Estimating colocalization probability from limited summary statistics.

BACKGROUND: Colocalization is a statistical method used in genetics to determine whether the same variant is causal for multiple phenotypes, for example, complex traits and gene expression. It provides stronger mechanistic evidence than shared significance, which can be produced through separate causal variants in linkage disequilibrium. Current colocalization methods require full summary statistics for both traits, limiting their use with the majority of reported GWAS associations (e.g. GWAS Catalog). We propose a new approximation to the popular coloc method that can be applied when limited summary statistics are available. Our method (POint EstiMation of Colocalization, POEMColoc) imputes missing summary statistics for one or both traits using LD structure in a reference panel, and performs colocalization using the imputed summary statistics. RESULTS: We evaluate the performance of POEMColoc using real (UK Biobank phenotypes and GTEx eQTL) and simulated datasets. We show good correlation between posterior probabilities of colocalization computed from imputed and observed datasets and similar accuracy in simulation. We evaluate scenarios that might reduce performance and show that multiple independent causal variants in a region and imputation from a limited subset of typed variants have a larger effect while mismatched ancestry in the reference panel has a modest effect. Further, we find that POEMColoc is a better approximation of coloc when the imputed association statistics are from a well powered study (e.g., relatively larger sample size or effect size). Applying POEMColoc to estimate colocalization of GWAS Catalog entries and GTEx eQTL, we find evidence for colocalization of 150,000 trait-gene-tissue triplets. CONCLUSIONS: We find that colocalization analysis performed with full summary statistics can be closely approximated when only the summary statistics of the top SNP are available for one or both traits. When applied to the full GWAS Catalog and GTEx eQTL, we find that colocalized trait-gene pairs are enriched in tissues relevant to disease etiology and for matches to approved drug mechanisms. POEMColoc R package is available at https://github.com/AbbVie-ComputationalGenomics/POEMColoc .

Targeting BCL-2 with venetoclax and dexamethasone in patients with relapsed/refractory t(11;14) multiple myeloma.

Venetoclax (Ven) is a selective small-molecule inhibitor of BCL-2 that exhibits antitumoral activity against MM cells with t(11;14) translocation. We evaluated the safety and efficacy of Ven and dexamethasone (VenDex) combination in patients with t(11;14) positive relapsed/refractory (R/R) multiple myeloma (MM). This open-label, multicenter study had two distinct phases (phase one [P1], phase two [P2]). Patients in both phases received VenDex (oral Ven 800 mg/day + oral Dex 40 mg [20 mg for patients ≥75 years] on days 1, 8, and 15, per 21-day cycle). The primary objective of the P1 VenDex cohort was to assess safety and pharmacokinetics. Phase two further evaluated efficacy with objective response rate (ORR) and very good partial response or better. Correlative studies explored baseline BCL2 (BCL-2) and BCL2L1 (BCL-XL ) gene expression, cytogenetics, and recurrent somatic mutations in MM. Twenty and 31 patients in P1 and P2 with t(11;14) positive translocation received VenDex. P1/P2 patients had received a median of 3/5 lines of prior therapy, and 20%/87% were refractory to daratumumab. Predominant grade 3/4 hematological adverse events (AEs) with ≥10% occurrence included lymphopenia (20%/19%), neutropenia (15%/7%), thrombocytopenia (10%/10%), and anemia (5%/16%). At a median follow-up of 12.3/9.2 months, ORR was 60%/48%. The duration of response estimate at 12 months was 50%/61%, and the median time to progression was 12.4/10.8 months. In biomarker evaluable patients, response to VenDex was independent of concurrent del(17p) or gain(1q) and mutations in key oncogenic signaling pathways, including MAPK and NF-kB. VenDex demonstrated efficacy and manageable safety in heavily-pre-treated patients with t(11;14) R/R MM.

Detection of Differentially Methylated Regions Using Bayes Factor for Ordinal Group Responses.

Researchers in genomics are increasingly interested in epigenetic factors such as DNA methylation, because they play an important role in regulating gene expression without changes in the DNA sequence. There have been significant advances in developing statistical methods to detect differentially methylated regions (DMRs) associated with binary disease status. Most of these methods are being developed for detecting differential methylation rates between cases and controls. We consider multiple severity levels of disease, and develop a Bayesian statistical method to detect the region with increasing (or decreasing) methylation rates as the disease severity increases. Patients are classified into more than two groups, based on the disease severity (e.g., stages of cancer), and DMRs are detected by using moving windows along the genome. Within each window, the Bayes factor is calculated to test the hypothesis of monotonic increase in methylation rates corresponding to severity of the disease versus no difference. A mixed-effect model is used to incorporate the correlation of methylation rates of nearby CpG sites in the region. Results from extensive simulation indicate that our proposed method is statistically valid and reasonably powerful. We demonstrate our approach on a bisulfite sequencing dataset from a chronic lymphocytic leukemia (CLL) study.