Evidence for extensive pleiotropy among pharmacogenes

Genetic analyses implicate complex links between adult testosterone levels and health and disease

BACKGROUND

Testosterone levels are linked with diverse characteristics of human health, yet, whether these associations reflect correlation or causation remains debated. Here, we provide a broad perspective on the role of genetically determined testosterone on complex diseases in both sexes.

METHODS

Leveraging genetic and health registry data from the UK Biobank and FinnGen (total N = 625,650), we constructed polygenic scores (PGS) for total testosterone, sex-hormone binding globulin (SHBG) and free testosterone, associating these with 36 endpoints across different disease categories in the FinnGen. These analyses were combined with Mendelian Randomization (MR) and cross-sex PGS analyses to address causality.

RESULTS

We show testosterone and SHBG levels are intricately tied to metabolic health, but report lack of causality behind most associations, including type 2 diabetes (T2D). Across other disease domains, including 13 behavioral and neurological diseases, we similarly find little evidence for a substantial contribution from normal variation in testosterone levels. We nonetheless find genetically predicted testosterone affects many sex-specific traits, with a pronounced impact on female reproductive health, including causal contribution to PCOS-related traits like hirsutism and post-menopausal bleeding (PMB). We also illustrate how testosterone levels associate with antagonistic effects on stroke risk and reproductive endpoints between the sexes.

CONCLUSIONS

Overall, these findings provide insight into how genetically determined testosterone correlates with several health parameters in both sexes. Yet the lack of evidence for a causal contribution to most traits beyond sex-specific health underscores the complexity of the mechanisms linking testosterone levels to disease risk and sex differences.

Revisiting Secondary Information Related to Pharmacogenetic Testing

Incidental or secondary findings have been a major part of the discussion of genomic medicine research and clinical applications. For pharmacogenetic (PGx) testing, secondary findings arise due to the pleiotropic effects of pharmacogenes, often related to their endogenous functions. Unlike the guidelines that have been developed for whole exome or genome sequencing applications for management of secondary findings (though slightly different from PGx testing in that these refer to detection of variants in multiple genes, some with clinical significance and actionability), no corresponding guidelines have been developed for PGx clinical laboratories. Nonetheless, patient and provider education will remain key components of any PGx testing program to minimize adverse responses related to secondary findings.

Methodology in phenome-wide association studies: a systematic review

Phenome-wide association study (PheWAS) has been increasingly used to identify novel genetic associations across a wide spectrum of phenotypes. This systematic review aims to summarise the PheWAS methodology, discuss the advantages and challenges of PheWAS, and provide potential implications for future PheWAS studies. Medical Literature Analysis and Retrieval System Online (MEDLINE) and Excerpta Medica Database (EMBASE) databases were searched to identify all published PheWAS studies up until 24 April 2021. The PheWAS methodology incorporating how to perform PheWAS analysis and which software/tool could be used, were summarised based on the extracted information. A total of 1035 studies were identified and 195 eligible articles were finally included. Among them, 137 (77.0%) contained 10 000 or more study participants, 164 (92.1%) defined the phenome based on electronic medical records data, 140 (78.7%) used genetic variants as predictors, and 73 (41.0%) conducted replication analysis to validate PheWAS findings and almost all of them (94.5%) received consistent results. The methodology applied in these PheWAS studies was dissected into several critical steps, including quality control of the phenome, selecting predictors, phenotyping, statistical analysis, interpretation and visualisation of PheWAS results, and the workflow for performing a PheWAS was established with detailed instructions on each step. This study provides a comprehensive overview of PheWAS methodology to help practitioners achieve a better understanding of the PheWAS design, to detect understudied or overstudied outcomes, and to direct their research by applying the most appropriate software and online tools for their study data structure.

Integrating disease and drug-related phenotypes for improved identification of pharmacogenomic variants

Aim: To improve the identification and interpretation of pharmacogenetic variants through the integration of disease and drug-related traits. Materials & methods: We hypothesized that integrating genome-wide disease and pharmacogenomic data may drive new insights into drug toxicity and response by identifying shared genetic architecture. Pleiotropic variants were identified using a methodological framework incorporating colocalization analysis. Results: Using genome-wide association studies summary statistics from the UK Biobank, European Bioinformatics Institute genome-wide association studies catalog and the Pharmacogenomics Research Network, we validated pleiotropy at the ABCG2 locus between allopurinol response and gout and identified novel pleiotropy between antihypertensive-induced new-onset diabetes, Crohn's disease and inflammatory bowel disease at the IL18RAP/SLC9A4 locus. Conclusion: New mechanistic insights and genetic loci can be uncovered by identifying pleiotropy between disease and drug-related traits.

The association between genetic polymorphisms in ABCG2 and SLC2A9 and urate: an updated systematic review and meta-analysis

Background

Replication studies showed conflicting effects of ABCG2 and SLC2A9 polymorphisms on gout and serum urate. This meta-analysis therefore aimed to pool their effects across studies.

Methods

Studies were located from MEDLINE and Scopus from inception to 17th June 2018. Observational studies in adults with any polymorphism in ABCG2 or SLC2A9, and outcome including gout, hyperuricemia, and serum urate were included for pooling. Data extractions were performed by two independent reviewers. Genotype effects were pooled stratified by ethnicity using a mixed-effect logistic model and a multivariate meta-analysis for dichotomous and continuous outcomes.

Results

Fifty-two studies were included in the analysis. For ABCG2 polymorphisms, mainly studied in Asians, carrying 1–2 minor-allele-genotypes of rs2231142 and rs72552713 were respectively about 2.1–4.5 and 2.5–3.9 times higher odds of gout than non-minor-allele-genotypes. The two rs2231142-risk-genotypes also had higher serum urate about 11–18 μmol/l. Conversely, carrying 1–2 minor alleles of rs2231137 was about 36–57% significantly lower odds of gout. For SLC2A9 polymorphisms, mainly studied in Caucasians, carrying 1–2 minor alleles of rs1014290, rs6449213, rs6855911, and rs7442295 were about 25–43%, 31–62%, 33–64%, and 35–65% significantly lower odds of gout than non-minor-allele-genotypes. In addition, 1–2 minor-allele-genotypes of the latter three polymorphisms had significantly lower serum urate about 20–49, 21–51, and 18–54 μmol/l than non-minor-allele-genotypes.

Conclusions

Our findings should be useful in identifying patients at risk for gout and high serum urate and these polymorphisms may be useful in personalized risk scores.

Trial registration

PROSPERO registration number: CRD42018105275.

Supplementary information

The online version contains supplementary material available at 10.1186/s12881-020-01147-2.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

Objective

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.