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We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis. A multi-ancestry genome-wide association study of liver cirrhosis and its associated endophenotypes identifies and validates 14 risk variants. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis.

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683–0.730] versus 0.740 [0.717–0.762] and 1-year: 0.691 [0.673–0.710] versus 0.714 [0.695–0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.

This multi-ancestry meta-analysis of genome-wide association studies (GWAS) investigated the genetic factors underlying chronic back pain (CBP) in a sample from the Million Veteran Program comprised of 553,601 Veterans of African (19.2%), European (72.6%), and Hispanic (8.2%) ancestry. The results revealed novel ( N  = 67) and known ( N  = 20) genome-wide significant loci associated with CBP, with 43 independent variants replicating in a non-overlapping contemporary meta-GWAS of the spinal pain dorsalgia phenotype. The most significant novel variant was rs12533005 (chr7:114416000, p  = 1.61 × 10 −20 , OR = 0.96 (95% CI: 0.95–0.97), EA = C, EAF = 0.39), in an intron of the FOXP2 gene. In silico functional characterization revealed enrichment in brain and pituitary tissues. Mendelian randomization analysis of 62 variants for CBP-MVP revealed 48 with causal links to dorsalgia. Notably, four genes ( INPP5B, DRD2, HTT, SLC30A6 ) associated with these variants are targets of existing drugs. Our findings more than double the number of previously reported genetic predictors across all spinal pain phenotypes. A multi-ancestry genome-wide association study meta-analysis of chronic back pain in 553,601 participants from the Million Veteran Program revealed 67 new genome-wide significant loci with gene enrichment in brain and pituitary tissues.

Chronic prostatitis/chronic pelvic pain syndrome is common, and it impacts men’s health and quality of life. The genetic basis of this condition remains largely unknown. Here, we conduct a GWAS using data from the Million Veteran Program of over 590,000 men of European, African, and Hispanic ancestry, including 14,575 chronic prostatitis/chronic pelvic pain syndrome cases. The multi-ancestry analysis identifies eight novel loci associated with chronic prostatitis/chronic pelvic pain syndrome risk, an increase from three significant genome-wide loci found in the European participants alone. We also estimate the genetic correlations between chronic prostatitis/chronic pelvic pain syndrome and 12 phenotypes. Notably, the genetic correlation between chronic prostatitis/chronic pelvic pain syndrome and prostate cancer is not significant. Further, Mendelian randomization shows a significant, potentially bidirectional causal relationship between chronic prostatitis/chronic pelvic pain syndrome and benign prostatic hyperplasia, but not between chronic prostatitis/chronic pelvic pain syndrome and prostate cancer, suggesting a complex interplay between chronic prostatitis/chronic pelvic pain syndrome and benign prostatic hyperplasia. Results of bivariate causal mixture modeling indicate that some of the same genetic variants likely contribute to the development of chronic prostatitis/chronic pelvic pain syndrome, benign prostatic hyperplasia, and prostate cancer. The genetic basis of chronic prostatitis/chronic pelvic pain syndrome remains poorly understood. Here, the authors present a multi-ancestry GWAS of this syndrome, identifying eight loci linked with higher risk.

We present allele frequencies of pharmacogenomics relevant variants across multiple ancestry in a sample representative of the US population. We analyzed 658,582 individuals with genotype data and extracted pharmacogenomics relevant single nucleotide variant (SNV) alleles, human leukocyte antigens (HLA) 4-digit alleles and an important copy number variant (CNV), the full deletion/duplication of CYP2D6 . We compiled distinct allele frequency tables for European, African American, Hispanic, and Asian ancestry individuals. In addition, we compiled allele frequencies based on local ancestry reconstruction in the African-American (2-way deconvolution) and Hispanic (3-way deconvolution) cohorts.

We present allele frequencies of pharmacogenomics relevant variants across multiple ancestry in a sample representative of the US population. We analyzed 658,582 individuals with genotype data and extracted pharmacogenomics relevant single nucleotide variant (SNV) alleles, human leukocyte antigens (HLA) 4-digit alleles and an important copy number variant (CNV), the full deletion/duplication of CYP2D6. We compiled distinct allele frequency tables for European, African American, Hispanic, and Asian ancestry individuals. In addition, we compiled allele frequencies based on local ancestry reconstruction in the African-American (2-way deconvolution) and Hispanic (3-way deconvolution) cohorts.

Cases for a disease can be defined broadly using diagnostic codes, or narrowly using gold-standard confirmation that often is not available in large administrative datasets. These different definitions can have significant impacts on the results and conclusions of studies. We conducted this study to assess how using melanoma phecodes versus histologic confirmation for invasive or in situ melanoma impacts the results of a genome-wide association study (GWAS) using the Million Veteran Program. Melanoma status was determined three ways: (1) by the presence of two or more phecodes, (2) histologically-confirmed invasive melanoma, and (3) histologically-confirmed melanoma in situ. We conducted a GWAS for variants with minor allele frequencies of 1% or greater. There were 45,665 cases in the phecode cohort, 5364 cases in the confirmed invasive melanoma cohort, and 4792 cases in the confirmed melanoma in situ cohort. There were 20,457 variants significant at the genome-wide level in the phecode cohort, 2582 in the invasive melanoma cohort, and 1989 in the melanoma in situ cohort. Most of the variants identified in the phecode cohort did not replicate in the histologically-confirmed cohorts. The different case definitions led to large differences in sample size and variants associated at the genome-wide level. Unvalidated and imprecise case definitions can lead to less accurate results. Investigators should use validated phenotypes when gold-standard definitions are not available.