scholarly journals Statistical methods to detect pleiotropy in human complex traits

Open Biology ◽  
2017 ◽  
Vol 7 (11) ◽  
pp. 170125 ◽  
Author(s):  
Sophie Hackinger ◽  
Eleftheria Zeggini
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Human Genetics ◽  
Genetic Locus ◽  
Summary Statistics ◽  
Multiple Traits ◽  
Future Directions ◽  
Genome Wide ◽  
Genetic Overlap ◽  
Human Complex

In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

scholarly journals Phenome-wide search for pleiotropic loci highlights key genes and molecular pathways for human complex traits

2019 ◽  
Author(s):  
Anton E. Shikov ◽  
Alexander V. Predeus ◽  
Yury A. Barbitoff
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Human Genetics ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Statistical Framework ◽  
Genome Wide ◽  
Mhc Locus ◽  
High Degree ◽  
Human Complex

AbstractOver recent decades, genome-wide association studies (GWAS) have dramatically changed the understanding of human genetics. A recent genetic data release by UK Biobank has allowed many researchers worldwide to have comprehensive look into the genetic architecture of thousands of human phenotypes. In this study, we developed a novel statistical framework to assess phenome-wide significance and genetic pleiotropy across the human phenome based on GWAS summary statistics. We demonstrate widespread sharing of genetic architecture components between distinct groups of traits. Apart from known multiple associations inside the MHC locus, we discover high degree of pleiotropy for genes involved in immune system function, apoptosis, hemostasis cascades, as well as lipid and xenobiotic metabolism. We find several notable examples of novel pleiotropic loci (e.g., the MIR2113 microRNA broadly associated with cognition), and provide several possible mechanisms for these association signals. Our results allow for a functional phenome-wide look into the shared components of genetic architecture of human complex traits, and highlight crucial genes and pathways for their development.

scholarly journals Evaluation of methods incorporating biological function and GWAS summary statistics to accelerate discovery

2022 ◽  
Author(s):  
Amy Moore ◽  
Jesse Marks ◽  
Bryan C Quach ◽  
Yuelong Guo ◽  
Laura J Bierut ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genome Wide Association Study ◽  
Biological Function ◽  
High Sensitivity ◽  
Multiple Traits ◽  
Genome Wide ◽  
Larger Sample ◽  
Weighting Methods ◽  
Novel Associations

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 18 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

scholarly journals A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS

Biostatistics ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 477-494 ◽  
Author(s):  
Jakub Pecanka ◽  
Marianne A. Jonker ◽  
Zoltan Bochdanovits ◽  
Aad W. Van Der Vaart ◽  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Statistical Power ◽  
Genome Wide Association Study ◽  
Score Test ◽  
Interaction Model ◽  
Type I ◽  
Two Stage ◽  
Genome Wide ◽  
Strong Control

Summary For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the “missing heritability” of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson’s disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.

scholarly journals GxEsum: a novel approach to estimate the phenotypic variance explained by genome-wide GxE interaction based on GWAS summary statistics for biobank-scale data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jisu Shin ◽  
Sang Hong Lee
Keyword(s):  
Complex Traits ◽  
Error Rates ◽  
Type I ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Summary Statistics ◽  
Gxe Interaction ◽  
Genome Wide ◽  
Scale Data ◽  
Variance Explained

AbstractGenetic variation in response to the environment, that is, genotype-by-environment interaction (GxE), is fundamental in the biology of complex traits and diseases. However, existing methods are computationally demanding and infeasible to handle biobank-scale data. Here, we introduce GxEsum, a method for estimating the phenotypic variance explained by genome-wide GxE based on GWAS summary statistics. Through comprehensive simulations and analysis of UK Biobank with 288,837 individuals, we show that GxEsum can handle a large-scale biobank dataset with controlled type I error rates and unbiased GxE estimates, and its computational efficiency can be hundreds of times higher than existing GxE methods.

scholarly journals Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alvaro N. Barbeira ◽  
◽  
Rodrigo Bonazzola ◽  
Eric R. Gamazon ◽  
Yanyu Liang ◽  
...  
Keyword(s):  
Complex Traits ◽  
Target Genes ◽  
Genome Wide Association Study ◽  
Data Driven ◽  
Functional Interpretation ◽  
Transcriptome Regulation ◽  
Genome Wide ◽  
Causal Genes ◽  
Dose Dependent ◽  
Single Approach

AbstractThe resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Identification of Potential Pleiotropic Genes for Immune and Skeletal Diseases Using Multivariate MetaCCA Analysis

2021 ◽  
Vol 23 ◽  
Author(s):  
Pei He ◽  
Rong- Rong Cao ◽  
Fei- Yan Deng ◽  
Shu- Feng Lei
Keyword(s):  
Association Study ◽  
Canonical Correlation ◽  
Genetic Factors ◽  
Genome Wide Association Study ◽  
Summary Statistics ◽  
Immune Disease ◽  
Histocompatibility Complex ◽  
Genome Wide ◽  
Genetic Mechanisms ◽  
Shared Genetic

Background: Immune and skeletal systems physiologically and pathologically interact with each other. The immune and skeletal diseases may share potential pleiotropic genetics factors, but the shared specific genes are largely unknown Objective: This study aimed to investigate the overlapping genetic factors between multiple diseases (including rheumatoid arthritis (RA), psoriasis, osteoporosis, osteoarthritis, sarcopenia and fracture) Methods: The canonical correlation analysis (metaCCA) approach was used to identify the shared genes for six diseases by integrating genome-wide association study (GWAS)-derived summary statistics. Versatile Gene-based Association Study (VEGAS2) method was further applied to refine and validate the putative pleiotropic genes identified by metaCCA. Results: About 157 (p<8.19E-6), 319 (p<3.90E-6) and 77 (p<9.72E-6) potential pleiotropic genes were identified shared by two immune disease, four skeletal diseases, and all of the six diseases, respectively. The top three significant putative pleiotropic genes shared by both immune and skeletal diseases, including HLA-B, TSBP1 and TSBP1-AS1 (p<E-300) were located in the major histocompatibility complex (MHC) region. Nineteen of 77 putative pleiotropic genes identified by metaCCA analysis were associated with at least one disease in the VEGAS2 analysis. Specifically, majority (18) of these 19 putative validated pleiotropic genes were associated with RA. Conclusion: The metaCCA method identified some pleiotropic genes shared by the immune and skeletal diseases. These findings help to improve our understanding of the shared genetic mechanisms and signaling pathways underlying immune and skeletal diseases.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment 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 twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

scholarly journals Improved estimation of phenotypic correlations using summary association statistics

2020 ◽  
Author(s):  
Xia Shen ◽  
Ting Li ◽  
Zheng Ning
Keyword(s):  
Complex Traits ◽  
State Of The Art ◽  
Phenotypic Correlation ◽  
Summary Statistics ◽  
Z Score ◽  
Phenotypic Correlations ◽  
Simple Strategy ◽  
Null Effect ◽  
Correlation Estimation ◽  
Genome Wide

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect SNPs and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. Comparing to LDSC, the low-MAF estimator improves phenotypic correlation estimation thus is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

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.

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