Rare variants in drug target genes contributing to complex diseases, phenome-wide

How Can Omics Inform Diabetic Foot Ulcer Clinical Management? A Whole Genome Comparison of Four Clinical Strains of Staphylococcus aureus

Foot ulcers and associated infections significantly contribute to morbidity and mortality in diabetes. While diverse pathogens are found in the diabetes-related infected ulcers, Staphylococcus aureus remains one of the most virulent and widely prevalent pathogens. The high prevalence of S. aureus in chronic wound infections, especially in clinical settings, is attributed to its ability to evolve and acquire resistance against common antibiotics and to elicit an array of virulence factors. In this study, whole genome comparison of four strains of S. aureus (MUF168, MUF256, MUM270, and MUM475) isolated from diabetic foot ulcer (DFU) infections showing varying resistance patterns was carried out to study the genomic similarity, antibiotic resistance profiling, associated virulence factors, and sequence variations in drug targets. The comparative genome analysis showed strains MUM475 and MUM270 to be highly resistant, MUF256 with moderate levels of resistance, and MUF168 to be the least resistant. Strain MUF256 and MUM475 harbored more virulence factors compared with other two strains. Deleterious sequence variants were observed suggesting potential role in altering drug targets and drug efficacy. This comparative whole genome study offers new molecular insights that may potentially inform evidence-based diagnosis and treatment of DFUs in the clinic.

Impact of natural selection on global patterns of genetic variation and association with clinical phenotypes at genes involved in SARS-CoV-2 infection

Human genomic diversity has been shaped by both ancient and ongoing challenges from viruses. The current coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had a devastating impact on population health. However, genetic diversity and evolutionary forces impacting host genes related to SARS-CoV-2 infection are not well understood. We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (angiotensin converting enzyme 2 [ACE2], transmembrane protease serine 2 [TMPRSS2], dipeptidyl peptidase 4 [DPP4], and lymphocyte antigen 6 complex locus E [LY6E]). We analyzed data from 2,012 ethnically diverse Africans and 15,977 individuals of European and African ancestry with electronic health records and integrated with global data from the 1000 Genomes Project. At ACE2, we identified 41 nonsynonymous variants that were rare in most populations, several of which impact protein function. However, three nonsynonymous variants (rs138390800, rs147311723, and rs145437639) were common among central African hunter-gatherers from Cameroon (minor allele frequency 0.083 to 0.164) and are on haplotypes that exhibit signatures of positive selection. We identify signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage compared with the chimpanzee genome. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19. Our study provides insights into global variation at host genes related to SARS-CoV-2 infection, which have been shaped by natural selection in some populations, possibly due to prior viral infections.

Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection

We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection (ACE2, TMPRSS2, DPP4, and LY6E). We analyzed novel data from 2,012 ethnically diverse Africans and 15,997 individuals of European and African ancestry with electronic health records, and integrated with global data from the 1000GP. At ACE2, we identified 41 non-synonymous variants that were rare in most populations, several of which impact protein function. However, three non-synonymous variants were common among Central African hunter-gatherers from Cameroon and are on haplotypes that exhibit signatures of positive selection. We identify strong signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2, we identified 13 amino acid changes that are adaptive and specific to the human lineage. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19.

Impact of natural selection on global patterns of genetic variation, and association with clinical phenotypes, at genes involved in SARS-CoV-2 infection

We investigated global patterns of genetic variation and signatures of natural selection at host genes relevant to SARS-CoV-2 infection ( ACE2, TMPRSS2, DPP4 , and LY6E ). We analyzed novel data from 2,012 ethnically diverse Africans and 15,997 individuals of European and African ancestry with electronic health records, and integrated with global data from the 1000GP. At ACE2 , we identified 41 non-synonymous variants that were rare in most populations, several of which impact protein function. However, three non-synonymous variants were common among Central African hunter-gatherers from Cameroon and are on haplotypes that exhibit signatures of positive selection. We identify strong signatures of selection impacting variation at regulatory regions influencing ACE2 expression in multiple African populations. At TMPRSS2 , we identified 13 amino acid changes that are adaptive and specific to the human lineage. Genetic variants that are targets of natural selection are associated with clinical phenotypes common in patients with COVID-19.

Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank

BACKGROUND

Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer.

METHODS

We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings.

RESULTS

We identified 133 significant pathway-cancer pairs (85 replicated) and 90 significant gene-cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05).

CONCLUSIONS

In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers.

IMPACT

This work adds to the knowledge base and progress being made in precision medicine.

Genetic Analysis Reveals Rare Variants in T-Cell Response Gene MR1 Associated with Poor Overall Survival after Urothelial Cancer Diagnosis

Urothelial carcinoma of the bladder (UC) is the fifth most common cancer in the United States. Germline variants, especially rare germline variants, may account for a portion of the disparity seen among patients in terms of UC incidence, presentation, and outcomes. The objectives of this study were to identify rare germline variant associations in UC incidence and to determine its association with clinical outcomes. Using exome sequencing data from the DiscovEHR UC cohort (n = 446), a European-ancestry, North American population, the complex influence of germline variants on known clinical phenotypes were analyzed using dispersion and burden metrics with regression tests. Outcomes measured were derived from the electronic health record (EHR) and included UC incidence, age at diagnosis, and overall survival (OS). Consequently, key rare variant association genes were implicated in MR1 and ADGRL2. The Kaplan-Meier survival analysis reveals that individuals with MR1 germline variants had significantly worse OS than those without any (log-rank p-value = 3.46 × 10^-7). Those with ADGRL2 variants were found to be slightly more likely to have UC compared to a matched control cohort (FDR q-value = 0.116). These associations highlight several candidate genes that have the potential to explain clinical disparities in UC and predict UC outcomes.

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.

Structural variation at the CYP2C locus: Characterization of deletion and duplication alleles

The human CYP2C locus harbors the polymorphic CYP2C18, CYP2C19, CYP2C9, and CYP2C8 genes, and of these, CYP2C19 and CYP2C9 are directly involved in the metabolism of ~15% of all medications. All variant CYP2C19 and CYP2C9 star (*) allele haplotypes currently cataloged by the Pharmacogene Variation (PharmVar) Consortium are defined by sequence variants. To determine if structural variation also occurs at the CYP2C locus, the 10q23.33 region was interrogated across deidentified clinical chromosomal microarray (CMA) data from 20,642 patients tested at two academic medical centers. Fourteen copy number variants that affected the coding region of CYP2C genes were detected in the clinical CMA cohorts, which ranged in size from 39.2 to 1,043.3 kb. Selected deletions and duplications were confirmed by MLPA or ddPCR. Analysis of the clinical CMA and an additional 78,839 cases from the Database of Genomic Variants (DGV) and ClinGen (total n = 99,481) indicated that the carrier frequency of a CYP2C structural variant is ~1 in 1,000, with ~1 in 2,000 being a CYP2C19 full gene or partial-gene deletion carrier, designated by PharmVar as CYP2C19*36 and *37, respectively. Although these structural variants are rare in the general population, their detection will likely improve metabolizer phenotype prediction when interrogated for research and/or clinical testing.

Innovative strategies for annotating the "relationSNP" between variants and molecular phenotypes

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.

Real world scenarios in rare variant association analysis: the impact of imbalance and sample size on the power in silico

Background

The development of sequencing techniques and statistical methods provides great opportunities for identifying the impact of rare genetic variation on complex traits. However, there is a lack of knowledge on the impact of sample size, case numbers, the balance of cases vs controls for both burden and dispersion based rare variant association methods. For example, Phenome-Wide Association Studies may have a wide range of case and control sample sizes across hundreds of diagnoses and traits, and with the application of statistical methods to rare variants, it is important to understand the strengths and limitations of the analyses.

Results

We conducted a large-scale simulation of randomly selected low-frequency protein-coding regions using twelve different balanced samples with an equal number of cases and controls as well as twenty-one unbalanced sample scenarios. We further explored statistical performance of different minor allele frequency thresholds and a range of genetic effect sizes. Our simulation results demonstrate that using an unbalanced study design has an overall higher type I error rate for both burden and dispersion tests compared with a balanced study design. Regression has an overall higher type I error with balanced cases and controls, while SKAT has higher type I error for unbalanced case-control scenarios. We also found that both type I error and power were driven by the number of cases in addition to the case to control ratio under large control group scenarios. Based on our power simulations, we observed that a SKAT analysis with case numbers larger than 200 for unbalanced case-control models yielded over 90% power with relatively well controlled type I error. To achieve similar power in regression, over 500 cases are needed. Moreover, SKAT showed higher power to detect associations in unbalanced case-control scenarios than regression.

Conclusions

Our results provide important insights into rare variant association study designs by providing a landscape of type I error and statistical power for a wide range of sample sizes. These results can serve as a benchmark for making decisions about study design for rare variant analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2591-6) contains supplementary material, which is available to authorized users.

Genomic and Phenomic Research in the 21st Century

The field of human genomics has changed dramatically over time. Initial genomic studies were predominantly restricted to rare disorders in small families. Over the past decade, researchers changed course from family-based studies and instead focused on common diseases and traits in populations of unrelated individuals. With further advancements in biobanking, computer science, electronic health record (EHR) data, and more affordable high-throughput genomics, we are experiencing a new paradigm in human genomic research. Rapidly changing technologies and resources now make it possible to study thousands of diseases simultaneously at the genomic level. This review will focus on these advancements as scientists begin to incorporate phenome-wide strategies in human genomic research to understand the etiology of human diseases and develop new drugs to treat them.