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.

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.

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.

Abstract 30: A pedigree-based approach to ovarian cancer risk variant discovery in the Utah Population Database

Introduction: Genetic risk variants are critical to risk prediction for invasive epithelial ovarian cancer (EOC), yet an estimated 50-55% of EOC heritability remains missing. Novel approaches to risk variant discovery are needed to detect variants that have not been uncovered by linkage analysis or genome-wide association studies (GWAS). We approach risk variant discovery using Shared Genomic Segment analysis (SGS), a novel statistical genetics method that enables risk variant discovery in extended high-risk pedigrees.

Methods: The Utah Population Database (UPDB) is a population-based resource of over ten million individuals connected to the state of Utah, five million of whom are linked to a minimum of three generations of genealogy data. By linking this genealogy data with cancer data from the Utah Cancer Registry, we identified pedigrees with an excess risk of EOC. We generated germline genotyping data from cases (diagnosed 1983-2018) in a subset of these high-risk pedigrees, focusing on pedigrees well-suited for SGS (i.e., at least 3 cases separated by a total of at least 12 meioses). SGS identifies all runs of consecutive alleles that are shared identical-by-state by cases in a pedigree and determines which of these genomic segments are longer than would be expected by chance. These long segments are likely to be identical-by-descent, inherited from a common founder, and may harbor risk variants.

Results: We successfully linked UPDB genealogy data to information from the Utah Cancer Registry to identify approximately 2,000 extended high-risk pedigrees with ≥5 ovarian cancer cases, an average of 7-10 meioses between pairs cases, and a statistically significant excess risk of ovarian cancer (α=0.05). We obtained biospecimens for 141 cases in 36 of the most promising pedigrees (i.e., high familial standardized incidence ratio, p-value<0.005, not attributable to known risk variants in BRCA1 or BRCA2 as observed in first-, second- or third-degree relatives of EOC cases). We genotyped 96 out of the 141 cases in these pedigrees (68%). SGS analyses to identify genomic regions that may harbor risk variants are currently underway.

Conclusions: Using pedigrees identified in the UPBD, SGS has identified risk loci and novel risk variants for a number of diseases, including breast and hematologic cancers. We have identified a set of EOC high-risk pedigrees for SGS analysis and will leverage SGS to identify genomic regions that may harbor EOC risk variants.

Citation Format: Mollie E. Barnard, Robert Sargent, G. Bryce Christensen, Bonita A. Mahaffey, James Albro, Elke A. Jarboe, Terence Rhodes, Nicola J. Camp, Jennifer A. Doherty. A pedigree-based approach to ovarian cancer risk variant discovery in the Utah Population Database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 30.

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.

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.

The sumLINK statistic for genetic linkage analysis in the presence of heterogeneity

We present the "sumLINK" statistic--the sum of multipoint LOD scores for the subset of pedigrees with nominally significant linkage evidence at a given locus--as an alternative to common methods to identify susceptibility loci in the presence of heterogeneity. We also suggest the "sumLOD" statistic (the sum of positive multipoint LOD scores) as a companion to the sumLINK. sumLINK analysis identifies genetic regions of extreme consistency across pedigrees without regard to negative evidence from unlinked or uninformative pedigrees. Significance is determined by an innovative permutation procedure based on genome shuffling that randomizes linkage information across pedigrees. This procedure for generating the empirical null distribution may be useful for other linkage-based statistics as well. Using 500 genome-wide analyses of simulated null data, we show that the genome shuffling procedure results in the correct type 1 error rates for both the sumLINK and sumLOD. The power of the statistics was tested using 100 sets of simulated genome-wide data from the alternative hypothesis from GAW13. Finally, we illustrate the statistics in an analysis of 190 aggressive prostate cancer pedigrees from the International Consortium for Prostate Cancer Genetics, where we identified a new susceptibility locus. We propose that the sumLINK and sumLOD are ideal for collaborative projects and meta-analyses, as they do not require any sharing of identifiable data between contributing institutions. Further, loci identified with the sumLINK have good potential for gene localization via statistical recombinant mapping, as, by definition, several linked pedigrees contribute to each peak.

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.

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.

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.

Genome-wide linkage analysis for aggressive prostate cancer in Utah high-risk pedigrees

BACKGROUND

It has been proposed that studying alternative phenotypes, such as tumor aggressiveness, may be a solution for overcoming the apparent heterogeneity that has hindered the identification of prostate cancer (PC) genes. We present the results of a genome-scan for predisposition to aggressive PC using the Utah high-risk pedigree resource.

METHODS

We identified 259 subjects with aggressive PC in 57 extended and nuclear families. Parametric and non-parametric multipoint linkage statistics were calculated for a genome-wide set of 401 microsatellite markers using the MCLINK software package. Stratification analyses by the number of affected subjects per pedigree (<5, >or=5) and the average age at diagnosis of affected subjects (<70 years, >or=70 years) were also performed.

RESULTS

No significant results were observed at the genome-wide level, but suggestive evidence for linkage was observed on chromosomes 9q (HLOD = 2.04) and 14q (HLOD = 2.08); several pedigrees showed individual evidence for linkage at each locus (LOD > 0.58). The subset of pedigrees with earlier age at onset demonstrated nominal linkage evidence on chromosomes 3q (HLOD = 1.79), 8q (HLOD = 1.67), and 20q (HLOD=1.82). The late-onset subset showed suggestive linkage on chromosome 6p (HLOD = 2.37) and the subset of pedigrees with fewer than five affected subjects showed suggestive linkage on chromosome 10p (HLOD = 1.99).

CONCLUSIONS

Linkage evidence observed on chromosomes 6p, 8q, and 20q support previously reported PC aggressiveness loci. While these results are encouraging, further research is necessary to identify the gene or genes responsible for PC aggressiveness and surmount the overarching problem of PC heterogeneity.

[Bilateral shoulder fractures following generalized seizure]

A 37 year-old man with bilateral shoulder fractures and posterior dislocation of one shoulder following a tonic-clonic seizure is presented. The seizure was most likely caused by alcohol withdrawal. The diagnosis was not suspected initially. X-ray and CT-scan led to the diagnosis. The relevant literature is reviewed with special focus on causes, trauma mechanisms, symptoms and signs and the pitfalls responsible for missing the diagnosis.