible causal effect of psoriasis genetic risk on BMI (0.004 kg/m2 change in BMI per doubling odds of psoriasis (-0.003 to 0.011). Limitations of our study include possible misreporting of psoriasis by patients, as well as potential misdiagnosis by clinicians. In addition, there is also limited ethnic variation in the cohorts studied.

CONCLUSIONS

Our study, using genetic variants as instrumental variables for BMI, provides evidence that higher BMI leads to a higher risk of psoriasis. This supports the prioritization of therapies and lifestyle interventions aimed at controlling weight for the prevention or treatment of this common skin disease. Mechanistic studies are required to improve understanding of this relationship.

Genetics of Obesity

Obesity is caused by an imbalance between energy intake and output, influenced by numerous environmental, biological, and genetic factors. Only a minority of people with obesity have a genetic defect that is the main cause of their obesity. A key symptom for most of these disorders is early-onset obesity and hyperphagia. For some genetic obesity disorders, the hyperphagia is the main characteristic, often caused by disruptions of the leptin-melanocortin pathway, the central pathway that regulates the body's satiety and energy balance. For other disorders, obesity is part of a distinct combination of other clinical features such as intellectual disability, dysmorphic facial features, or organ abnormalities. This chapter focuses on genetic obesity disorders and also summarizes the present knowledge on the genetics of the more common polygenic/multifactorial obesity.

Gene expression variation and parental allele inheritance in a Xiphophorus interspecies hybridization model

Understanding the genetic mechanisms underlying segregation of phenotypic variation through successive generations is important for understanding physiological changes and disease risk. Tracing the etiology of variation in gene expression enables identification of genetic interactions, and may uncover molecular mechanisms leading to the phenotypic expression of a trait, especially when utilizing model organisms that have well-defined genetic lineages. There are a plethora of studies that describe relationships between gene expression and genotype, however, the idea that global variations in gene expression are also controlled by genotype remains novel. Despite the identification of loci that control gene expression variation, the global understanding of how genome constitution affects trait variability is unknown. To study this question, we utilized Xiphophorus fish of different, but tractable genetic backgrounds (inbred, F₁ interspecies hybrids, and backcross hybrid progeny), and measured each individual’s gene expression concurrent with the degrees of inter-individual expression variation. We found, (a) F₁ interspecies hybrids exhibited less variability than inbred animals, indicting gene expression variation is not affected by the fraction of heterozygous loci within an individual genome, and (b), that mixing genotypes in backcross populations led to higher levels of gene expression variability, supporting the idea that expression variability is caused by heterogeneity of genotypes of cis or trans loci. In conclusion, heterogeneity of genotype, introduced by inheritance of different alleles, accounts for the largest effects on global phenotypical variability.

Phenotypical variability is a multi-factorial phenomenon. Although it has been shown that inheriting certain gene is associated with lower phenotypical variability, how genome complexity affect phenotypical variability is still unclear. To study this question, we used inbred Xiphophorus fish, backcross interspecies hybrids, and F₁ interspecies hybrids between select Xiphophorus species to model genetic composition with minimum, medium, and maximum heterozygosity respectively, and measured their global gene expression variability. We found gene expression variation is not affected by the percentage of heterozygous loci in individual genome, but instead related to heterogeneity of genotype at local or remote loci.

Imprint of assortative mating on the human genome

Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1-5 and has evolutionary consequences6-8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes.

Invited Commentary: Detecting Individual and Global Horizontal Pleiotropy in Mendelian Randomization-A Job for the Humble Heterogeneity Statistic?

Mendelian randomization (MR) is gaining in recognition and popularity as a method for strengthening causal inference in epidemiology by utilizing genetic variants as instrumental variables. Concurrently with the explosion in empirical MR studies, there has been the steady production of new approaches for MR analysis. The recently proposed "global and individual tests for direct effects" (GLIDE) approach fits into a family of methods that aim to detect horizontal pleiotropy-at the individual single nucleotide polymorphism level and at the global level-and to adjust the analysis by removing outlying single nucleotide polymorphisms. In this commentary, we explain how existing methods can (and indeed are) being used to detect pleiotropy at the individual and global levels, although not explicitly using this terminology. By doing so, we show that the true comparator for GLIDE is not MR-Egger regression (as Dai et al., the authors of the accompanying article (Am J Epidemiol. 2018;187(12):2672-2680), claim) but rather the humble heterogeneity statistic.

A reference haplotype panel for genome-wide imputation of short tandem repeats

Short tandem repeats (STRs) are involved in dozens of Mendelian disorders and have been implicated in complex traits. However, genotyping arrays used in genome-wide association studies focus on single nucleotide polymorphisms (SNPs) and do not readily allow identification of STR associations. We leverage next-generation sequencing (NGS) from 479 families to create a SNP + STR reference haplotype panel. Our panel enables imputing STR genotypes into SNP array data when NGS is not available for directly genotyping STRs. Imputed genotypes achieve mean concordance of 97% with observed genotypes in an external dataset compared to 71% expected under a naive model. Performance varies widely across STRs, with near perfect concordance at bi-allelic STRs vs. 70% at highly polymorphic repeats. Imputation increases power over individual SNPs to detect STR associations with gene expression. Imputing STRs into existing SNP datasets will enable the first large-scale STR association studies across a range of complex traits.

Genetik der allgemeinen kognitiven Fähigkeit

Intelligenz ist eines der bestuntersuchten Konstrukte der empirischen Verhaltenswissenschaften und stellt eine allgemeine geistige Kapazität dar, die unter anderem die Fähigkeit zum schlussfolgernden Denken, zum Lösen neuartiger Probleme, zum abstrakten Denken sowie zum schnellen Lernen umfasst. Diese kognitiven Fähigkeiten spielen eine große Rolle in der Erklärung und Vorhersage individueller Unterschiede in zentralen Bereichen des gesellschaftlichen Lebens, wie Schul- und Bildungserfolg, Berufserfolg, sozioökonomischer Status und Gesundheitsverhalten. Verhaltensgenetische Studien zeigen konsistent, dass genetische Einflüsse einen substanziellen Beitrag zur Erklärung individueller Unterschiede leisten, die über 60 % der Intelligenzunterschiede im Erwachsenenalter erklären. In den letzten Jahren konnten in großen genomweiten Assoziationsstudien mit häufigen genetischen Varianten Hunderte mit Intelligenz assoziierte Loci identifiziert werden sowie über 1300 assoziierte Gene mit differentieller Expression überwiegend im Gehirn. Mehrere Signalwege waren angereichert, vor allen für Neurogenese, Regulation der Entwicklung des Nervensystems sowie der synaptischen Struktur und Aktivität. Die Mehrzahl der assoziierten Loci betraf regulatorische Regionen und interessanterweise lag die Hälfte intronisch. Von den über 1300 Genen überlappen nur 9,2 % mit solchen, die mit monogenen neurokognitiven Störungen assoziiert sind. Insgesamt bestätigen die Befunde ein polygenes Modell Tausender additiver Faktoren, wobei die einzelnen Loci eine sehr geringe Effektstärke aufweisen. Insgesamt erklären die jetzigen Befunde ca. 10 % der Gesamtvarianz des Merkmals. Diese Ergebnisse sind ein wichtiger Ausgangspunkt für zukünftige Forschung sowohl in der Genetik als auch den Verhaltenswissenschaften.

Genetic Variants Associated with Circulating Fibroblast Growth Factor 23

BACKGROUND

Fibroblast growth factor 23 (FGF23), a bone-derived hormone that regulates phosphorus and vitamin D metabolism, contributes to the pathogenesis of mineral and bone disorders in CKD and is an emerging cardiovascular risk factor. Central elements of FGF23 regulation remain incompletely understood; genetic variation may help explain interindividual differences.

METHODS

We performed a meta-analysis of genome-wide association studies of circulating FGF23 concentrations among 16,624 participants of European ancestry from seven cohort studies, excluding participants with eGFR<30 ml/min per 1.73 m2 to focus on FGF23 under normal conditions. We evaluated the association of single-nucleotide polymorphisms (SNPs) with natural log-transformed FGF23 concentration, adjusted for age, sex, study site, and principal components of ancestry. A second model additionally adjusted for BMI and eGFR.

RESULTS

We discovered 154 SNPs from five independent regions associated with FGF23 concentration. The SNP with the strongest association, rs17216707 (P=3.0×10-24), lies upstream of CYP24A1, which encodes the primary catabolic enzyme for 1,25-dihydroxyvitamin D and 25-hydroxyvitamin D. Each additional copy of the T allele at this locus is associated with 5% higher FGF23 concentration. Another locus strongly associated with variations in FGF23 concentration is rs11741640, within RGS14 and upstream of SLC34A1 (a gene involved in renal phosphate transport). Additional adjustment for BMI and eGFR did not materially alter the magnitude of these associations. Another top locus (within ABO, the ABO blood group transferase gene) was no longer statistically significant at the genome-wide level.

CONCLUSIONS

Common genetic variants located near genes involved in vitamin D metabolism and renal phosphate transport are associated with differences in circulating FGF23 concentrations.

Identification of Novel Candidate Markers of Type 2 Diabetes and Obesity in Russia by Exome Sequencing with a Limited Sample Size

Type 2 diabetes (T2D) and obesity are common chronic disorders with multifactorial etiology. In our study, we performed an exome sequencing analysis of 110 patients of Russian ethnicity together with a multi-perspective approach based on biologically meaningful filtering criteria to detect novel candidate variants and loci for T2D and obesity. We have identified several known single nucleotide polymorphisms (SNPs) as markers for obesity (rs11960429), T2D (rs9379084, rs1126930), and body mass index (BMI) (rs11553746, rs1956549 and rs7195386) (p < 0.05). We show that a method based on scoring of case-specific variants together with selection of protein-altering variants can allow for the interrogation of novel and known candidate markers of T2D and obesity in small samples. Using this method, we identified rs328 in LPL (p = 0.023), rs11863726 in HBQ1 (p = 8 × 10⁻⁵), rs112984085 in VAV3 (p = 4.8 × 10⁻⁴) for T2D and obesity, rs6271 in DBH (p = 0.043), rs62618693 in QSER1 (p = 0.021), rs61758785 in RAD51B (p = 1.7 × 10⁻⁴), rs34042554 in PCDHA1 (p = 1 × 10⁻⁴), and rs144183813 in PLEKHA5 (p = 1.7 × 10⁻⁴) for obesity; and rs9379084 in RREB1 (p = 0.042), rs2233984 in C6orf15 (p = 0.030), rs61737764 in ITGB6 (p = 0.035), rs17801742 in COL2A1 (p = 8.5 × 10⁻⁵), and rs685523 in ADAMTS13 (p = 1 × 10⁻⁶) for T2D as important susceptibility loci in Russian population. Our results demonstrate the effectiveness of whole exome sequencing (WES) technologies for searching for novel markers of multifactorial diseases in cohorts of limited size in poorly studied populations.

Glucose and Insulin-Related Traits, Type 2 Diabetes and Risk of Schizophrenia: A Mendelian Randomization Study

BACKGROUND

The link between schizophrenia and diabetes mellitus is well established by observational studies; however, the cause-effect relationship remains unclear.

METHODS

Here, we conducted Mendelian randomization analyses to assess a causal relationship of the genetic variants related to elevated fasting glucose levels, hemoglobin A1c (HbA1c), fasting insulin levels, and type 2 diabetes with the risk of schizophrenia. The analyses were performed using summary statistics obtained for the variants identified from the genome-wide association meta-analyses of fasting glucose levels (up to 133,010 individuals), HbA1c (up to 153,377 individuals), fasting insulin levels (up to 108,557 individuals), type 2 diabetes (up to 659,316 individuals), and schizophrenia (up to 108,341 individuals). The association between each variant and schizophrenia was weighted by its association with each studied condition, and estimates were combined using an inverse-variance weighted meta-analysis.

FINDINGS

Using information from thirteen variants related to fasting insulin levels, the causal effect of fasting insulin levels increases (per 1-SD) on the risk of schizophrenia was estimated at an odds ratio (OR) of 2·33 (p = 0·001), which is consistent with findings from the observational studies. The fasting glucose associated single nucleotide polymorphisms (SNPs) had no effect on the risk of schizophrenia in Europeans and East Asians (p > 0·05). Nonsignificant effects on the risk of schizophrenia was observed with raised HbA1c and type 2 diabetes, and consistent estimates were obtained across different populations.

INTERPRETATION

Our results suggest a causal role of elevated fasting insulin levels in schizophrenia pathogenesis.

Genetic nature or genetic nurture? Introducing social genetic parameters to quantify bias in polygenic score analyses

Results from a genome-wide association study (GWAS) can be used to generate a polygenic score (PGS), an individual-level measure summarizing identified genetic influence on a trait dispersed across the genome. For complex, behavioral traits, the association between an individual's PGS and their phenotype may contain bias (from geographic, ancestral, and/or socioeconomic confounding) alongside the causal effect of the individual's genes. We formalize the introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes, known as genetic nurture. GWAS do not discriminate between the various pathways through which genes become associated with outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects. We construct a theoretical model for genetic effects and show that the presence of genetic nurture biases PGS coefficients from both naïve OLS (between-family) and family fixed effects (within-family) regressions. This bias is in opposite directions; while naïve OLS estimates are biased away from zero, family fixed effects estimates are biased toward zero. We quantify this bias using two novel parameters: (1) the genetic correlation between the direct and nurture effects and (2) the ratio of the SNP heritabilities for the direct and nurture effects.