scholarly journals An Atlas of Genetic Correlations across Human Diseases and Traits

2015 ◽  
Author(s):  
Brendan Bulik-Sullivan ◽  
Hilary K Finucane ◽  
Verneri Anttila ◽  
Alexander Gusev ◽  
Felix R Day ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Educational Attainment ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Significant Snps ◽  
Modeling Framework ◽  
Genome Wide ◽  
Meta Analyses

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia/ body mass index and associations between educational attainment and several diseases. These results highlight the power of a polygenic modeling framework, since there currently are no genome-wide significant SNPs for anorexia nervosa and only three for educational attainment.

scholarly journals LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

2016 ◽  
Author(s):  
Jie Zheng ◽  
A. Mesut Erzurumluoglu ◽  
Benjamin L. Elsworth ◽  
Laurence Howe ◽  
Philip C. Haycock ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Web Interface ◽  
The Public ◽  
Genome Wide ◽  
Level Data ◽  
Centralized Database

AbstractMotivationLD score regression is a reliable and efficient method of using genome-wide association study (GWAS) summary-level results data to estimate the SNP heritability of complex traits and diseases, partition this heritability into functional categories, and estimate the genetic correlation between different phenotypes. Because the method relies on summary level results data, LD score regression is computationally tractable even for very large sample sizes. However, publicly available GWAS summary-level data are typically stored in different databases and have different formats, making it difficult to apply LD score regression to estimate genetic correlations across many different traits simultaneously.ResultsIn this manuscript, we describe LD Hub – a centralized database of summary-level GWAS results for 177 diseases/traits from different publicly available resources/consortia and a web interface that automates the LD score regression analysis pipeline. To demonstrate functionality and validate our software, we replicated previously reported LD score regression analyses of 49 traits/diseases using LD Hub; and estimated SNP heritability and the genetic correlation across the different phenotypes. We also present new results obtained by uploading a recent atopic dermatitis GWAS meta-analysis to examine the genetic correlation between the condition and other potentially related traits. In response to the growing availability of publicly accessible GWAS summary-level results data, our database and the accompanying web interface will ensure maximal uptake of the LD score regression methodology, provide a useful database for the public dissemination of GWAS results, and provide a method for easily screening hundreds of traits for overlapping genetic aetiologies.Availability and implementationThe web interface and instructions for using LD Hub are available at http://ldsc.broadinstitute.org/

scholarly journals Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Vasiliki Lagou ◽  
◽  
Reedik Mägi ◽  
Jouke- Jan Hottenga ◽  
Harald Grallert ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
Fasting Glucose ◽  
Genetic Correlations ◽  
Epidemiological Studies ◽  
Genetic Effects ◽  
Fasting Insulin ◽  
Sex Dimorphism ◽  
Waist To Hip Ratio ◽  
Genome Wide ◽  
Meta Analyses

AbstractDifferences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

scholarly journals Genome-wide association study identifies 48 common genetic variants associated with handedness

2019 ◽  
Author(s):  
Gabriel Cuellar Partida ◽  
Joyce Y Tung ◽  
Nicholas Eriksson ◽  
Eva Albrecht ◽  
Fazil Aliev ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Variants ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Left Handedness ◽  
Left Handed ◽  
Genome Wide ◽  
Common Genetic Variants

AbstractHandedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% – 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% – 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 – 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin Verweij ◽  
Michel G Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

scholarly journals Geographic Confounding in Genome-Wide Association Studies

2021 ◽  
Author(s):  
Abdel Abdellaoui ◽  
Karin J.H. Verweij ◽  
Michel G. Nivard
Keyword(s):  
Educational Attainment ◽  
Social Stratification ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Geographic Region ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Gene Environment ◽  
Genome Wide

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N=69,772-271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

scholarly journals Large-scale genome-wide association study of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits.

2021 ◽  
Author(s):  
Sebastian May-Wilson ◽  
Nana Matoba ◽  
Kaitlin H Wade ◽  
Jouke-Jan Hottenga ◽  
Maria Pina Concas ◽  
...  
Keyword(s):  
Association Study ◽  
Genetic Correlation ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Taste Receptor ◽  
Genome Wide Association ◽  
Genetic Determinants ◽  
Genome Wide ◽  
Food Liking

Variable preferences for different foods are among the main determinants of their intake and are influenced by many factors, including genetics. Despite considerable twins' heritability, studies aimed at uncovering food-liking genetics have focused mostly on taste receptors. Here, we present the first results of a large-scale genome-wide association study of food liking conducted on 161,625 participants from UK Biobank. Liking was assessed over 139 specific foods using a 9-point hedonic scale. After performing GWAS, we used genetic correlations coupled with structural equation modelling to create a multi-level hierarchical map of food liking. We identified three main dimensions: high caloric foods defined as "Highly palatable", strong-tasting foods ranging from alcohol to pungent vegetables, defined as "Learned" and finally "Low caloric" foods such as fruit and vegetables. The "Highly palatable" dimension was genetically uncorrelated from the other two, suggesting that two independent processes underlie liking high reward foods and the Learned/Low caloric ones. Genetic correlation analysis with the corresponding food consumption traits revealed a high correlation, while liking showed twice the heritability compared to consumption. For example, fresh fruit liking and consumption showed a genetic correlation of 0.7 with heritabilities of 0.1 and 0.05, respectively. GWAS analysis identified 1401 significant food-liking associations located in 173 genomic loci, with only 11 near taste or olfactory receptors. Genetic correlation with morphological and functional brain data (33,224 UKB participants) uncovers associations of the three food-liking dimensions with non-overlapping, distinct brain areas and networks, suggestive of separate neural mechanisms underlying the liking dimensions. In conclusion, we created a comprehensive and data-driven map of the genetic determinants and associated neurophysiological factors of food liking beyond taste receptor genes.

scholarly journals Genetic architecture of early childhood growth phenotypes gives insights into their link with later obesity

2017 ◽  
Author(s):  
N. Maneka G. De Silva ◽  
Sylvain Sebert ◽  
Alexessander Couto Alves ◽  
Ulla Sovio ◽  
Shikta Das ◽  
...  
Keyword(s):  
Early Childhood ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Subcutaneous Fat ◽  
Peak Height ◽  
Early Growth ◽  
Growth Patterns ◽  
Childhood Growth ◽  
Genome Wide ◽  
Meta Analyses

AbstractEarly childhood growth patterns are associated with adult metabolic health, but the underlying mechanisms are unclear. We performed genome-wide meta-analyses and follow-up in up to 22,769 European children for six early growth phenotypes derived from longitudinal data: peak height and weight velocities, age and body mass index (BMI) at adiposity peak (AP ~9 months) and rebound (AR ~5-6 years). We identified four associated loci (P< 5x10−8): LEPR/LEPROT with BMI at AP, FTO and TFAP2B with Age at AR and GNPDA2 with BMI at AR. The observed AR-associated SNPs at FTO, TFAP2B and GNPDA2 represent known adult BMI-associated variants. The common BMI at AP associated variant at LEPR/LEPROT was not associated with adult BMI but was associated with LEPROT gene expression levels, especially in subcutaneous fat (P<2x10−51). We identify strong positive genetic correlations between early growth and later adiposity traits, and analysis of the full discovery stage results for Age at AR revealed enrichment for insulin-like growth factor 1 (IGF-1) signaling and apolipoprotein pathways. This genome-wide association study suggests mechanistic links between early childhood growth and adiposity in later childhood and adulthood, highlighting these early growth phenotypes as potential targets for the prevention of obesity.

scholarly journals ANGI — Anorexia Nervosa Genetics Initiative

2020 ◽  
Vol 23 (2) ◽  
pp. 135-136
Author(s):  
Cynthia Bulik ◽  
Martin Kennedy ◽  
Tracey Wade
Keyword(s):  
Anorexia Nervosa ◽  
Genetic Variants ◽  
Large Scale ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Life Threatening Illness ◽  
Genome Wide ◽  
Life Threatening

AbstractIdentification of genetic variants associated with eating disorders is underway. The Anorexia Nervosa Genetics Initiative, an initiative of the Klarman Family Foundation, has contributed to advancing the field, yielding a large-scale genome-wide association study published in Nature Genetics. Eight genetic variants significantly associated with anorexia nervosa were identified, along with patterns of genetic correlations that suggest both psychiatric and metabolic origins of this serious and life-threatening illness. This article details the role of Professor Nick Martin in contributing to this important collaboration.

scholarly journals CandiHap: a toolkit for haplotype analysis for sequence of samples and fast identification of candidate causal gene(s) in genome-wide association study

2020 ◽  
Author(s):  
Xukai Li ◽  
Zhiyong Shi ◽  
Qianru Qie ◽  
Jianhua Gao ◽  
Xingchun Wang ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Association Study ◽  
Complex Traits ◽  
Haplotype Analysis ◽  
Genome Wide Association Study ◽  
Significant Snps ◽  
Causal Variant ◽  
Genome Wide Association ◽  
Causal Gene ◽  
Genome Wide

AbstractGenome-wide association study (GWAS) is widely used to identify genes involved in plants, animals and human complex traits. Generally, the identified SNP is not necessarily the causal variant, but it is rather in linkage disequilibrium (LD). One key challenge for GWAS results interpretation is to rapidly identify causal genes and provide profound evidence on how they affect the trait. Researches want to identify candidate causal variants from the most significant SNPs of GWAS in any species and on their local computer, while to complete these tasks are to be time-consuming, laborious and prone to errors and omission. To our knowledge, so far there is no tool available to solve the challenge for GWAS data very quickly. Based on the standard VCF (variant call format) format, CandiHap is developed to fast preselection candidate causal SNPs and gene(s) from GWAS by integrating LD result, SNP annotation, haplotype analysis and traits statistics of haplotypes. Investigators can specify genes or linkage regions based on GWAS results, linkage disequilibrium (LD), and predicted candidate causal gene(s). It supported Windows, Mac and Linux computers and servers in graphical interface and command line, and applied to any other plant, animal or bacteria species. The source code of CandiHap tool is freely available at https://github.com/xukaili/CandiHap

scholarly journals Significant Locus and Metabolic Genetic Correlations Revealed in Genome-Wide Association Study of Anorexia Nervosa

2017 ◽  
Vol 174 (9) ◽  
pp. 850-858 ◽  
Author(s):  
Laramie Duncan ◽  
Zeynep Yilmaz ◽  
Helena Gaspar ◽  
Raymond Walters ◽  
Jackie Goldstein ◽  
...  
Keyword(s):  
Anorexia Nervosa ◽  
Association Study ◽  
Genome Wide Association Study ◽  
Genetic Correlations ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Significant Locus
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