Bioinformatics pipeline for the systematic mining genomic and proteomic variation linked to rare diseases: The example of monogenic diabetes

Monogenic diabetes is characterized as a group of diseases caused by rare variants in single genes. Like for other rare diseases, multiple genes have been linked to monogenic diabetes with different measures of pathogenicity, but the information on the genes and variants is not unified among different resources, making it challenging to process them informatically. We have developed an automated pipeline for collecting and harmonizing data on genetic variants linked to monogenic diabetes. Furthermore, we have translated variant genetic sequences into protein sequences accounting for all protein isoforms and their variants. This allows researchers to consolidate information on variant genes and proteins linked to monogenic diabetes and facilitates their study using proteomics or structural biology. Our open and flexible implementation using Jupyter notebooks enables tailoring and modifying the pipeline and its application to other rare diseases.

Assessing the genetic contribution of cumulative behavioral factors associated with longitudinal type 2 diabetes risk highlights adiposity and the brain-metabolic axis

While genetic factors, behavior, and environmental exposures form a complex web of interrelated associations in type 2 diabetes (T2D), their interaction is poorly understood. Here, using data from ~500K participants of the UK Biobank, we identify the genetic determinants of a "polyexposure risk score" (PXS) a new risk factor that consists of an accumulation of 25 associated individual-level behaviors and environmental risk factors that predict longitudinal T2D incidence. PXS-T2D had a non-zero heritability (h2 = 0.18) extensive shared genetic architecture with established clinical and biological determinants of T2D, most prominently with body mass index (genetic correlation [rg] = 0.57) and Homeostatic Model Assessment for Insulin Resistance (rg = 0.51). Genetic loci associated with PXS-T2D were enriched for expression in the brain. Biobank scale data with genetic information illuminates how complex and cumulative exposures and behaviors as a whole impact T2D risk but whose biology have been elusive in genome-wide studies of T2D.

Rare variant association analysis in 51,256 type 2 diabetes cases and 370,487 controls informs the spectrum of pathogenicity of monogenic diabetes genes

We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.

Heterogeneous effects on type 2 diabetes and cardiovascular outcomes of genetic variants and traits associated with fasting insulin

Hyperinsulinemia is a complex and heterogeneous phenotype that characterizes molecular alterations that precede the development of type 2 diabetes (T2D). It results from a complex combination of molecular processes, including insulin secretion and insulin sensitivity, that differ between individuals. To better understand the physiology of hyperinsulinemia and ultimately T2D, we implemented a genetic approach grouping fasting insulin (FI)-associated genetic variants based on their molecular and phenotypic similarities. We identified seven distinctive genetic clusters representing different physiologic mechanisms leading to rising FI levels, ranging from clusters of variants with effects on increased FI, but without increased risk of T2D (non-diabetogenic hyperinsulinemia), to clusters of variants that increase FI and T2D risk with demonstrated strong effects on body fat distribution, liver, lipid, and inflammatory processes (diabetogenic hyperinsulinemia). We generated cluster-specific polygenic scores in 1,104,258 individuals from five multi-ancestry cohorts to show that the clusters differed in associations with cardiometabolic traits. Among clusters characterized by non-diabetogenic hyperinsulinemia, there was both increased and decreased risk of coronary artery disease despite the non-increased risk of T2D. Similarly, the clusters characterized by diabetogenic hyperinsulinemia were associated with an increased risk of T2D, yet had differing risks of cardiovascular conditions, including coronary artery disease, myocardial infarction, and stroke. The strongest cluster-T2D associations were observed with the same direction of effect in non-Hispanic Black, Hispanic, non-Hispanic White, and non-Hispanic East Asian populations. These genetic clusters provide important insights into granular metabolic processes underlying the physiology of hyperinsulinemia, notably highlighting specific processes that decouple increasing FI levels from T2D and cardiovascular risk. Our findings suggest that increasing FI levels are not invariably associated with adverse cardiometabolic outcomes.

Initial Insights into the Genetic Variation Associated with Metformin Treatment Failure in Youth with Type 2 Diabetes

Metformin is the first-line treatment for type 2 diabetes (T2D) in youth but with limited sustained glycemic response. To identify common variants associated with metformin response, we used a genome-wide approach in 506 youth from the Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) study and examined the relationship between T2D partitioned polygenic scores (pPS), glycemic traits, and metformin response in these youth. Several variants met a suggestive threshold (P < 1 × 10-6), though none including published adult variants reached genome-wide significance. We pursued replication of top nine variants in three cohorts, and rs76195229 in ATRNL1 was associated with worse metformin response in the Metformin Genetics Consortium (n = 7,812), though statistically not being significant after Bonferroni correction (P = 0.06). A higher β-cell pPS was associated with a lower insulinogenic index (P = 0.02) and C-peptide (P = 0.047) at baseline and higher pPS related to two insulin resistance processes were associated with increased C-peptide at baseline (P = 0.04,0.02). Although pPS were not associated with changes in glycemic traits or metformin response, our results indicate a trend in the association of the β-cell pPS with reduced β-cell function over time. Our data show initial evidence for genetic variation associated with metformin response in youth with T2D.

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes

Background

The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities.

Methods and Findings

All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized.

Conclusions

This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

OR05-1 Genetic Discovery and Translational Decision Support from Exome Sequencing of 45,231 Type 2 Diabetes Cases and Controls from Five Ancestries

Analysis of human exome sequences, the protein-coding regions of genomes, has the potential to identify variation in genes relevant to disease for expedited clinical and therapeutic translation. Here we present results from a massive-scale type 2 diabetes (T2D) exome sequencing project. We analyzed exomes from 20,791 participants with T2D and 24,440 controls from five ancestries (Hispanic/Latino 33.8%, European 24.6%, African-American 13.9%, East Asian 14.1%, South Asian 13.6%) with mean depth 40x, and conducted both single-variant and gene-level association testing. Eighteen single-variant signals reached exome-wide significance (p< 4.3×10^-7); all were common variants (minor allele frequency [MAF] > 5%) except MC4R p.Ile269Asn, which was almost exclusively seen in Hispanic/Latinos with MAF 0.89% and T2D OR=2.17 (95% CI: 1.63-2.89) in that population. Only one single-variant association represented a previously unknown T2D genetic locus (SFI1 p.Arg724Trp), but this finding failed to replicate in an independent cohort. We identified gene-level associations composed of rare (MAF < 0.5%) variants (a) in three genes each reaching a gene-level exome-wide significance threshold of 6.57×10^-7 (MC4R, PAM, and SLC30A8), including a T2D protective series of >30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to known T2D drug targets (p=6.1×10^-3) and candidate genes from knockout mice (p=5.2×10^-3). These aggregate gene-level associations suggest that larger sample sizes will allow more refined identification of novel T2D genes, including potential new drug targets. Based on the rare-variant gene-level effect sizes we observed in established T2D drug targets, we estimate that 110K-180K sequenced cases would be required for reliable identification of new T2D drug targets. Additionally, we have developed a Bayesian framework using our association results to facilitate prioritization of genes for translational decision support.

Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum

There is a limited understanding about the impact of rare protein-truncating variants across multiple phenotypes. We explore the impact of this class of variants on 13 quantitative traits and 10 diseases using whole-exome sequencing data from 100,296 individuals. Protein-truncating variants in genes intolerant to this class of mutations increased risk of autism, schizophrenia, bipolar disorder, intellectual disability, and ADHD. In individuals without these disorders, there was an association with shorter height, lower education, increased hospitalization, and reduced age at enrollment. Gene sets implicated from GWASs did not show a significant protein-truncating variants burden beyond what was captured by established Mendelian genes. In conclusion, we provide a thorough investigation of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.

Common Germline Genetic Variation in Antioxidant Defense Genes and Survival After Diagnosis of Breast Cancer

Purpose

The prognosis of breast cancer varies considerably among individuals, and inherited genetic factors may help explain this variability. Of particular interest are genes involved in defense against reactive oxygen species (ROS) because ROS are thought to cause DNA damage and contribute to the pathogenesis of cancer.

Patients and Methods

We examined associations between 54 polymorphisms that tag the known common variants (minor allele frequency > 0.05) in 10 genes involved in oxidative damage repair (CAT, SOD1, SOD2, GPX1, GPX4, GSR, TXN, TXN2, TXNRD1, and TXNRD2) and survival in 4,470 women with breast cancer.

Results

Two single nucleotide polymorphisms (SNPs) in GPX4 ( rs713041 and rs757229 ) were associated with all-cause mortality even after adjusting for multiple hypothesis testing (adjusted P = .0041 and P = .0035). These SNPs are correlated with each other (r2 = 0.61). GPX4 rs713041 is located near the selenocysteine insertion sequence element in the GPX4 3′ untranslated region, and the rare allele of this SNP is associated with an increased risk of death, with a hazard ratio of 1.27 per rare allele carried (95% CI, 1.13 to 11.43). This effect was not attenuated after adjusting for tumor stage, grade, or estrogen receptor status. We found that the common allele is preferentially expressed in normal lymphocytes, normal breast, and breast tumors compared with the rare allele, but there were no differences in total levels of GPX4 mRNA across genotypes.

Conclusion

These data provide strong support for the hypothesis that common variation in GPX4 is associated with prognosis after a diagnosis of breast cancer.