scholarly journals Analysis of common genetic variation and rare CNVs in the Australian Autism Biobank

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Chloe X. Yap ◽  
Gail A. Alvares ◽  
Anjali K. Henders ◽  
Tian Lin ◽  
Leanne Wallace ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Autism Spectrum ◽  
Research Centre ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Common Genetic Variation ◽  
Cooperative Research ◽  
Rare Cnvs ◽  
Genome Wide ◽  
Polygenic Scores

Abstract Background Autism spectrum disorder (ASD) is a complex neurodevelopmental condition whose biological basis is yet to be elucidated. The Australian Autism Biobank (AAB) is an initiative of the Cooperative Research Centre for Living with Autism (Autism CRC) to establish an Australian resource of biospecimens, phenotypes and genomic data for research on autism. Methods Genome-wide single-nucleotide polymorphism genotypes were available for 2,477 individuals (after quality control) from 546 families (436 complete), including 886 participants aged 2 to 17 years with diagnosed (n = 871) or suspected (n = 15) ASD, 218 siblings without ASD, 1,256 parents, and 117 unrelated children without an ASD diagnosis. The genetic data were used to confirm familial relationships and assign ancestry, which was majority European (n = 1,964 European individuals). We generated polygenic scores (PGS) for ASD, IQ, chronotype and height in the subset of Europeans, and in 3,490 unrelated ancestry-matched participants from the UK Biobank. We tested for group differences for each PGS, and performed prediction analyses for related phenotypes in the AAB. We called copy-number variants (CNVs) in all participants, and intersected these with high-confidence ASD- and intellectual disability (ID)-associated CNVs and genes from the public domain. Results The ASD (p = 6.1e−13), sibling (p = 4.9e−3) and unrelated (p = 3.0e−3) groups had significantly higher ASD PGS than UK Biobank controls, whereas this was not the case for height—a control trait. The IQ PGS was a significant predictor of measured IQ in undiagnosed children (r = 0.24, p = 2.1e−3) and parents (r = 0.17, p = 8.0e−7; 4.0% of variance), but not the ASD group. Chronotype PGS predicted sleep disturbances within the ASD group (r = 0.13, p = 1.9e−3; 1.3% of variance). In the CNV analysis, we identified 13 individuals with CNVs overlapping ASD/ID-associated CNVs, and 12 with CNVs overlapping ASD/ID/developmental delay-associated genes identified on the basis of de novo variants. Limitations This dataset is modest in size, and the publicly-available genome-wide-association-study (GWAS) summary statistics used to calculate PGS for ASD and other traits are relatively underpowered. Conclusions We report on common genetic variation and rare CNVs within the AAB. Prediction analyses using currently available GWAS summary statistics are largely consistent with expected relationships based on published studies. As the size of publicly-available GWAS summary statistics grows, the phenotypic depth of the AAB dataset will provide many opportunities for analyses of autism profiles and co-occurring conditions, including when integrated with other omics datasets generated from AAB biospecimens (blood, urine, stool, hair).

scholarly journals HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Daniel M. Jordan ◽  
Marie Verbanck ◽  
Ron Do
Keyword(s):  
Genetic Variation ◽  
Genetic Architecture ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Human Genetic Variation ◽  
Uk Biobank ◽  
Multiple Traits ◽  
Genome Wide ◽  
Quantitative Score ◽  
Independent Effects

Abstract Horizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

scholarly journals HOPS: a quantitative score reveals pervasive horizontal pleiotropy in human genetic variation is driven by extreme polygenicity of human traits and diseases

2018 ◽  
Author(s):  
Daniel M. Jordan ◽  
Marie Verbanck ◽  
Ron Do
Keyword(s):  
Genetic Variation ◽  
Genetic Architecture ◽  
Genome Wide Association ◽  
Summary Statistics ◽  
Human Genetic Variation ◽  
Uk Biobank ◽  
Multiple Traits ◽  
Genome Wide ◽  
Quantitative Score ◽  
Independent Effects

AbstractHorizontal pleiotropy, where one variant has independent effects on multiple traits, is important for our understanding of the genetic architecture of human phenotypes. We develop a method to quantify horizontal pleiotropy using genome-wide association summary statistics and apply it to 372 heritable phenotypes measured in 361,194 UK Biobank individuals. Horizontal pleiotropy is pervasive throughout the human genome, prominent among highly polygenic phenotypes, and enriched in active regulatory regions. Our results highlight the central role horizontal pleiotropy plays in the genetic architecture of human phenotypes. The HOrizontal Pleiotropy Score (HOPS) method is available on Github at https://github.com/rondolab/HOPS.

Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

2020 ◽  
Author(s):  
John E. McGeary ◽  
Chelsie Benca-Bachman ◽  
Victoria Risner ◽  
Christopher G Beevers ◽  
Brandon Gibb ◽  
...  
Keyword(s):  
Suicidal Ideation ◽  
Cognitive Reappraisal ◽  
Twin Studies ◽  
European Ancestry ◽  
Summary Statistics ◽  
Depression Severity ◽  
Uk Biobank ◽  
Polygenic Scores ◽  
Adults And Children ◽  
The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

scholarly journals Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

2019 ◽  
Vol 25 (10) ◽  
pp. 2422-2430 ◽  
Author(s):  
Douglas M. Ruderfer ◽  
Colin G. Walsh ◽  
Matthew W. Aguirre ◽  
Yosuke Tanigawa ◽  
Jessica D. Ribeiro ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Suicide Attempt ◽  
Genetic Correlation ◽  
Genetic Profile ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Common Genetic Variation ◽  
Genetic Studies ◽  
Patient Reported ◽  
The Uk

Abstract Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

Common genetic variation and risk of gallbladder cancer in India: a case-control genome-wide association study

2017 ◽  
Vol 18 (4) ◽  
pp. 535-544 ◽  
Author(s):  
Sharayu Mhatre ◽  
Zhaoming Wang ◽  
Rajini Nagrani ◽  
Rajendra Badwe ◽  
Shubhada Chiplunkar ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Association Study ◽  
Gallbladder Cancer ◽  
Genome Wide Association Study ◽  
Case Control ◽  
Genome Wide Association ◽  
Common Genetic Variation ◽  
Genome Wide

scholarly journals Polygenic scores for UK Biobank scale data

2018 ◽  
Author(s):  
Timothy Shin Heng Mak ◽  
Robert Milan Porsch ◽  
Shing Wan Choi ◽  
Pak Chung Sham
Keyword(s):  
External Source ◽  
Summary Statistics ◽  
Uk Biobank ◽  
Validation Data ◽  
Raw Data ◽  
Cross Prediction ◽  
Polygenic Scores ◽  
The Difference ◽  
The Uk ◽  
Meta Analyses

AbstractPolygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, such that it would be a waste if the raw data were not made use of in constructing PGS. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied in PGS calculations and highlight the difference between overfitting due to the overlap between the target and the discovery data (OTD), and overfitting due to the overlap between the target the the validation data (OTV). We propose two methods — cross prediction and split validation — to overcome OTD and OTV respectively. Using these two methods, PGS can be calculated using raw data without overfitting. We show that PGSs thus calculated have better predictive power than those using summary statistics alone for six phenotypes in the UK Biobank data.

scholarly journals MTAG: Multi-Trait Analysis of GWAS

2017 ◽  
Author(s):  
Patrick Turley ◽  
Raymond K. Walters ◽  
Omeed Maghzian ◽  
Aysu Okbay ◽  
James J. Lee ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Well Being ◽  
Joint Analysis ◽  
Summary Statistics ◽  
Subjective Well Being ◽  
Bioinformatics Analyses ◽  
Trait Analysis ◽  
Genome Wide ◽  
Polygenic Scores ◽  
Variance Explained

ABSTRACTWe introduce Multi-Trait Analysis of GWAS (MTAG), a method for joint analysis of summary statistics from GWASs of different traits, possibly from overlapping samples. We apply MTAG to summary statistics for depressive symptoms (Neff = 354,862), neuroticism (N = 168,105), and subjective well-being (N = 388,538). Compared to 32, 9, and 13 genome-wide significant loci in the single-trait GWASs (most of which are themselves novel), MTAG increases the number of loci to 64, 37, and 49, respectively. Moreover, association statistics from MTAG yield more informative bioinformatics analyses and increase variance explained by polygenic scores by approximately 25%, matching theoretical expectations.

scholarly journals Multi-ancestry Multivariate Genome-Wide Analysis Highlights the Role of Common Genetic Variation in Cardiac Structure, Function, and Heart Failure-related Traits

2021 ◽  
Author(s):  
Michael G. Levin ◽  
Noah L. Tsao ◽  
Tiffany R. Bellomo ◽  
William Paul Bone ◽  
Krishna G. Aragam ◽  
...  
Keyword(s):  
Gene Expression ◽  
Heart Failure ◽  
Genetic Variation ◽  
Association Study ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Mendelian Randomization ◽  
Cardiac Structure ◽  
Common Genetic Variation ◽  
Genome Wide

Heart failure (HF) is a leading cause of cardiovascular morbidity and mortality, yet the contribution of common genetic variation to HF risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We conducted a multi-ancestry genome-wide association study (GWAS) meta-analysis of all-cause HF including up to 56,722 HF cases and 1,133,054 controls, identifying 4 novel loci. We then performed a multi-ancestry multivariate association study of HF and related cardiac imaging endophenotypes, identifying 71 conditionally-independent variants, including 16 novel loci. Secondary colocalization and transcriptome-wide association analyses identified known and novel candidate cardiomyopathy genes, which were validated in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provided convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization revealed 11 circulating proteins associated with HF or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate novel common genetic variation in the pathogenesis of HF, and identify circulating proteins that may represent novel cardiomyopathy treatment targets.

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