GW-SEM 2.0: Efficient, Flexible, and Accessible Multivariate GWAS

Abstract To learn more about the mechanisms of human dietary fat perception, we asked 398 human twins to rate the fattiness and how much they liked 6 types of potato chips that differed in triglyceride content (2.5%, 5%, 10%, and 15% corn oil); reliability estimates were obtained from a subset (n = 50) who did the task twice. Some chips also had a saturated long-chain fatty acid (FA; hexadecanoic acid, 16:0) added (0.2%) to evaluate its effect on fattiness and liking. We computed the heritability of these measures and conducted a genome-wide association study (GWAS) to identify regions of the genome that co-segregate with fattiness and liking. Perceived fattiness of and liking for the potato chips were reliable (r = 0.31–0.62, P < 0.05) and heritable (up to h2 = 0.29, P < 0.001, for liking). Adding hexadecanoic acid to the potato chips significantly increased ratings of fattiness but decreased liking. Twins with the G allele of rs263429 near GATA3-AS1 or the G allele of rs8103990 within ZNF729 reported more liking for potato chips than did twins with the other allele (multivariate GWAS, P < 1 × 10–5), with results reaching genome-wide suggestive but not significance criteria. Person-to-person variation in the perception and liking of dietary fat was 1) negatively affected by the addition of a saturated FA and 2) related to inborn genetic variants. These data suggest that liking for dietary fat is not due solely to FA content and highlight new candidate genes and proteins within this sensory pathway.

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Use of the Multivariate Discriminant Analysis for Genome-Wide Association Studies in Cattle

Animals ◽

10.3390/ani10081300 ◽

2020 ◽

Vol 10 (8) ◽

pp. 1300 ◽

Cited By ~ 1

Author(s):

Elisabetta Manca ◽

Alberto Cesarani ◽

Giustino Gaspa ◽

Silvia Sorbolini ◽

Nicolò P.P. Macciotta ◽

...

Keyword(s):

Discriminant Analysis ◽

Association Studies ◽

Real Data ◽

Genome Wide Association ◽

Stepwise Discriminant Analysis ◽

Genome Wide Association Studies ◽

Multivariate Method ◽

Genome Wide ◽

Single Marker ◽

Multivariate Gwas

Genome-wide association studies (GWAS) are traditionally carried out by using the single marker regression model that, if a small number of individuals is involved, often lead to very few associations. The Bayesian methods, such as BayesR, have obtained encouraging results when they are applied to the GWAS. However, these approaches, require that an a priori posterior inclusion probability threshold be fixed, thus arbitrarily affecting the obtained associations. To partially overcome these problems, a multivariate statistical algorithm was proposed. The basic idea was that animals with different phenotypic values of a specific trait share different allelic combinations for genes involved in its determinism. Three multivariate techniques were used to highlight the differences between the individuals assembled in high and low phenotype groups: the canonical discriminant analysis, the discriminant analysis and the stepwise discriminant analysis. The multivariate method was tested both on simulated and on real data. The results from the simulation study highlighted that the multivariate GWAS detected a greater number of true associated single nucleotide polymorphisms (SNPs) and Quantitative trait loci (QTLs) than the single marker model and the Bayesian approach. For example, with 3000 animals, the traditional GWAS highlighted only 29 significantly associated markers and 13 QTLs, whereas the multivariate method found 127 associated SNPs and 65 QTLs. The gap between the two approaches slowly decreased as the number of animals increased. The Bayesian method gave worse results than the other two. On average, with the real data, the multivariate GWAS found 108 associated markers for each trait under study and among them, around 63% SNPs were also found in the single marker approach. Among the top 118 associated markers, 76 SNPs harbored putative candidate genes.

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Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities

10.1101/603134 ◽

2019 ◽

Cited By ~ 11

Author(s):

Travis T. Mallard ◽

Richard K. Linnér ◽

Andrew D. Grotzinger ◽

Sandra Sanchez-Roige ◽

Jakob Seidlitz ◽

...

Keyword(s):

Mental Illness ◽

Serious Mental Illness ◽

Genome Wide Association Study ◽

Psychiatric Symptoms ◽

Two Dimensions ◽

Mood Disturbance ◽

Psychotic Symptoms ◽

Genome Wide ◽

Multivariate Gwas ◽

Cardinal Symptoms

AbstractUnderstanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study (GWAS) of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of mood disturbance (major depressive disorder, bipolar II, and self-reported symptoms of depression, mania, and psychosis) versus rarer forms of serious mental illness (bipolar I, schizoaffective disorder, and schizophrenia). Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind, rather than merely in degree.

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DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

10.1101/069096 ◽

2016 ◽

Cited By ~ 1

Author(s):

Janine Arloth ◽

Gökcen Eraslan ◽

Till F.M. Andlauer ◽

Jade Martins ◽

Stella Iurato ◽

...

Keyword(s):

Deep Learning ◽

Disease Risk ◽

Association Studies ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Cell Type ◽

Individual Snps ◽

Multivariate Gwas ◽

Regulatory Effects ◽

Joint Contribution

AbstractGenome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS- and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

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A Fast Method for Estimating Statistical Power of Multivariate GWAS in Real Case Scenarios: Examples from the Field of Imaging Genetics

Behavior Genetics ◽

10.1007/s10519-018-9936-9 ◽

2018 ◽

Vol 49 (1) ◽

pp. 112-121 ◽

Cited By ~ 2

Author(s):

Baptiste Couvy-Duchesne ◽

Lachlan T. Strike ◽

Katie L. McMahon ◽

Greig I. de Zubicaray ◽

Paul M. Thompson ◽

...

Keyword(s):

Statistical Power ◽

Imaging Genetics ◽

Real Case ◽

Fast Method ◽

Multivariate Gwas

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Genome-wide association analyses of individual differences in quantitatively assessed reading- and language-related skills in up to 34,000 people

10.1101/2021.11.04.466897 ◽

2021 ◽

Author(s):

Else Eising ◽

Nazanin Mirza-Schreiber ◽

Eveline L de Zeeuw ◽

Carol A Wang ◽

Dongnhu T Truong ◽

...

Keyword(s):

Genetic Variation ◽

Individual Differences ◽

Word Reading ◽

Written Language ◽

Phoneme Awareness ◽

Genome Wide Association ◽

Nonword Repetition ◽

Nonword Reading ◽

Genome Wide ◽

Multivariate Gwas

The use of spoken and written language is a capacity that is unique to humans. Individual differences in reading- and language-related skills are influenced by genetic variation, with twin-based heritability estimates of 30-80%, depending on the trait. The relevant genetic architecture is complex, heterogeneous, and multifactorial, and yet to be investigated with well-powered studies. Here, we present a multicohort genome-wide association study (GWAS) of five traits assessed individually using psychometric measures: word reading, nonword reading, spelling, phoneme awareness, and nonword repetition, with total sample sizes ranging from 13,633 to 33,959 participants aged 5-26 years (12,411 to 27,180 for those with European ancestry, defined by principal component analyses). We identified a genome-wide significant association with word reading (rs11208009, p=1.098 x 10-8) independent of known loci associated with intelligence or educational attainment. All five reading-/language-related traits had robust SNP-heritability estimates (0.13-0.26), and genetic correlations between them were modest to high. Using genomic structural equation modelling, we found evidence for a shared genetic factor explaining the majority of variation in word and nonword reading, spelling, and phoneme awareness, which only partially overlapped with genetic variation contributing to nonword repetition, intelligence and educational attainment. A multivariate GWAS was performed to jointly analyse word and nonword reading, spelling, and phoneme awareness, maximizing power for follow-up investigation. Genetic correlation analysis of multivariate GWAS results with neuroimaging traits identified association with cortical surface area of the banks of the left superior temporal sulcus, a brain region with known links to processing of spoken and written language. Analysis of evolutionary annotations on the lineage that led to modern humans showed enriched heritability in regions depleted of Neanderthal variants. Together, these results provide new avenues for deciphering the biological underpinnings of these uniquely human traits.

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Multi-Trait Genome-Wide Association Studies Reveal Loci Associated with Maize Inflorescence and Leaf Architecture

Plant and Cell Physiology ◽

10.1093/pcp/pcaa039 ◽

2020 ◽

Vol 61 (8) ◽

pp. 1427-1437 ◽

Cited By ~ 6

Author(s):

Brian R Rice ◽

Samuel B Fernandes ◽

Alexander E Lipka

Keyword(s):

Quantitative Trait ◽

Genome Wide Association Study ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Multiple Traits ◽

Leaf Architecture ◽

Genome Wide ◽

Multivariate Gwas ◽

Genomic Regions

Abstract Maize inflorescence is a complex phenotype that involves the physical and developmental interplay of multiple traits. Given the evidence that genes could pleiotropically contribute to several of these traits, we used publicly available maize data to assess the ability of multivariate genome-wide association study (GWAS) approaches to identify pleiotropic quantitative trait loci (pQTL). Our analysis of 23 publicly available inflorescence and leaf-related traits in a diversity panel of n = 281 maize lines genotyped with 376,336 markers revealed that the two multivariate GWAS approaches we tested were capable of identifying pQTL in genomic regions coinciding with similar associations found in previous studies. We then conducted a parallel simulation study on the same individuals, where it was shown that multivariate GWAS approaches yielded a higher true-positive quantitative trait nucleotide (QTN) detection rate than comparable univariate approaches for all evaluated simulation settings except for when the correlated simulated traits had a heritability of 0.9. We therefore conclude that the implementation of state-of-the-art multivariate GWAS approaches is a useful tool for dissecting pleiotropy and their more widespread implementation could facilitate the discovery of genes and other biological mechanisms underlying maize inflorescence.

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Studies of human twins reveal genetic variation that affects dietary fat perception

10.1101/2020.01.18.910448 ◽

2020 ◽

Author(s):

Cailu Lin ◽

Lauren Colquitt ◽

Paul Wise ◽

Paul A. S. Breslin ◽

Nancy E. Rawson ◽

...

Keyword(s):

Fatty Acid ◽

Dietary Fat ◽

Genome Wide Association Study ◽

Corn Oil ◽

Fatty Acid Content ◽

Potato Chips ◽

Hexadecanoic Acid ◽

Genome Wide ◽

A Genome ◽

Multivariate Gwas

AbstractTo learn more about the mechanisms of human dietary fat perception, 398 human twins rated fattiness and liking for six types of potato chips that differed in triglyceride content (2.5, 5, 10, and 15% corn oil); reliability estimates were obtained from a subset (n = 50) who did the task twice. Some chips also had a saturated long-chain fatty acid (hexadecanoic acid, 16:0) added (0.2%) to evaluate its effect on fattiness and liking. We computed the heritability of these measures and conducted a genome-wide association study (GWAS) to identify regions of the genome that co-segregate with fattiness and liking. Perceived fattiness and liking for the potato chips were reliable (r = 0.31-0.62, p < 0.05) and heritable (up to h2 = 0.29, p < 0.001, for liking). Adding hexadecanoic acid to the potato chips significantly increased ratings of fattiness but decreased liking. Twins with the G allele of rs263429 near GATA3-AS1 or the G allele of rs8103990 within ZNF729 reported more liking for potato chips than did twins with the other allele (multivariate GWAS, p < 1×10-5), with results reaching genome-wide suggestive but not significance criteria. Person-to-person variation in the perception and liking of dietary fat was (a) negatively affected by the addition of a saturated fatty acid and (b) related to inborn genetic variants. These data suggest liking for dietary fat is not due solely to fatty acid content and highlight new candidate genes and proteins within this sensory pathway.

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Genome-wide association identifies candidate genes for drought tolerance in coast redwood and giant sequoia

10.1101/2021.10.25.465813 ◽

2021 ◽

Author(s):

Amanda De La Torre ◽

Manoj K Sekhwal ◽

Daniela Puiu ◽

Steven Salzberg ◽

Alison Dawn Scott ◽

...

Keyword(s):

Drought Tolerance ◽

Candidate Genes ◽

Metabolic Stress ◽

Population Level ◽

Severe Drought ◽

Physiological Basis ◽

Coast Redwood ◽

Genome Wide ◽

Giant Sequoia ◽

Multivariate Gwas

Drought is a major limitation for survival and growth in plants. With more frequent and severe drought episodes occurring due to climate change, it is imperative to understand the genomic and physiological basis of drought tolerance to be able to predict how species will respond in the future. In this study, univariate and multitrait multivariate GWAS methods were used to identify candidate genes in two iconic and ecosystem-dominating species of the western US, coast redwood and giant sequoia, using ten drought-related physiological and anatomical traits and genome wide sequence-capture SNPs. Population level phenotypic variation was found in carbon isotope discrimination, osmotic pressure at full turgor, xylem hydraulic diameter and total area of transporting fibers in both species. Our study identified new 78 new marker x trait associations in coast redwood and six in giant sequoia, with genes involved in a range of metabolic, stress and signaling pathways, among other functions. This study contributes to a better understanding of the genomic basis of drought tolerance in long-generation conifers and helps guide current and future conservation efforts in the species.

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