Paving a pathway for large-scale utilization of genomics in precision medicine and population health.

Having worked with two large population sequencing initiatives, the separation between the potential for genomics in precision medicine and the current reality have become clear. To realize this potential requires workflows, policies, and technical architectures that are foreign to most healthcare systems. Many historical processes and regulatory barriers currently impede our progress. The future of precision medicine includes genomic data being widely available at the point of care with systems in place to manage its efficient utilization. To achieve such vision requires substantial changes in billing, reimbursement, and reporting as well as the development of new systemic and technical architectures within the healthcare system. Clinical geneticist roles will evolve into managing precision health frameworks and genetic counselors will serve crucial roles in both leading and supporting precision medicine through the implementation and maintenance of precision medicine architectures. Our current path has many obstacles that hold us back, leaving preventable deaths in the wake. Reengineering our healthcare systems to support genomics can have a major impact on patient outcomes and allow us to realize the long-sought promises of precision medicine.

HerediGene Population Study IT infrastructure: A model to support genomic research recruitment and precision public health.

The HerediGene Population Study is a large research study focused on identifying new genetic biomarkers for disease prevention, diagnosis, prognosis, and development of new therapeutics. A substantial IT infrastructure evolved to reach enrollment targets and return results to participants. More than 170,000 participants have been enrolled in the study to date, with 5.87% of those whole genome sequenced and 0.46% of those genotyped harboring pathogenic variants. Among other purposes, this infrastructure supports: (1) identifying candidates from clinical criteria, (2) monitoring for qualifying clinical events (e.g., blood draw), (3) contacting candidates, (4) obtaining consent electronically, (5) initiating lab orders, (6) integrating consent and lab orders into clinical workflow, (7) de-identifying samples and clinical data, (8) shipping/transmitting samples and clinical data, (9) genotyping/sequencing samples, (10) and re-identifying and returning results for participants where applicable. This study may serve as a model for similar genomic research and precision public health initiatives.

The Development of an Infrastructure to Facilitate the Use of Whole Genome Sequencing for Population Health.

The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.

Discovery of TITIN Gene Truncating Variant Mutations and 5-Year Outcomes in Patients With Nonischemic Dilated Cardiomyopathy.

Genetic factors play an important role in nonischemic dilated cardiomyopathy (NIDC). However, prime opportunities remain for genetic discovery and prognostic understanding. TITIN gene truncating variant mutations (TTNtv) are of interest because of their frequent appearance in NIDC series. We sought to discover known and novel TTNtv mutations in a NIDC cohort and assess 5-year outcomes. Patients with NIDC entered into the INSPIRE Registry with ≥3 years of follow-up were studied. Whole exome sequencing (WES) was performed using an Illumina Novaseq platform. Genetic analysis used Sentieon software and the GRCh38 human reference genome. Variant calls were annotated with ClinVar. Five-year outcomes were determined by functional assessment and ejection fraction (EF) as recovered (EF ≥50%), persistent (EF 21% to 49%), or progressive (left ventricular assist device, transplant, heart failure [HF] or arrhythmic death, or EF ≤20%). The study comprised 229 NIDC patients (age = 50 ± 15 years, 58% men). TTNtv's were discovered in 27 patients with 22 unique mutations; (7 known, 15 novel). TTNtv+ patients more frequently presented with severe NIDC (EF ≤20%) (p = 0.032). By 5-year, outcomes were worse in TTNtv+ patients (p = 0.027), and patients less often recovered (11% vs. 30%). Prognosis was similar with known and novel mutations. Nongenetic (e.g., environmental) cocausal risk factors for HF were frequently present, and these factors frequently appeared to act in concert with genetic variants to precipitate clinical HF. In conclusion, our study expands the library of likely pathogenic TTN mutations and increases our understanding of their clinical impact in association with other HF risk factors.

Concepts and relevance of genome-wide association studies.

Genome-wide association study (GWAS) technology has been a primary method for identifying the genes responsible for diseases and other traits for the past 10 years. Over 2,000 human GWAS reports now appear in the scientific journals. The technology is continuing to improve, and has recently become accessible to researchers studying a wide variety of animals, plants and model organisms. Here, we present an overview of GWAS concepts: the underlying biology, the origins of the method, and the primary components of a GWAS experiment.

Identification of rare recurrent copy number variants in high-risk autism families and their prevalence in a large ASD population.

Structural variation is thought to play a major etiological role in the development of autism spectrum disorders (ASDs), and numerous studies documenting the relevance of copy number variants (CNVs) in ASD have been published since 2006. To determine if large ASD families harbor high-impact CNVs that may have broader impact in the general ASD population, we used the Affymetrix genome-wide human SNP array 6.0 to identify 153 putative autism-specific CNVs present in 55 individuals with ASD from 9 multiplex ASD pedigrees. To evaluate the actual prevalence of these CNVs as well as 185 CNVs reportedly associated with ASD from published studies many of which are insufficiently powered, we designed a custom Illumina array and used it to interrogate these CNVs in 3,000 ASD cases and 6,000 controls. Additional single nucleotide variants (SNVs) on the array identified 25 CNVs that we did not detect in our family studies at the standard SNP array resolution. After molecular validation, our results demonstrated that 15 CNVs identified in high-risk ASD families also were found in two or more ASD cases with odds ratios greater than 2.0, strengthening their support as ASD risk variants. In addition, of the 25 CNVs identified using SNV probes on our custom array, 9 also had odds ratios greater than 2.0, suggesting that these CNVs also are ASD risk variants. Eighteen of the validated CNVs have not been reported previously in individuals with ASD and three have only been observed once. Finally, we confirmed the association of 31 of 185 published ASD-associated CNVs in our dataset with odds ratios greater than 2.0, suggesting they may be of clinical relevance in the evaluation of children with ASDs. Taken together, these data provide strong support for the existence and application of high-impact CNVs in the clinical genetic evaluation of children with ASD.

Search for compound heterozygous effects in exome sequence of unrelated subjects.

To enable the assessment of compound heterozygosity, we propose a simple approach for incorporating genotype phase in a rare variant collapsing procedure for the analysis of DNA sequence data. When multiple variants are identified within a gene, knowing the phase of each variant may provide additional statistical power to detect associations with phenotypes that follow a recessive or additive inheritance pattern. We begin by phasing all marker data; then, we collapse nonsynonymous single-nucleotide polymorphisms within genes on each phased haplotype, resulting in a single diploid genotype for each gene, which represents whether one or both haplotypes carry a nonsynonymous variant allele. A recessive or additive association test can then be used to assess the relationship between the collapsed genotype and the phenotype of interest. We apply this approach to the unrelated individuals data from Genetic Analysis Workshop 17 and compare the results of the additive test with a dominant test in which phase is not informative. Analysis of the first phenotype replicate shows that the FLT1 gene is significantly associated with both Q1 and the binary affection status phenotype. This association was detected by both the additive and dominant tests, although the additive phase-informed test resulted in a smaller p-value. No false-positive results were detected in the first phenotype replicate. Analysis of the average values of all phenotype replicates correctly identified five other genes important to the simulation, but with an increase in false-positive rates. The accuracy of our method is contingent on correct phase determination.

Genome-wide linkage analysis of 1,233 prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics using novel sumLINK and sumLOD analyses.

BACKGROUND: Prostate cancer (PC) is generally believed to have a strong inherited component, but the search for susceptibility genes has been hindered by the effects of genetic heterogeneity. The recently developed sumLINK and sumLOD statistics are powerful tools for linkage analysis in the presence of heterogeneity. METHODS: We performed a secondary analysis of 1,233 PC pedigrees from the International Consortium for Prostate Cancer Genetics (ICPCG) using two novel statistics, the sumLINK and sumLOD. For both statistics, dominant and recessive genetic models were considered. False discovery rate (FDR) analysis was conducted to assess the effects of multiple testing. RESULTS: Our analysis identified significant linkage evidence at chromosome 22q12, confirming previous findings by the initial conventional analyses of the same ICPCG data. Twelve other regions were identified with genome-wide suggestive evidence for linkage. Seven regions (1q23, 5q11, 5q35, 6p21, 8q12, 11q13, 20p11-q11) are near loci previously identified in the initial ICPCG pooled data analysis or the subset of aggressive PC pedigrees. Three other regions (1p12, 8p23, 19q13) confirm loci reported by others, and two (2p24, 6q27) are novel susceptibility loci. FDR testing indicates that over 70% of these results are likely true positive findings. Statistical recombinant mapping narrowed regions to an average of 9 cM. CONCLUSIONS: Our results represent genomic regions with the greatest consistency of positive linkage evidence across a very large collection of high-risk PC pedigrees using new statistical tests that deal powerfully with heterogeneity. These regions are excellent candidates for further study to identify PC predisposition genes.

Replication of the 10q11 and Xp11 prostate cancer risk variants: results from a Utah pedigree-based study.

A recent genome-wide association study suggested seven new loci as associated with prostate cancer susceptibility. The strongest associated single nucleotide polymorphism (SNP) in each region was identified (rs2660753, rs9364554, rs6465657, rs10993994, rs7931342, rs2735839, rs5945619). We studied these seven SNPs in a replication study consisting of 169 familial prostate cancer cases selected from Utah high-risk prostate cancer pedigrees and 805 controls. We performed subset analyses for aggressive and early-onset prostate cancer. At a nominal significance level, two SNPs were found to be associated with prostate cancer: rs10993994 on chromosome 10q11 [odds ratio (OR), 1.42; 95% confidence interval (95% CI), 1.05-1.90; P = 0.022] and rs5945619 on chromosome Xp11 (OR, 1.54; 95% CI, 1.03-2.31; P = 0.035). Restricting analysis to familial prostate cancer cases with aggressive disease yielded very similar risk estimates at both SNPs. However, subset analysis for familial, early-onset disease indicated highly significant association evidence and substantially higher risk estimates for rs10993994 (OR, 2.20; 95% CI, 1.48-3.27; P < 0.0001). This result suggests that the higher risk estimates from the stage 1 cohort in the original study for rs10993994 may have been due to the early-onset and familial nature of the prostate cancer cases in that cohort. In conclusion, in a small case-control study of prostate cancer cases from Utah high-risk pedigrees, we have significantly replicated association of prostate cancer with rs10993994 (10q11) upon study-wide correction for multiple comparisons. We also nominally replicated the association of prostate cancer with rs5945619 (Xp11). In particular, it seems that the susceptibility locus at 10q11 maybe involved in familial, early-onset disease.

Summary of Genetic Analysis Workshop 15: Group 9 linkage analysis of the CEPH expression data.

Group 9 participants carried out linkage analysis of the Centre d'Etude de Polymorphism Humain (CEPH) expression data, using strategies that ranged from focused investigation of a small number of traits to full genome scans of all available traits. Results from five key areas encompass the most important results within and across the 17 participating groups. First, both extensive genetic heterogeneity and poor predictability of mapping results based on heritability have key implications for study design. Second, choice of the map used for linkage analysis is influential, with the implication that meiotic maps are preferable to physical maps. Third, performance of different analytic methods was in general fairly consistent, with the exception of one variance-component method that uses marker allele sharing as the dependent rather than independent variable. Fourth, multivariate analysis approaches did not generally appear to provide advantages over univariate approaches for linkage detection. Finally, there were computational and analytic challenges in working with a large public data set, along with need for more data documentation.

Evaluation of genetic variations in the androgen and estrogen metabolic pathways as risk factors for sporadic and familial prostate cancer.

Previous studies suggest that enzymes involved in the androgen metabolic pathway are susceptibility factors for prostate cancer. Estrogen metabolites functioning as genotoxins have also been proposed as risk factors. In this study, we systematically tested the hypothesis that common genetic variations for those enzymes involved in the androgen and estrogen metabolic pathways increase risk for sporadic and familial prostate cancer. From these two pathways, 46 polymorphisms (34 single nucleotide polymorphisms, 10 short tandem repeat polymorphisms, and 2 null alleles) in 25 genes were tested for possible associations. Those genes tested included PRL, LHB, CYP11A1, HSD3B1, HSD3B2, HSD17B2, CYP17, SRD5A2, AKR1C3, UGT2B15, AR, SHBG, and KLK3 from the androgen pathway and CYP19, HSD17B1, CYP1A1, CYP1A2, CYP1B1, COMT, GSTP1, GSTT1, GSTM1, NQO1, ESR1, and ESR2 from the estrogen pathway. A case-control study design was used with two sets of cases: familial cases with a strong prostate cancer family history (n = 438 from 178 families) and sporadic cases with a negative prostate cancer family history (n = 499). The controls (n = 493) were derived from a population-based collection. Our results provide suggestive findings for an association with either familial or sporadic prostate cancer with polymorphisms in four genes: AKR1C3, HSD17B1, NQO1, and GSTT1. Additional suggestive findings for an association with clinical variables (disease stage, grade, and/or node status) were observed for single nucleotide polymorphisms in eight genes: HSD3B2, SRD5A2, SHBG, ESR1, CYP1A1, CYP1B1, GSTT1, and NQO1. However, none of the findings were statistically significant after appropriate corrections for multiple comparisons. Given that the point estimates for the odds ratio for each of these polymorphisms are <2.0, much larger sample sizes will be required for confirmation.

Extracting disease risk profiles from expression data for linkage analysis: application to prostate cancer.

The genetic factors underlying many complex traits are not well understood. The Genetic Analysis Workshop 15 Problem 1 data present the opportunity to explore whether gene expression data from microarrays can be utilized to define useful phenotypes for linkage analysis in complex diseases. We utilize expression profiles for multiple genes that have been associated with a disease to develop a composite 'risk profile' that can be used to map other loci involved in the same disease process. Using prostate cancer as our disease of interest, we identified 26 genes whose expression levels had previously been associated with prostate cancer and defined three phenotypes: high, neutral, or low risk profiles, based on individual expression levels. Linkage analyses using MCLINK, a Markov-chain Monte Carlo method, and MERLIN were performed for all three phenotypes. Both methods were in very close agreement. Genome-wide suggestive linkage evidence was observed on chromosomes 6 and 4. It was interesting to note that the linkage signals did not appear to be strongly influenced by the location of the original 26 genes used in the phenotype definition, indicating that composite measures may have potential to locate additional genes in the same process. In this example, however, extreme caution is necessary in any extrapolation of the identified loci to prostate cancer due to the lack of data regarding the behavior of these genes' expression level in lymphoblastoid cells. Our results do indicate there exists potential to augment our current knowledge about the relationships among genes associated with complex diseases using expression data.