Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants

Non-coding variants in the human genome greatly influence some traits and complex diseases by their own regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in the human non-coding sequences. However, it is difficult for users with insufficient knowledge about the performances of computational methods to select appropriate computational methods from dozens of methods. In order to solve this problem, we assessed 12 performance measures of 24 methods on four independent non-coding variant benchmark datasets: (Ⅰ) rare germline variant from ClinVar, (Ⅱ) rare somatic variant from COSMIC, (Ⅲ) common regulatory variant dataset, and (Ⅳ) disease associated common variant dataset. All 24 tested methods performed differently under various conditions, indicating that these methods have varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable in the rare germline variant from ClinVar with area under curves (AUCs) of 0.4481 - 0.8033 and poor in the rare somatic variant from COSMIC (AUCs: 0.4984 - 0.7131), common regulatory variant dataset (AUCs: 0.4837 - 0.6472), and disease associated common variant dataset (AUCs: 0.4766 -0.5188). We also compared the prediction performance among 24 methods for non-coding de novo mutations in autism spectrum disorder and found that the CADD and CDTS methods showed better performance. Summarily, we assessed the performances of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and new method development in interpreting non-coding variants.

UGT1A1 regulatory variant with potential effect on efficacy of HIV and cancer drugs commonly prescribed in South Africa

Aim: Despite the high disease burden of human immunodeficiency virus (HIV) infection and colorectal cancer (CRC) in South Africa (SA), treatment-relevant pharmacogenetic variants are understudied. Materials & methods: Using publicly available genotype and gene expression data, a bioinformatic pipeline was developed to identify liver expression quantitative trait loci (eQTLs). Results: A novel cis-eQTL, rs28967009, was identified for UGT1A1, which is predicted to upregulate UGT1A1 expression thereby potentially affecting the metabolism of dolutegravir and irinotecan, which are extensively prescribed in SA for HIV and colorectal cancer treatment, respectively. Conclusion: As increased UGT1A1 expression could affect the clinical outcome of dolutegravir and irinotecan treatment by increasing drug clearance, patients with the rs28967009A variant may require increased drug doses to reach therapeutic levels or should be prescribed alternative drugs.

LTA4H rs2660845 association with montelukast response in early and late-onset asthma

Leukotrienes play a central pathophysiological role in both paediatric and adult asthma. However, 35% to 78% of asthmatics do not respond to leukotriene inhibitors. In this study we tested the role of the LTA4H regulatory variant rs2660845 and age of asthma onset in response to montelukast in ethnically diverse populations. We identified and genotyped 3,594 asthma patients treated with montelukast (2,514 late-onset and 1,080 early-onset) from seven cohorts (UKBiobank, GoSHARE, BREATHE, Tayside RCT, PAGES, GALA II and SAGE). Individuals under montelukast treatment experiencing at least one exacerbation in a 12-month period were compared against individuals with no exacerbation, using logistic regression for each cohort and meta-analysis. While no significant association was found with European late-onset subjects, a meta-analysis of 523 early-onset individuals from European ancestry demonstrated the odds of experiencing asthma exacerbations by carriers of at least one G allele, despite montelukast treatment, were increased (odds-ratio = 2.92, 95%confidence interval (CI): 1.04–8.18, I2 = 62%, p = 0.0412) compared to those in the AA group. When meta-analysing with other ethnic groups, no significant increased risk of asthma exacerbations was found (OR = 1.60, 95% CI: 0.61–4.19, I2 = 85%, p = 0.342). Our study demonstrates that genetic variation in LTA4H, together with timing of asthma onset, may contribute to variability in montelukast response. European individuals with early-onset (≤18y) carrying at least one copy of rs2660845 have increased odd of exacerbation under montelukast treatment, presumably due to the up-regulation of LTA4H activity. These findings support a precision medicine approach for the treatment of asthma with montelukast.

A semisupervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays

Motivation Predicting regulatory effects of genetic variants is a challenging but important problem in functional genomics. Given the relatively low sensitivity of functional assays, and the pervasiveness of class imbalance in functional genomic data, popular statistical prediction models can sharply underestimate the probability of a regulatory effect. We describe here the presence-only model (PO-EN), a type of semisupervised model, to predict regulatory effects of genetic variants at sequence-level resolution in a context of interest by integrating a large number of epigenetic features and massively parallel reporter assays (MPRAs). Results Using experimental data from a variety of MPRAs we show that the presence-only model produces better calibrated predicted probabilities and has increased accuracy relative to state-of-the-art prediction models. Furthermore, we show that the predictions based on pretrained PO-EN models are useful for prioritizing functional variants among candidate eQTLs and significant SNPs at GWAS loci. In particular, for the costimulatory locus, associated with multiple autoimmune diseases, we show evidence of a regulatory variant residing in an enhancer 24.4 kb downstream of CTLA4, with evidence from capture Hi-C of interaction with CTLA4. Furthermore, the risk allele of the regulatory variant is on the same risk increasing haplotype as a functional coding variant in exon 1 of CTLA4, suggesting that the regulatory variant acts jointly with the coding variant leading to increased risk to disease. Availability and implementation The presence-only model is implemented in the R package ‘PO.EN’, freely available on CRAN. A vignette describing a detailed demonstration of using the proposed PO-EN model can be found on github at https://github.com/Iuliana-Ionita-Laza/PO.EN/ Supplementary information Supplementary data are available at Bioinformatics online.