Dataset Information

Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

ABSTRACT: 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.

SUBMITTER: Funkhouser SA

PROVIDER: S-EPMC7198271 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

Funkhouser Scott A SA Vazquez Ana I AI Steibel Juan P JP Ernst Catherine W CW Los Campos Gustavo de G

Genetics 20200320 1

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 u ...[more]

PMID: 32198180

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Project description:Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model.We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called ? performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set.We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter ? appears more promising than a number of alternatives.

Project description:BackgroundThe inheritance pattern in most cases of autism is complex. The risk of autism is increased in siblings of children with autism and previous studies have indicated that the level of risk can be further identified by the accumulation of multiple susceptibility single nucleotide polymorphisms (SNPs) allowing for the identification of a higher-risk subgroup among siblings. As a result of the sex difference in the prevalence of autism, we explored the potential for identifying sex-specific autism susceptibility SNPs in siblings of children with autism and the ability to develop a sex-specific risk assessment genetic scoring system.MethodsSNPs were chosen from genes known to be associated with autism. These markers were evaluated using an exploratory sample of 480 families from the Autism Genetic Resource Exchange (AGRE) repository. A reproducibility index (RI) was proposed and calculated in all children with autism and in males and females separately. Differing genetic scoring models were then constructed to develop a sex-specific genetic score model designed to identify individuals with a higher risk of autism. The ability of the genetic scores to identify high-risk children was then evaluated and replicated in an independent sample of 351 affected and 90 unaffected siblings from families with at least 1 child with autism.ResultsWe identified three risk SNPs that had a high RI in males, two SNPs with a high RI in females, and three SNPs with a high RI in both sexes. Using these results, genetic scoring models for males and females were developed which demonstrated a significant association with autism (P = 2.2 × 10-6 and 1.9 × 10-5, respectively).ConclusionsOur results demonstrate that individual susceptibility associated SNPs for autism may have important differential sex effects. We also show that a sex-specific risk score based on the presence of multiple susceptibility associated SNPs allow for the identification of subgroups of siblings of children with autism who have a significantly higher risk of autism.

Dataset Information

Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

Publications

Deciphering Sex-Specific Genetic Architectures Using Local Bayesian Regressions.

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