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Improving genetic prediction by leveraging genetic correlations among human diseases and traits.

ABSTRACT: Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.

SUBMITTER: Maier RM

PROVIDER: S-EPMC5841449 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Improving genetic prediction by leveraging genetic correlations among human diseases and traits.

Maier Robert M RM Zhu Zhihong Z Lee Sang Hong SH Trzaskowski Maciej M Ruderfer Douglas M DM Stahl Eli A EA Ripke Stephan S Wray Naomi R NR Yang Jian J Visscher Peter M PM Robinson Matthew R MR

Nature communications 20180307 1

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. ...[more]

PMID: 29515099

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Project description:BackgroundConsidering relatives' health history in logistic regression for case-control genome-wide association studies (CC-GWAS) may provide new information that increases accuracy and power to detect disease associated genetic variants. We conducted simulations and analyzed type 2 diabetes (T2D) data from the Framingham Heart Study (FHS) to compare two methods, liability threshold model conditional on both case-control status and family history (LT-FH) and Fam-meta, which incorporate family history into CC-GWAS.ResultsIn our simulation scenario of trait with modest T2D heritability (h2 = 0.28), variant minor allele frequency ranging from 1% to 50%, and 1% of phenotype variance explained by the genetic variants, Fam-meta had the highest overall power, while both methods incorporating family history were more powerful than CC-GWAS. All three methods had controlled type I error rates, while LT-FH was the most conservative with a lower-than-expected error rate. In addition, we observed a substantial increase in power of the two familial history methods compared to CC-GWAS when the prevalence of the phenotype increased with age. Furthermore, we showed that, when only the phenotypes of more distant relatives were available, Fam-meta still remained more powerful than CC-GWAS, confirming that leveraging disease history of both close and distant relatives can increase power of association analyses. Using FHS data, we confirmed the well-known association of TCF7L2 region with T2D at the genome-wide threshold of P-value < 5 × 10-8, and both familial history methods increased the significance of the region compared to CC-GWAS. We identified two loci at 5q35 (ADAMTS2) and 5q23 (PRR16), not previously reported for T2D using CC-GWAS and Fam-meta; both genes play a role in cardiovascular diseases. Additionally, CC-GWAS detected one more significant locus at 13q31 (GPC6) reported associated with T2D-related traits.ConclusionsOverall, LT-FH and Fam-meta had higher power than CC-GWAS in simulations, especially using phenotypes that were more prevalent in older age groups, and both methods detected known genetic variants with lower P-values in real data application, highlighting the benefits of including family history in genetic association studies.

Project description:Clinical studies have indicated a comorbidity between sepsis and kidney diseases. Individuals with specific mutations that predispose them to kidney conditions are also at an elevated risk for developing sepsis, and vice versa. This suggests a potential shared genetic etiology that has not been fully elucidated. Summary statistics data on exposure and outcomes were obtained from genome-wide association meta-analysis studies. We utilized these data to assess genetic correlations, employing a pleiotropy analysis method under the composite null hypothesis to identify pleiotropic loci. After mapping the loci to their corresponding genes, we conducted pathway analysis using Generalized Gene-Set Analysis of GWAS Data (MAGMA). Additionally, we utilized MAGMA gene-test and eQTL information (whole blood tissue) for further determination of gene involvement. Further investigation involved stratified LD score regression, using diverse immune cell data, to study the enrichment of SNP heritability in kidney-related diseases and sepsis. Furthermore, we employed Mendelian Randomization (MR) analysis to investigate the causality between kidney diseases and sepsis. In our genetic correlation analysis, we identified significant correlations among BUN, creatinine, UACR, serum urate, kidney stones, and sepsis. The PLACO analysis method identified 24 pleiotropic loci, pinpointing a total of 28 nearby genes. MAGMA gene-set enrichment analysis revealed a total of 50 pathways, and tissue-specific analysis indicated significant enrichment of five pairs of pleiotropic results in kidney tissue. MAGMA gene test and eQTL information (whole blood tissue) identified 33 and 76 pleiotropic genes, respectively. Notably, genes PPP2R3A for BUN, VAMP8 for UACR, DOCK7 for creatinine, and HIBADH for kidney stones were identified as shared risk genes by all three methods. In a series of immune cell-type-specific enrichment analyses of pleiotropy, we identified a total of 37 immune cells. However, MR analysis did not reveal any causal relationships among them. This study lays the groundwork for shared etiological factors between kidney and sepsis. The confirmed pleiotropic loci, shared pathogenic genes, and enriched pathways and immune cells have enhanced our understanding of the multifaceted relationships among these diseases. This provides insights for early disease intervention and effective treatment, paving the way for further research in this field.

Dataset Information

Improving genetic prediction by leveraging genetic correlations among human diseases and traits.

Publications

Improving genetic prediction by leveraging genetic correlations among human diseases and traits.

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