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Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype–phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all. Biobanks help centralize specimen collections, store and disseminate data, and facilitate large-scale analyses. This Review discusses how biobanks facilitate genetics research towards advancing precision medicine and overviews potential solutions to their current limitations.

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R 2  ~ 83–97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations. The emergence of large-scale genomics projects has led to genetic studies across cohorts. Here, the authors conduct genome-wide association studies meta-analyzing in trusted research environments or pooling together and find similar, but not identical results.

Background The All of Us Research Program (AoURP, “the program”) is an initiative, sponsored by the National Institutes of Health (NIH), that aims to enroll one million people (or more) across the USA. Through repeated engagement of participants, a research resource is being created to enable a variety of future observational and interventional studies. The program has also committed to genomic data generation and returning important health-related information to participants. Methods Whole-genome sequencing (WGS), variant calling processes, data interpretation, and return-of-results procedures had to be created and receive an Investigational Device Exemption (IDE) from the United States Food and Drug Administration (FDA). The performance of the entire workflow was assessed through the largest known cross-center, WGS-based, validation activity that was refined iteratively through interactions with the FDA over many months. Results The accuracy and precision of the WGS process as a device for the return of certain health-related genomic results was determined to be sufficient, and an IDE was granted. Conclusions We present here both the process of navigating the IDE application process with the FDA and the results of the validation study as a guide to future projects which may need to follow a similar path. Changes to the program in the future will be covered in supplementary submissions to the IDE and will support additional variant classes, sample types, and any expansion to the reportable regions.

Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings. A new study from the eMERGE consortium provides insights on the development of a pipeline for the generation and reporting of polygenic risk scores for ten diseases to diverse populations in a clinical setting.

We are conducting a genome scan at an average resolution of 10 centimorgans (cM) for type 2 diabetes susceptibility genes in 716 affected sib pairs from 477 Finnish families. To date, our best evidence for linkage is on chromosome 20 with potentially separable peaks located on both the long and short arms. The unweighted multipoint maximum logarithm of odds score (MLS) was 3.08 on 20p (location, x̂ = 19.5 cM) under an additive model, whereas the weighted MLS was 2.06 on 20q (x̂ = 57 cM, recurrence risk, λ̂s= 1.25, P=0.009) Weighted logarithm of odds scores of 2.00 (x̂ = 6.95 cM, P=0.010) and 1.92 (x̂ = 18.5 cM, P=0.013) were also observed. Ordered subset analyses based on sibships with extreme mean values of diabetes-related quantitative traits yielded sets of families who contributed disproportionately to the peaks. Two-hour glucose levels in offspring of diabetic individuals gave a MLS of 2.12 (P = 0.0018) at 9.5 cM. Evidence from this and other studies suggests at least two diabetes-susceptibility genes on chromosome 20. We have also screened the gene for maturity-onset diabetes of the young 1, hepatic nuclear factor 4-a (HNF-4α) in 64 affected sibships with evidence for high chromosomal sharing at its location on chromosome 20q. We found no evidence that sequence changes in this gene accounted for the linkage results we observed.

The rapid growth of genomic data has led to a new research paradigm where data are stored centrally in Trusted Research Environments (TREs) such as the All of Us Researcher Workbench (RW) and the UK Biobank Research Analysis Platform (RAP). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conducted a Genome-Wide Association Study (GWAS) of standard lipid measures on the UKB RAP and AoU RW using two approaches: meta-analysis and pooled analysis. We curated lipid measurements for 37,754 All of Us participants with whole genome sequence (WGS) data and 190,982 UK Biobank participants with whole exome sequence (WES) data. For the meta-analysis, we performed a GWAS of each cohort in their respective platform and meta-analyzed the results. We separately performed a pooled GWAS on both datasets combined. We identified 454 and 445 significant variants in meta-analysis and pooled analysis, respectively. Comparison of full summary data from both meta-analysis and pooled analysis with an external study showed strong correlation of known loci with lipid levels (R2~91-98%). Importantly, 84 variants met the significance threshold only in the meta-analysis and 75 variants were significant only in pooled analysis. These method-specific differences may be explained by differences in cohort size, ancestry, and phenotype distributions in All of Us and UK Biobank. Importantly, we noted a significant increase in the proportion of significant variants predominantly from non-European ancestry individuals in the pooled analysis compared to meta-analysis (p=0.01). Pooled analysis required about half as many computational steps as meta-analysis. These findings have important implications for both platform implementations and researchers undertaking large-scale cross-cohort analyses, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.Competing Interest StatementP.N. reports investigator-initiated grants from Amgen, Apple, AstraZeneca, Boston Scientific, and Novartis, personal fees from Apple, AstraZeneca, Blackstone Life Sciences, Foresite Labs, Novartis, Roche / Genentech, is a co-founder of TenSixteen Bio, is a shareholder of geneXwell and TenSixteen Bio, and spousal employment at Vertex, all unrelated to the present work. A.G.B is a co-founder and shareholder of TenSixteen Bio.

Bipolar disorder is a severe mental illness characterized by episodes of extremely elated mood (mania) and very low mood (depression), which has a large public health impact. Family, twin, and adoption studies have shown that genetic factors play a large role in the etiology of bipolar disorder, with estimates of heritability as high as 85%. The etiology of bipolar disorder is likely multifactorial, with many genes, as well as environmental factors, interacting to cause disease. Linkage studies completed to date have been relatively unsuccessful—no single susceptibility gene has yet been identified. Given that there is no single major gene that confers disease risk, and the true underlying genetic model is unknown, use of alternative statistical methods, which incorporate linkage evidence from multiple trait loci concurrently, as well as important disease characteristics and environmental factors, is warranted. The analyses completed in this dissertation utilized the raw data from a pooled sample of 148 multiplex bipolar pedigrees to test two alternative statistical approaches designed to overcome the limitations of analyses that assess linkage evidence one locus at a time. The NPL regression approach (Langefeld, 1999) utilizes the multipoint pedigree-specific NPL scores derived from the program GENEHUNTER as covariates in a conditional logistic regression model that tests for increased allele sharing. The subset approach (Cox et al., 1999) weights families on evidence for linkage at one trait locus in order to test for linkage at other unlinked regions of the genome. Single-locus analyses of these bipolar families revealed suggestive evidence for linkage on chromosome 2q and 8q. Through multi-locus modeling using the NPL regression method, evidence for linkage to 8q increased to a LOD = 3.8, conditioning on linkage evidence at 5 additional unlinked regions. Both approaches identified a putative trait locus on chromosome 5q that did not show evidence for linkage in single-locus analysis, suggesting a significant epistatic interaction with the locus on chromosomes 2q. Each approach has advantages and disadvantages, which are discussed. Power comparisons of these methods, as well as other recently proposed multi-locus methods and methods that incorporate relevant covariates, deserves further scrutiny.