Population screening shows risk of inherited cancer and familial hypercholesterolemia in Oregon.

The Healthy Oregon Project (HOP) is a statewide effort that aims to build a large research repository and influence the health of Oregonians through providing no-cost genetic screening to participants for a next-generation sequencing 32-gene panel comprising genes related to inherited cancers and familial hypercholesterolemia. This type of unbiased population screening can detect at-risk individuals who may otherwise be missed by conventional medical approaches. However, challenges exist for this type of high-throughput testing in an academic setting, including developing a low-cost high-efficiency test and scaling up the clinical laboratory for processing large numbers of samples. Modifications to our academic clinical laboratory including efficient test design, robotics, and a streamlined analysis approach increased our ability to test more than 1,000 samples per month for HOP using only one dedicated HOP laboratory technologist. Additionally, enrollment using a HIPAA-compliant smartphone app and sample collection using mouthwash increased efficiency and reduced cost. Here, we present our experience three years into HOP and discuss the lessons learned, including our successes, challenges, opportunities, and future directions, as well as the genetic screening results for the first 13,670 participants tested. Overall, we have identified 730 pathogenic/likely pathogenic variants in 710 participants in 24 of the 32 genes on the panel. The carrier rate for pathogenic/likely pathogenic variants in the inherited cancer genes on the panel for an unselected population was 5.0% and for familial hypercholesterolemia was 0.3%. Our laboratory experience described here may provide a useful model for population screening projects in other states.

Artificial intelligence (AI)-assisted exome reanalysis greatly aids in the identification of new positive cases and reduces analysis time in a clinical diagnostic laboratory.

PURPOSE: Artificial intelligence (AI) and variant prioritization tools for genomic variant analysis are being rapidly developed for use in clinical diagnostic testing. However, their clinical utility and reliability are currently limited. Therefore, we performed a validation of a commercial AI tool (Moon) and a comprehensive reanalysis of previously collected clinical exome sequencing cases using an open-source variant prioritization tool (Exomiser) and the now-validated AI tool to test their feasibility in clinical diagnostics. METHODS: A validation study of Moon was performed with 29 positive cases determined by previous manual analysis. After validation, reanalysis was performed on 80 previously manually analyzed nondiagnostic exome cases using Moon. Finally, a comparison between Moon and Exomiser was completed regarding their ability to identify previously completed positive cases and to identify new positive cases. RESULTS: Moon correctly selected the causal variant(s) in 97% of manually analyzed positive cases and identified 7 new positive cases. Exomiser correctly identified the causal gene in 85% of positive cases and agreed with Moon by ranking the new gene in its top 10 list 43% of the time. CONCLUSION: The use of AI in diagnostic laboratories greatly enhances exome sequencing analysis by reducing analysis time and increasing the diagnostic rate.

Whole-genome characterization in pedigreed non-human primates using genotyping-by-sequencing (GBS) and imputation.

BACKGROUND: Rhesus macaques are widely used in biomedical research, but the application of genomic information in this species to better understand human disease is still in its infancy. Whole-genome sequence (WGS) data in large pedigreed macaque colonies could provide substantial experimental power for genetic discovery, but the collection of WGS data in large cohorts remains a formidable expense. Here, we describe a cost-effective approach that selects the most informative macaques in a pedigree for 30X WGS, followed by low-cost genotyping-by-sequencing (GBS) at 30X on the remaining macaques in order to generate sparse genotype data at high accuracy. Dense variants from the selected macaques with WGS data are then imputed into macaques having only sparse GBS data, resulting in dense genome-wide genotypes throughout the pedigree. RESULTS: We developed GBS for the macaque genome using a digestion with PstI, followed by sequencing of size-selected fragments at 30X coverage. From GBS sequence data collected on all individuals in a 16-member pedigree, we characterized high-confidence genotypes at 22,455 single nucleotide variant (SNV) sites that were suitable for guiding imputation of dense sequence data from WGS. To characterize dense markers for imputation, we performed WGS at 30X coverage on nine of the 16 individuals, yielding 10,193,425 high-confidence SNVs. To validate the use of GBS data for facilitating imputation, we initially focused on chromosome 19 as a test case, using an optimized panel of 833 sparse, evenly-spaced markers from GBS and 5,010 dense markers from WGS. Using the method of "Genotype Imputation Given Inheritance" (GIGI), we evaluated the effects on imputation accuracy of 3 different strategies for selecting individuals for WGS, including 1) using "GIGI-Pick" to select the most informative individuals, 2) using the most recent generation, or 3) using founders only.  We also evaluated the effects on imputation accuracy of using a range of from 1 to 9 WGS individuals for imputation. We found that the GIGI-Pick algorithm for selection of WGS individuals outperformed common heuristic approaches, and that genotype numbers and accuracy improved very little when using >5 WGS individuals for imputation. Informed by our findings, we used 4 macaques with WGS data to impute variants at up to 7,655,491 sites spanning all 20 autosomes in the 12 remaining macaques, based on their GBS genotypes at only 17,158 loci. Using a strict confidence threshold, we imputed an average of 3,680,238 variants per individual at >99 % accuracy, or an average 4,458,883 variants per individual at a more relaxed threshold, yielding >97 % accuracy. CONCLUSIONS: We conclude that an optimal tradeoff between genotype accuracy, number of imputed genotypes, and overall cost exists at the ratio of one individual selected for WGS using the GIGI-Pick algorithm, per 3-5 relatives selected for GBS. This approach makes feasible the collection of accurate, dense genome-wide sequence data in large pedigreed macaque cohorts without the need for more expensive WGS data on all individuals.

Anti-Müllerian hormone is produced heterogeneously in primate preantral follicles and is a potential biomarker for follicle growth and oocyte maturation in vitro.

PURPOSE: The main goals of this study were to investigate the expression of anti-Müllerian hormone (AMH) and its receptor (AMHR2) during follicular development in primates, and to evaluate the potential of AMH as a biomarker for follicle growth and oocyte maturation in vitro. METHODS: The mRNA and protein expression of AMH and AMHR2 were determined using isolated follicles and ovarian sections from rhesus macaques (n = 4) by real-time PCR and immunohistochemistry, respectively. Isolated secondary follicles were cultured individually. Follicle growth and media AMH concentrations were assessed by ELISA. The mRNA expression profiles, obtained from RNA sequencing, of in vitro- and in vivo-developed antral follicles were compared. Secondary follicles from additional animals (n = 35) were cultured. Follicle growth, oocyte maturation, and media AMH concentrations were evaluated for forecasting follicular development in vitro by AMH levels. RESULTS: AMH immunostaining was heterogeneous in the population of preantral follicles that were also stained for AMHR2. The mRNA expression profiles were comparable between in vivo- and in vitro-developed follicles. AMH levels produced by growing follicles were higher than those of nongrowing follicles in culture. With a cutoff value of 1.40 ng/ml, 85 % of nongrowing follicles could be identified while eliminating only 5 % of growing follicles. Growing follicles that generated metaphase II-stage oocytes secreted greater amounts of AMH than did those yielding immature germinal vesicle-stage oocytes. CONCLUSIONS: AMH, co-expressed with AMHR2, was produced heterogeneously by preantral follicles in macaques with levels correlated positively with follicle growth and oocyte maturation. AMH may serve as a biomarker for primate follicular development in vitro.

Genetic Architecture of Human Obesity Traits in the Rhesus Macaque.

OBJECTIVE: Whereas the metabolic consequences of obesity have been studied extensively in the rhesus macaque, corollary genetic studies of obesity are nonexistent. This study assessed genetic contributions to spontaneous adiposity in this species. METHODS: Phenotypic variation by age class and sex for BMI, waist to height ratio, waist to thigh ratio, and waist circumference was assessed in 583 macaques. Total and sex-specific heritability for all traits was estimated, including waist to thigh ratio adjusted for BMI, as well as genotypic and phenotypic correlations. In addition, functional genetic variation at BDNF, FTO, LEP, LEPR, MC4R, PCSK1, POMC, and SIM1 was assessed in four animals with extreme spontaneous adiposity. RESULTS: Trait heritability in the combined sample was low to moderate (0.14-0.32), whereas sex-specific heritability was more substantial (0.20-0.67). Heritability was greater in females for all traits except BMI. All traits were robustly correlated, with genetic correlations of 0.63 to 0.93 indicating substantial pleiotropy. Likely functional variants were discovered in the four macaques at all eight human obesity genes, including six missense mutations in BDNF, FTO, LEP, LEPR, and PCSK1 and, notably, one nonsense mutation in LEPR. CONCLUSIONS: A moderate polygenic contribution to adiposity in rhesus macaques was found, as well as mutations with potentially larger effects in multiple genes that influence obesity in humans.

The spotted gar genome illuminates vertebrate evolution and facilitates human-teleost comparisons.

To connect human biology to fish biomedical models, we sequenced the genome of spotted gar (Lepisosteus oculatus), whose lineage diverged from teleosts before teleost genome duplication (TGD). The slowly evolving gar genome has conserved in content and size many entire chromosomes from bony vertebrate ancestors. Gar bridges teleosts to tetrapods by illuminating the evolution of immunity, mineralization and development (mediated, for example, by Hox, ParaHox and microRNA genes). Numerous conserved noncoding elements (CNEs; often cis regulatory) undetectable in direct human-teleost comparisons become apparent using gar: functional studies uncovered conserved roles for such cryptic CNEs, facilitating annotation of sequences identified in human genome-wide association studies. Transcriptomic analyses showed that the sums of expression domains and expression levels for duplicated teleost genes often approximate the patterns and levels of expression for gar genes, consistent with subfunctionalization. The gar genome provides a resource for understanding evolution after genome duplication, the origin of vertebrate genomes and the function of human regulatory sequences.

Connectivity of vertebrate genomes: Paired-related homeobox (Prrx) genes in spotted gar, basal teleosts, and tetrapods.

Teleost fish are important models for human biology, health, and disease. Because genome duplication in a teleost ancestor (TGD) impacts the evolution of teleost genome structure and gene repertoires, we must discriminate gene functions that are shared and ancestral from those that are lineage-specific in teleosts or tetrapods to accurately apply inferences from teleost disease models to human health. Generalizations must account both for the TGD and for divergent evolution between teleosts and tetrapods after the likely two rounds of genome duplication shared by all vertebrates. Progress in sequencing techniques provides new opportunities to generate genomic and transcriptomic information from a broad range of phylogenetically informative taxa that facilitate detailed understanding of gene family and gene function evolution. We illustrate here the use of new sequence resources from spotted gar (Lepisosteus oculatus), a rayfin fish that diverged from teleosts before the TGD, as well as RNA-Seq data from gar and multiple teleost lineages to reconstruct the evolution of the Paired-related homeobox (Prrx) transcription factor gene family, which is involved in the development of mesoderm and neural crest-derived mesenchyme. We show that for Prrx genes, the spotted gar genome and gene expression patterns mimic mammals better than teleosts do. Analyses force the seemingly paradoxical conclusion that regulatory mechanisms for the limb expression domains of Prrx genes existed before the evolution of paired appendages. Detailed evolutionary analyses like those reported here are required to identify fish species most similar to the human genome to optimally connect fish models to human gene functions in health and disease.