Genotyping Array Design and Data Quality Control in the Million Veteran Program.

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect biosamples with consent from at least one million veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records, make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide a genome-wide scan of the entire cohort, in parallel with whole-genome sequencing, methylation, and other 'omics assays. Here, we present the design and performance of the MVP 1.0 custom Axiom array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality-control analysis was developed and conducted on an initial tranche of 485,856 individuals, leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high-quality genotypes not only on common variants but also on rare variants. We confirmed that, with non-European individuals making up nearly 30%, MVP's substantial ancestral diversity surpasses that of other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current dataset has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

Fast and accurate genomic analyses using genome graphs.

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, thus impairing analysis accuracy. Here we present a graph reference genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million insertions and deletions (indels). The pipeline processes one whole-genome sequencing sample in 6.5 h using a system with 36 CPU cores. We show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, with unaffected specificity. Structural variations incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is an important advance toward fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

Factors Associated With Outcomes of Patients With Primary Sclerosing Cholangitis and Development and Validation of a Risk Scoring System.

We sought to identify factors that are predictive of liver transplantation or death in patients with primary sclerosing cholangitis (PSC), and to develop and validate a contemporaneous risk score for use in a real-world clinical setting. Analyzing data from 1,001 patients recruited to the UK-PSC research cohort, we evaluated clinical variables for their association with 2-year and 10-year outcome through Cox-proportional hazards and C-statistic analyses. We generated risk scores for short-term and long-term outcome prediction, validating their use in two independent cohorts totaling 451 patients. Thirty-six percent of the derivation cohort were transplanted or died over a cumulative follow-up of 7,904 years. Serum alkaline phosphatase of at least 2.4 × upper limit of normal at 1 year after diagnosis was predictive of 10-year outcome (hazard ratio [HR] = 3.05; C = 0.63; median transplant-free survival 63 versus 108 months; P < 0.0001), as was the presence of extrahepatic biliary disease (HR = 1.45; P = 0.01). We developed two risk scoring systems based on age, values of bilirubin, alkaline phosphatase, albumin, platelets, presence of extrahepatic biliary disease, and variceal hemorrhage, which predicted 2-year and 10-year outcomes with good discrimination (C statistic = 0.81 and 0.80, respectively). Both UK-PSC risk scores were well-validated in our external cohort and outperformed the Mayo Clinic and aspartate aminotransferase-to-platelet ratio index (APRI) scores (C statistic = 0.75 and 0.63, respectively). Although heterozygosity for the previously validated human leukocyte antigen (HLA)-DR*03:01 risk allele predicted increased risk of adverse outcome (HR = 1.33; P = 0.001), its addition did not improve the predictive accuracy of the UK-PSC risk scores. Conclusion: Our analyses, based on a detailed clinical evaluation of a large representative cohort of participants with PSC, furthers our understanding of clinical risk markers and reports the development and validation of a real-world scoring system to identify those patients most likely to die or require liver transplantation.

Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease.

Genetic association studies have identified 215 risk loci for inflammatory bowel disease, thereby uncovering fundamental aspects of its molecular biology. We performed a genome-wide association study of 25,305 individuals and conducted a meta-analysis with published summary statistics, yielding a total sample size of 59,957 subjects. We identified 25 new susceptibility loci, 3 of which contain integrin genes that encode proteins in pathways that have been identified as important therapeutic targets in inflammatory bowel disease. The associated variants are correlated with expression changes in response to immune stimulus at two of these genes (ITGA4 and ITGB8) and at previously implicated loci (ITGAL and ICAM1). In all four cases, the expression-increasing allele also increases disease risk. We also identified likely causal missense variants in a gene implicated in primary immune deficiency, PLCG2, and a negative regulator of inflammation, SLAMF8. Our results demonstrate that new associations at common variants continue to identify genes relevant to therapeutic target identification and prioritization.

Genome-wide association study of primary sclerosing cholangitis identifies new risk loci and quantifies the genetic relationship with inflammatory bowel disease.

Primary sclerosing cholangitis (PSC) is a rare progressive disorder leading to bile duct destruction; ∼75% of patients have comorbid inflammatory bowel disease (IBD). We undertook the largest genome-wide association study of PSC (4,796 cases and 19,955 population controls) and identified four new genome-wide significant loci. The most associated SNP at one locus affects splicing and expression of UBASH3A, with the protective allele (C) predicted to cause nonstop-mediated mRNA decay and lower expression of UBASH3A. Further analyses based on common variants suggested that the genome-wide genetic correlation (rG) between PSC and ulcerative colitis (UC) (rG = 0.29) was significantly greater than that between PSC and Crohn's disease (CD) (rG = 0.04) (P = 2.55 × 10-15). UC and CD were genetically more similar to each other (rG = 0.56) than either was to PSC (P < 1.0 × 10-15). Our study represents a substantial advance in understanding of the genetics of PSC.

Network-assisted investigation of virulence and antibiotic-resistance systems in Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a Gram-negative bacterium of clinical significance. Although the genome of PAO1, a prototype strain of P. aeruginosa, has been extensively studied, approximately one-third of the functional genome remains unknown. With the emergence of antibiotic-resistant strains of P. aeruginosa, there is an urgent need to develop novel antibiotic and anti-virulence strategies, which may be facilitated by an approach that explores P. aeruginosa gene function in systems-level models. Here, we present a genome-wide functional network of P. aeruginosa genes, PseudomonasNet, which covers 98% of the coding genome, and a companion web server to generate functional hypotheses using various network-search algorithms. We demonstrate that PseudomonasNet-assisted predictions can effectively identify novel genes involved in virulence and antibiotic resistance. Moreover, an antibiotic-resistance network based on PseudomonasNet reveals that P. aeruginosa has common modular genetic organisations that confer increased or decreased resistance to diverse antibiotics, which accounts for the pervasiveness of cross-resistance across multiple drugs. The same network also suggests that P. aeruginosa has developed mechanism of trade-off in resistance across drugs by altering genetic interactions. Taken together, these results clearly demonstrate the usefulness of a genome-scale functional network to investigate pathogenic systems in P. aeruginosa.