scholarly journals Local genetic correlation gives insights into the shared genetic architecture of complex traits

2016 ◽  
Author(s):  
Huwenbo Shi ◽  
Nicholas Mancuso ◽  
Sarah Spendlove ◽  
Bogdan Pasaniuc
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Large Scale ◽  
Genetic Correlations ◽  
Small Region ◽  
Causal Variant ◽  
Genome Wide ◽  
Precise Quantification ◽  
Genomic Regions ◽  
Summary Data

AbstractAlthough genetic correlations between complex traits provide valuable insights into epidemiological and etiological studies, a precise quantification of which genomic regions contribute to the genome-wide genetic correlation is currently lacking. Here, we introduce ρ-HESS, a technique to quantify the correlation between pairs of traits due to genetic variation at a small region in the genome. Our approach only requires GWAS summary data and makes no distributional assumption on the causal variant effects sizes while accounting for linkage disequilibrium (LD) and overlapping GWAS samples. We analyzed large-scale GWAS summary data across 35 complex traits, and identified 27 genomic regions that contribute significantly to the genetic correlation among these traits. Notably, we find 7 genomic regions that contribute to the genetic correlation of 12 pairs of traits that show negligible genome-wide correlation, further showcasing the power of local genetic correlation analyses. Finally, we leverage the distribution of local genetic correlations across the genome to assign putative direction of causality for 15 pairs of traits.

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 Localizing components of shared transethnic genetic architecture of complex traits from GWAS summary data

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Kathryn S. Burch ◽  
Ruth Johnson ◽  
Malika K. Freund ◽  
Gleb Kichaev ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Empirical Bayes ◽  
Genetic Correlations ◽  
Causal Snps ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Snps ◽  
Genome Wide ◽  
Summary Data

AbstractDespite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze 9 complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8x enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWAS due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

scholarly journals Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

2020 ◽  
Author(s):  
Yiliang Zhang ◽  
Qiongshi Lu ◽  
Yixuan Ye ◽  
Kunling Huang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Genomic Regions

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions, which could shed unique light on etiologic sharing and provide additional mechanistic insights into the genetic basis of complex traits compared to global genetic correlation. However, accurate estimation of local genetic correlation remains challenging, in part due to extensive linkage disequilibrium in local genomic regions and pervasive sample overlap across studies. We introduce SUPERGNOVA, a unified framework to estimate both global and local genetic correlations using summary statistics from genome-wide association studies. Through extensive simulations and analyses of 30 complex traits, we demonstrate that SUPERGNOVA substantially outperforms existing methods and identifies 150 trait pairs with significant local genetic correlations. In particular, we show that the positive, consistently-identified, yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically-distinct genetic signatures with bidirectional local genetic correlations. We believe that statistically-rigorous local genetic correlation analysis could accelerate progress in complex trait genetics research.

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 Shared genetic architecture and casual relationship between leptin levels and type 2 diabetes: large-scale cross-trait meta-analysis and Mendelian randomization analysis

2020 ◽  
Vol 8 (1) ◽  
pp. e001140
Author(s):  
Xinpei Wang ◽  
Jinzhu Jia ◽  
Tao Huang
Keyword(s):  
Type 2 Diabetes ◽  
Genetic Correlation ◽  
Genetic Architecture ◽  
Large Scale ◽  
Meta Analysis ◽  
Genetic Correlations ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Shared Genetic

ObjectiveWe aimed to estimate genetic correlation, identify shared loci and test causality between leptin levels and type 2 diabetes (T2D).Research design and methodsOur study consists of three parts. First, we calculated the genetic correlation of leptin levels and T2D or glycemic traits by using linkage disequilibrium score regression analysis. Second, we conducted a large-scale genome-wide cross-trait meta-analysis using cross-phenotype association to identify shared loci between trait pairs that showed significant genetic correlations in the first part. In the end, we carried out a bidirectional MR analysis to find out whether there is a causal relationship between leptin levels and T2D or glycemic traits.ResultsWe found positive genetic correlations between leptin levels and T2D (Rg=0.3165, p=0.0227), fasting insulin (FI) (Rg=0.517, p=0.0076), homeostasis model assessment-insulin resistance (HOMA-IR) (Rg=0.4785, p=0.0196), as well as surrogate estimates of β-cell function (HOMA-β) (Rg=0.4456, p=0.0214). We identified 12 shared loci between leptin levels and T2D, 1 locus between leptin levels and FI, 1 locus between leptin levels and HOMA-IR, and 1 locus between leptin levels and HOMA-β. We newly identified eight loci that did not achieve genome-wide significance in trait-specific genome-wide association studies. These shared genes were enriched in pancreas, thyroid gland, skeletal muscle, placenta, liver and cerebral cortex. In addition, we found that 1-SD increase in HOMA-IR was causally associated with a 0.329 ng/mL increase in leptin levels (β=0.329, p=0.001).ConclusionsOur results have shown the shared genetic architecture between leptin levels and T2D and found causality of HOMA-IR on leptin levels, shedding light on the molecular mechanisms underlying the association between leptin levels and T2D.

scholarly journals SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiliang Zhang ◽  
Qiongshi Lu ◽  
Yixuan Ye ◽  
Kunling Huang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Autism Spectrum ◽  
Accurate Estimation ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genomic Regions ◽  
Genetic Signatures

AbstractLocal genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

scholarly journals Population-specific causal disease effect sizes in functionally important regions impacted by selection

2019 ◽  
Author(s):  
Huwenbo Shi ◽  
Steven Gazal ◽  
Masahiro Kanai ◽  
Evan M. Koch ◽  
Armin P. Schoech ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Causal Effect ◽  
Genetic Correlations ◽  
Effect Sizes ◽  
Environment Interaction ◽  
Immune Genes ◽  
Gene Environment ◽  
Genome Wide ◽  
Disease Effect

AbstractMany diseases and complex traits exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We developed a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and applied S-LDXR to genome-wide association summary statistics for 31 diseases and complex traits in East Asians (EAS) and Europeans (EUR) (average NEAS=90K, NEUR=267K) with an average trans-ethnic genetic correlation of 0.85 (s.e. 0.01). We determined that squared trans-ethnic genetic correlation was 0.82× (s.e. 0.01) smaller than the genome-wide average at SNPs in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes were more population-specific in functionally important regions, including conserved and regulatory regions. In analyses of regions surrounding specifically expressed genes, causal effect sizes were most population-specific for skin and immune genes and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

scholarly journals Fast estimation of genetic correlation for Biobank-scale data

2019 ◽  
Author(s):  
Yue Wu ◽  
Kathryn S. Burch ◽  
Andrea Ganna ◽  
Päivi Pajukanta ◽  
Bogdan Pasaniuc ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Genetic Correlations ◽  
Uk Biobank ◽  
Computationally Efficient ◽  
High Definition ◽  
Method Of Moments Estimator ◽  
The Uk ◽  
Summary Data ◽  
Randomized Method

AbstractGenetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while being computationally efficient, tend to yield estimates of genetic correlation with reduced precision. We propose, SCORE, a randomized method of moments estimator of genetic correlation that is both scalable and accurate. SCORE obtains more precise estimates of genetic correlations relative to summary-statistic methods that can be applied at scale achieving a 50% reduction in standard error relative to LD-score regression (LDSC) and a 26% reduction relative to high-definition likelihood (HDL) (averaged over all simulations). The efficiency of SCORE enables computation of genetic correlations on the UK biobank dataset consisting of ≈ 300K individuals and ≈ 500K SNPs in a few hours (orders of magnitude faster than methods that analyze individual data such as GCTA). Across 780 pairs of traits in 291, 273 unrelated white British individuals in the UK Biobank, SCORE identifies significant genetic correlation between 200 additional pairs of traits over LDSC (beyond the 245 pairs identified by both).

scholarly journals Population-specific causal disease effect sizes in functionally important regions impacted by selection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Huwenbo Shi ◽  
Steven Gazal ◽  
Masahiro Kanai ◽  
Evan M. Koch ◽  
Armin P. Schoech ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Causal Effect ◽  
Genetic Correlations ◽  
Effect Sizes ◽  
Environment Interaction ◽  
Immune Genes ◽  
Gene Environment ◽  
Genome Wide ◽  
Disease Effect

AbstractMany diseases exhibit population-specific causal effect sizes with trans-ethnic genetic correlations significantly less than 1, limiting trans-ethnic polygenic risk prediction. We develop a new method, S-LDXR, for stratifying squared trans-ethnic genetic correlation across genomic annotations, and apply S-LDXR to genome-wide summary statistics for 31 diseases and complex traits in East Asians (average N = 90K) and Europeans (average N = 267K) with an average trans-ethnic genetic correlation of 0.85. We determine that squared trans-ethnic genetic correlation is 0.82× (s.e. 0.01) depleted in the top quintile of background selection statistic, implying more population-specific causal effect sizes. Accordingly, causal effect sizes are more population-specific in functionally important regions, including conserved and regulatory regions. In regions surrounding specifically expressed genes, causal effect sizes are most population-specific for skin and immune genes, and least population-specific for brain genes. Our results could potentially be explained by stronger gene-environment interaction at loci impacted by selection, particularly positive selection.

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/

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