scholarly journals Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions

Genetics ◽  
2020 ◽  
Vol 215 (1) ◽  
pp. 231-241
Author(s):  
Scott A. Funkhouser ◽  
Ana I. Vazquez ◽  
Juan P. Steibel ◽  
Catherine W. Ernst ◽  
Gustavo de los Campos
Keyword(s):  
Bayesian Regression ◽  
Specific Marker ◽  
Phenotypic Variance ◽  
Mineral Density ◽  
Partial Explanation ◽  
Multiple Test Correction ◽  
Multiple Test ◽  
Specific Effects ◽  
Genome Wide ◽  
The Uk

Many complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (G×S) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events, due partly to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and G×S interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect G×S interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large G×S interaction impacting bone mineral density within ABO, and replicated many previously detected large-magnitude G×S interactions impacting waist-to-hip ratio. We also discovered many new G×S interactions impacting such traits as height and body mass index (BMI) within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1 × 10−4), but are enriched in known expression quantitative trait loci.

scholarly journals Deciphering sex-specific genetic architectures using local Bayesian regressions

2019 ◽  
Author(s):  
Scott A Funkhouser ◽  
Ana I Vazquez ◽  
Juan P Steibel ◽  
Catherine W Ernst ◽  
Gustavo de los Campos
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Effects ◽  
Genome Wide Association ◽  
Phenotypic Variance ◽  
Multiple Test Correction ◽  
Multiple Test ◽  
Specific Effects ◽  
Genome Wide ◽  
Causal Variants ◽  
Complex Human Traits

AbstractMany complex human traits exhibit differences between sexes. While numerous factors likely contribute to this phenomenon, growing evidence from genome-wide studies suggest a partial explanation: that males and females from the same population possess differing genetic architectures. Despite this, mapping gene-by-sex (G×S) interactions remains a challenge likely because the magnitude of such an interaction is typically and exceedingly small; traditional genome-wide association techniques may be underpowered to detect such events partly due to the burden of multiple test correction. Here, we developed a local Bayesian regression (LBR) method to estimate sex-specific SNP marker effects after fully accounting for local linkage-disequilibrium (LD) patterns. This enabled us to infer sex-specific effects and G×S interactions either at the single SNP level, or by aggregating the effects of multiple SNPs to make inferences at the level of small LD-based regions. Using simulations in which there was imperfect LD between SNPs and causal variants, we showed that aggregating sex-specific marker effects with LBR provides improved power and resolution to detect G×S interactions over traditional single-SNP-based tests. When using LBR to analyze traits from the UK Biobank, we detected a relatively large G×S interaction impacting bone-mineral density within ABO and replicated many previously detected large-magnitude G×S interactions impacting waist-to-hip ratio. We also discovered many new G×S interactions impacting such traits as height and BMI within regions of the genome where both male- and female-specific effects explain a small proportion of phenotypic variance (R2 < 1×10−4), but are enriched in known expression quantitative trait loci. By combining biobank-level data and techniques to estimate sex-specific SNP effects after accounting for local-LD patterns, we are providing evidence that numerous small-magnitude G×S interactions exist to influence sex differences in a variety of complex traits.Author SummaryMany complex human traits are known to be influenced by an impressive number of causal variants each with very small effects, posing great challenges for genome-wide association studies (GWAS). To add to this challenge, many causal variants may possess context-dependent effects such as effects that are dependent on biological sex. While GWAS are commonly performed using specific methods in which one single nucleotide polymorphism (SNP) at a time is tested for association with a trait, alternatively we utilize methods more commonly observed in the genomic prediction literature. Such methods are advantageous in that they are not burdened by multiple test correction in the same way as traditional GWAS techniques are, and can fully account for linkage-disequilibrium patterns to accurately estimate the true effects of SNP markers. Here we adapt such methods to estimate genetic effects within sexes and provide a powerful means to compare sex-specific genetic effects.

scholarly journals Analysis of genetic dominance in the UK Biobank

2021 ◽  
Author(s):  
Duncan S Palmer ◽  
Wei Zhou ◽  
Liam Abbott ◽  
Nik Baya ◽  
Claire Churchhouse ◽  
...  
Keyword(s):  
Complex Traits ◽  
Multiple Testing ◽  
Model Organisms ◽  
Systematic Evaluation ◽  
Hair Color ◽  
Phenotypic Variance ◽  
Additive Effects ◽  
Uk Biobank ◽  
Genome Wide ◽  
The Uk

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

scholarly journals Genome-wide association analysis of 350,000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema

2017 ◽  
Author(s):  
Åsa Johansson ◽  
Mathias Rask-Andersen ◽  
Torgny Karlsson ◽  
Weronica E. Ek
Keyword(s):  
Genetic Factors ◽  
Hay Fever ◽  
The Novel ◽  
Genetic Contribution ◽  
Uk Biobank ◽  
Disease Phenotypes ◽  
Specific Effects ◽  
Genome Wide ◽  
Causal Genes ◽  
The Uk

AbstractEven though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this GWA study, we include 346,545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema. We further investigate if associated lead SNPs have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease specific effects.We identified 141 loci, of which 41 are novel, to be associated (P≤3×10−8) with asthma, hay fever or eczema, analysed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci were associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema-only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in LD (> 0.8) with potentially casual missense variants.Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes suggesting that part of the genetic contribution is more phenotype specific. Identified loci and probable causal genes may in the future be used as targets for treatments of asthma, hay fever and eczema.

scholarly journals Genome-wide association analysis of 350 000 Caucasians from the UK Biobank identifies novel loci for asthma, hay fever and eczema

2019 ◽  
Vol 28 (23) ◽  
pp. 4022-4041 ◽  
Author(s):  
Åsa Johansson ◽  
Mathias Rask-Andersen ◽  
Torgny Karlsson ◽  
Weronica E Ek
Keyword(s):  
Genome Wide Association Study ◽  
Hay Fever ◽  
Genome Wide Association ◽  
The Novel ◽  
Nucleotide Polymorphisms ◽  
Genetic Contribution ◽  
Uk Biobank ◽  
Specific Effects ◽  
Genome Wide ◽  
The Uk

Abstract Even though heritability estimates suggest that the risk of asthma, hay fever and eczema is largely due to genetic factors, previous studies have not explained a large part of the genetics behind these diseases. In this genome-wide association study, we include 346 545 Caucasians from the UK Biobank to identify novel loci for asthma, hay fever and eczema and replicate novel loci in three independent cohorts. We further investigate if associated lead single nucleotide polymorphisms (SNPs) have a significantly larger effect for one disease compared to the other diseases, to highlight possible disease-specific effects. We identified 141 loci, of which 41 are novel, to be associated (P ≤ 3 × 10−8) with asthma, hay fever or eczema, analyzed separately or as disease phenotypes that includes the presence of different combinations of these diseases. The largest number of loci was associated with the combined phenotype (asthma/hay fever/eczema). However, as many as 20 loci had a significantly larger effect on hay fever/eczema only compared to their effects on asthma, while 26 loci exhibited larger effects on asthma compared with their effects on hay fever/eczema. At four of the novel loci, TNFRSF8, MYRF, TSPAN8, and BHMG1, the lead SNPs were in Linkage Disequilibrium (LD) (&gt;0.8) with potentially casual missense variants. Our study shows that a large amount of the genetic contribution is shared between the diseases. Nonetheless, a number of SNPs have a significantly larger effect on one of the phenotypes, suggesting that part of the genetic contribution is more phenotype specific.

scholarly journals Probing the aggregated effects of purifying selection per individual on 1,380 medical phenotypes in the UK Biobank

PLoS Genetics ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. e1009337
Author(s):  
Ha My T. Vy ◽  
Daniel M. Jordan ◽  
Daniel J. Balick ◽  
Ron Do
Keyword(s):  
Natural Selection ◽  
Strong Association ◽  
Purifying Selection ◽  
Relative Fitness ◽  
Uk Biobank ◽  
Multiple Test Correction ◽  
Multiple Test ◽  
The Uk ◽  
The Relationship ◽  
Statistical Metrics

Understanding the relationship between natural selection and phenotypic variation has been a long-standing challenge in human population genetics. With the emergence of biobank-scale datasets, along with new statistical metrics to approximate strength of purifying selection at the variant level, it is now possible to correlate a proxy of individual relative fitness with a range of medical phenotypes. We calculated a per-individual deleterious load score by summing the total number of derived alleles per individual after incorporating a weight that approximates strength of purifying selection. We assessed four methods for the weight, including GERP, phyloP, CADD, and fitcons. By quantitatively tracking each of these scores with the site frequency spectrum, we identified phyloP as the most appropriate weight. The phyloP-weighted load score was then calculated across 15,129,142 variants in 335,161 individuals from the UK Biobank and tested for association on 1,380 medical phenotypes. After accounting for multiple test correction, we observed a strong association of the load score amongst coding sites only on 27 traits including body mass, adiposity and metabolic rate. We further observed that the association signals were driven by common variants (derived allele frequency > 5%) with high phyloP score (phyloP > 2). Finally, through permutation analyses, we showed that the load score amongst coding sites had an excess of nominally significant associations on many medical phenotypes. These results suggest a broad impact of deleterious load on medical phenotypes and highlight the deleterious load score as a tool to disentangle the complex relationship between natural selection and medical phenotypes.

scholarly journals Wnt receptors, bone mass, and fractures: gene-wide association analysis of LRP5 and LRP6 polymorphisms with replication

2011 ◽  
Vol 164 (1) ◽  
pp. 123-131 ◽  
Author(s):  
José A Riancho ◽  
José M Olmos ◽  
Begoña Pineda ◽  
Carmen García-Ibarbia ◽  
María I Pérez-Núñez ◽  
...  
Keyword(s):  
Femoral Neck ◽  
Hip Fractures ◽  
Osteoporotic Fractures ◽  
Nucleotide Polymorphisms ◽  
Mineral Density ◽  
Discovery Cohort ◽  
Cross Sectional ◽  
Multiple Test Correction ◽  
Multiple Test ◽  
Genes Encoding

ObjectiveGenes explaining the susceptibility to osteoporosis have not been fully elucidated. Our objective was to explore the association of polymorphisms capturing common variations of the lipoprotein receptor-related protein (LRP) 5 and 6 genes, encoding two Wnt receptors, with femoral neck bone mineral density (BMD) and osteoporotic fractures of the spine and the hip.DesignCross-sectional, case–control, and replication genetic association study.MethodsThirty-nine tagging and functional single nucleotide polymorphisms (SNPs) were analyzed in a group of 1043 postmenopausal women and 394 women with hip fractures. The results were replicated in a different group of 342 women.ResultsThree SNPs of the LRP6 gene were associated with BMD (nominal uncorrected P values <0.05) in the discovery cohort. One showed a significant association after multiple test correction; two of them were also associated in the replication cohort, with a combined standardized mean difference of 0.51 (P=0.009) and 0.47 (P<0.003) across rs11054704 and rs2302685 genotypes. In the discovery cohort, several LRP5 SNPs were associated with vertebral fractures (odds ratio (OR) 0.67; P=0.01), with hip fractures (unadjusted ORs between 0.59 and 1.21; P=0.005–0.033, but not significant after multiple test adjustment or age adjustment), and with height and the projected femoral neck area, but not with BMD. Transcripts of LRP5 and LRP6 were similarly abundant in bone samples.ConclusionsIn this study, we found common polymorphisms of LRP5 associated with osteoporotic fractures, and polymorphisms of the LRP6 gene associated with BMD, thus suggesting them as likely candidates to contribute to the explaination of the hereditary influence on osteoporosis.

scholarly journals Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers

2021 ◽  
Author(s):  
Kenneth E Westerman ◽  
Timothy D Majarian ◽  
Franco Giulianini ◽  
Dong-Keun Jang ◽  
Jose C Florez ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Meta Analysis ◽  
Health Study ◽  
Small Subset ◽  
Phenotypic Variance ◽  
Gene Environment ◽  
Genome Wide ◽  
Trait Loci ◽  
The Uk

Gene-environment interactions (GEIs) represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. GEIs often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci (vQTLs) can be prioritized in a two-stage GEI detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We performed genome-wide vQTL analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5x10-9). Most vQTLs were concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicated (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we tested each vQTL for interaction across 2,380 exposures, identifying 846 significant GEIs (p < 2.4x10-7). Specific examples demonstrated interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of vQTLs and GEIs is publicly available in an online portal.

scholarly journals Accurate estimation of SNP-heritability from biobank-scale data irrespective of genetic architecture

2019 ◽  
Author(s):  
Kangcheng Hou ◽  
Kathryn S. Burch ◽  
Arunabha Majumdar ◽  
Huwenbo Shi ◽  
Nicholas Mancuso ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Accurate Estimation ◽  
Phenotypic Variance ◽  
Uk Biobank ◽  
Genome Wide ◽  
Wide Range ◽  
Fundamental Quantity ◽  
The Uk ◽  
Scale Data

AbstractThe proportion of phenotypic variance attributable to the additive effects of a given set of genotyped SNPs (i.e. SNP-heritability) is a fundamental quantity in the study of complex traits. Recent works have shown that existing methods to estimate genome-wide SNP-heritability often yield biases when their assumptions are violated. While various approaches have been proposed to account for frequency- and LD-dependent genetic architectures, it remains unclear which estimates of SNP-heritability reported in the literature are reliable. Here we show that genome-wide SNP-heritability can be accurately estimated from biobank-scale data irrespective of the underlying genetic architecture of the trait, without specifying a heritability model or partitioning SNPs by minor allele frequency and/or LD. We use theoretical justifications coupled with extensive simulations starting from real genotypes from the UK Biobank (N=337K) to show that, unlike existing methods, our closed-form estimator for SNP-heritability is highly accurate across a wide range of architectures. We provide estimates of SNP-heritability for 22 complex traits and diseases in the UK Biobank and show that, consistent with our results in simulations, existing biobank-scale methods yield estimates up to 30% different from our theoretically-justified approach.

Genetic Burden Analyses of Phenotypes Relevant to Aging in the Berlin Aging Study II (BASE-II)

Gerontology ◽  
2016 ◽  
Vol 62 (3) ◽  
pp. 316-322 ◽  
Author(s):  
Christina M. Lill ◽  
Tian Liu ◽  
Kristina Norman ◽  
Antje Meyer ◽  
Elisabeth Steinhagen-Thiessen ◽  
...  
Keyword(s):  
Telomere Length ◽  
Association Studies ◽  
Receiver Operating Curve ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Mineral Density ◽  
Association Analyses ◽  
Genome Wide ◽  
Aging Study

Background: Body mass index (BMI), bone mineral density (BMD), and telomere length are phenotypes that modulate the course of aging. Over 40% of their phenotypic variance is determined by genetics. Genome-wide association studies (GWAS) have recently uncovered >100 independent single-nucleotide polymorphisms (SNPs) showing genome-wide significant (p < 5 × 10-8) association with these traits. Objective: To test the individual and combined impact of previously reported GWAS SNPs on BMI, BMD, and relative leukocyte telomere length (rLTL) in ∼1,750 participants of the Berlin Aging Study II (BASE-II), a cohort consisting predominantly of individuals >60 years of age. Methods: Linear regression analyses were performed on a total of 101 SNPs and BMI, BMD measurements of the femoral neck (FN) and lumbar spine (LS), and rLTL. The combined effect of all trait-specific SNPs was evaluated by generating a weighted genomic profile score (wGPS) used in the association analyses. The predictive capability of the wGPS was estimated by determining the area under the receiver operating curve (AUC) for osteoporosis status (determined by BMD) with and without the wGPS. Results: Five loci showed experiment-wide significant association with BMI (FTO rs1558902, p = 1.80 × 10-5) or BMD (MEPE rs6532023, pFN = 5.40 × 10-4, pLS = 1.09 × 10-4; TNFRSF11B rs2062377, pLS = 8.70 × 10-4; AKAP11 rs9533090, pLS = 1.05 × 10-3; SMG6 rs4790881, pFN = 3.41 × 10-4) after correction for multiple testing. Several additional loci showed nominally significant (p < 0.05) association with BMI and BMD. The trait-specific wGPS was highly significantly associated with BMD (p < 2 × 10-16) and BMI (p = 1.10 × 10-6). No significant association was detected for rLTL in either single-SNP or wGPS-based analyses. The AUC for osteoporosis improved modestly from 0.762 (95% CI 0.733-0.800) to 0.786 (95% CI 0.756-0.823) and 0.785 (95% CI 0.757-0.824) upon inclusion of the FN- and LS-BMD wGPS, respectively. Conclusion: Our study provides an independent validation of previously reported genetic association signals for BMI and BMD in the BASE-II cohort. Additional studies are needed to pinpoint the factors underlying the proportion of phenotypic variance that remains unexplained by the current models.

Sign in / Sign up

Export Citation Format

Share Document