American journal of human genetics最新文献

Sharing diverse genomic and other biomedical datasets is critical to advancing scientific discoveries and their equitable translation to improve human health. However, data sharing remains challenging in the context of legacy datasets, evolving policies, multi-institutional consortium science, and international stakeholders. The NIH-funded Polygenic Risk Methods in Diverse Populations (PRIMED) Consortium was established to improve the performance of polygenic risk estimates for a broad range of health and disease outcomes with global impacts. Improving polygenic risk score performance across genetically diverse populations requires access to large, diverse cohorts. We report on the design and implementation of data-sharing policies and procedures developed in PRIMED to aggregate and analyze data from multiple heterogeneous sources while adhering to pre-existing data-sharing policies for each integrated dataset and respecting participant preferences and informed consent. Specifically, we describe two primary data-sharing mechanisms-coordinated dbGaP applications and a Consortium Data Sharing Agreement-and provide alternatives when individual-level data cannot be shared within the Consortium (e.g., federated analyses). We also describe technical implementation of Consortium data sharing in the NHGRI Analysis Visualization and Informatics Lab-space (AnVIL) cloud platform to share derived individual-level data, genomic summary results, and methods workflows with appropriate permissions. As a consortium making secondary use of pre-existing data sources, we also discuss challenges and propose solutions for release of individual- and summary-level data products to the broader scientific community. We make recommendations for ongoing and future policymaking with the goal of informing future consortia and other research activities.

This paper reports the findings of an international survey of health data ecosystems (HDEs) in 12 countries plus the H3 Africa project using live, structured interviews with senior project team members under the auspices of Canada's All for One Precision Health Initiative. We note the high level of interest in HDEs around the world, as well as in Canada, despite the financial, jurisdictional, and other barriers that continue to hold back widespread data sharing. We present results detailing operational profiles for each of the 13 participants, including whether their healthcare systems are centralized (national) or decentralized (regional), project start date, funding, information technology (IT) infrastructure, and the extent to which participants have implemented a data-sharing mandate. We find no evidence to confirm common assumptions about features conferring an advantage on HDE development, such as early launch date or top-down government mandate. We also find no evidence of a reference model to explain what makes any HDE effective, valuable, or successful and conclude, on the basis of our interviews, that the diversity that makes each of these projects unique may undermine collective actions like data sharing. While participants provided useful cautions about pitfalls they encountered, more research on these issues is required, and we anticipate that advanced assessment tools like the maturity level model (MLM) developed by the European Union (EU) may help countries understand what stage of the HDE development process they have reached and what strategies will be most effective for them in later stages.

Elucidating ancestry-specific structures in admixed populations is crucial for comprehending population history and mitigating confounding effects in genome-wide association studies. Existing methods to reveal the ancestry-specific structures generally rely on frequency-based estimates of genetic relationship matrix (GRM) among admixed individuals after masking segments from ancestry components not being targeted for investigation. However, these approaches disregard linkage information between markers, potentially limiting their resolution in revealing structure within an ancestry component. We introduce ancestry-specific expected GRM (as-eGRM), a novel framework for estimating the relatedness within ancestry components between admixed individuals. The key design of as-eGRM consists of defining ancestry-specific pairwise relatedness between individuals based on genealogical trees encoded in the ancestral recombination graph (ARG) and local ancestry calls and then computing the expectation of the ancestry-specific relatedness across the genome. Comprehensive evaluations using both simulated stepping-stone models of population structure and empirical datasets based on three-way admixed Latino cohorts showed that analysis based on as-eGRM robustly outperforms existing methods in revealing the structure in admixed populations with diverse demographic histories, which in turn improves the robustness against confounding due to population structure in association testing.

Despite the well-documented benefits of genome sequencing in critically ill pediatric patients, genomic testing is rarely utilized in critically ill adults, and data on its diagnostic yield and clinical implications in this population are lacking. We retrospectively analyzed whole-exome sequencing (WES) data from 365 adults ages 18-40 years with intensive care unit (ICU) admissions at the University of Pennsylvania Health System. For each participant, two medical genetics- and internal medicine-trained clinicians reviewed WES reports and patient charts for variant classification, result interpretation, and identification of genetic diagnoses related to their critical illness. We identified a diagnostic genetic variant in 24.4% of patients, with nearly half of these being unknown to patients and their care teams at the time of ICU admission. Of these genetic diagnoses, 76.6% conferred specific care-altering medical management recommendations. Importantly, diagnostic yield did not decrease with increasing patient age, and patients with undocumented diagnoses trended toward higher mortality rates compared to either patients with known diagnoses or patients with negative exomes. Significant disparities were seen by electronic health record-reported race, with genetic diagnoses known/documented for 63.1% of White patients at the time of ICU admission but only for 22.7% of Black patients. Altogether, the results of this study of broad, exclusion-based genetic testing in the critically ill adult population suggest that the broad implementation of genetic testing in critically ill adults has the potential to improve patient care and dismantle disparities in healthcare delivery.

Genomic medicine requires a robust evidence base of variant phenotypic impacts, which remains incomplete even in extensively studied genes with monogenic disease associations. Here, we evaluated the broad potential of using population cohort data to identify evidence that can be used in variant assessment. Across 41 genes related to 18 clinically actionable monogenic phenotypes, we calculated variant-level odds ratios of disease enrichment using data from 469,803 UK Biobank participants. We found significant differences in odds ratio values between ClinVar-labeled pathogenic and benign variants in 11 phenotypes, spanning both common and rare disorders. To facilitate clinical translation, we calibrated the strength of evidence provided by variant-level odds ratios to align with American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) interpretation guidelines (PS4 criterion) and found that odds ratios may reach "moderate," "strong," or "very strong" evidence, varying by phenotype and gene. Overall, we found that 2.6% (N = 12,350) of participants harbor a rare variant of uncertain significance (VUS) with at least moderate evidence of pathogenicity-an indication of potentially unrecognized disease risk. Finally, by incorporating computational and functional data alongside population-based odds ratios, we identified variants that met the criteria for clinical reclassification. Notably, using this approach, we identified that 12.4% of rare VUSs in LDLR seen in participants meet diagnostic criteria to be classified as likely pathogenic, demonstrating its potential to scale the reclassification of VUSs.

The human leukocyte antigen (HLA) region plays an important role in human health through its involvement in immune cell recognition and maturation. While genetic variation in the HLA region is associated with many diseases, the pleiotropic patterns of these associations have not been systematically investigated. Here, we developed a haplotype approach to investigate disease associations phenome wide for 412,181 Finnish individuals and 2,459 diseases. Across the 1,035 diseases with a genome-wide association study association, we found a 17-fold average per-SNP enrichment of hits in the HLA region. Altogether, we identified 7,649 HLA associations across 647 diseases, including 1,750 associations uncovered by haplotype analysis. We found that some haplotypes show both risk-increasing and protective associations across different diseases, while others consistently increase risk across diseases, indicating a complex pleiotropic landscape involving a range of diseases. This study highlights the extensive impact of HLA variation on disease risk and underscores the importance of classical and non-classical genes as well as non-coding variation.

Mendelian randomization (MR) has emerged as a highly valuable tool for inferring the causal effects of exposures on outcomes in observational studies using genetic variants, typically single-nucleotide polymorphisms (SNPs), as instrumental variables (IVs). Standard MR typically involves three steps: inputs of genome-wide association studies (GWASs) for both exposure and outcome, determination of IVs, and inference of causal effects. However, existing methods fail to simultaneously account for characteristics of GWAS data, uncertainty surrounding the validity of SNPs as IVs, and efficiency of estimating and testing the causal effect. Here, we developed MR method with self-adaptive determination of sample structure and multiple pleiotropic effects (MAPLE), a method for effective MR analysis. MAPLE utilizes correlated SNPs, self-adaptively accounts for the sample structure and the uncertainty that these correlated SNPs may exhibit multiple pleiotropic effects, and relies on a maximum-likelihood framework to infer the causal effects and obtain calibrated p values. We illustrate the advantage of MAPLE through comprehensively realistic simulations, where MAPLE, compared with another eight MR methods, shows calibrated type I error control and reduces false positives while being more powerful. In three types of lipid-trait-centric MR analyses in UK Biobank, MAPLE produces the most accurate causal-effect estimates in positive-control analyses evaluating the causal effect of each lipid trait on itself; reduces the false positives by 12.5% on average compared with existing methods in negative-control analyses investigating the causal effects of lipid traits on hair color and skin color; and highlights the causal effects of physical activity, alcohol, and smoking on lipid profiles in factor-screening analyses involving 412 trait pairs.

The current surveillance guideline of esophageal squamous cell carcinoma (ESCC) runs the risk of underestimation of early Lugol-unstained lesions (LULs), and extremely early genomic events in the carcinogenesis and their temporal order of occurrence remain unclear. Here, we performed whole-exome sequencing analyses of 148 biopsy samples obtained at different time points (with a median 4.6-year interval) from the same esophageal lesions of 74 asymptomatic subjects with LULs detected at community-based screening, of whom 33 individuals showed progression at the follow-up chromoendoscopy, while the other 41 did not. We found that progressors showed higher tumor mutational burden, chromosomal instability level, whole-genome doubling (WGD) events, and apolipoprotein B mRNA-editing catalytic polypeptide-like (APOBEC) activity at both index and follow-up compared to non-progressors. Sustained TP53 two-hit events, absence of NOTCH1 mutation, presence of CDKN2A mutation/deletion, and WGD were detected both before and after LUL progression in 64% (9/14) of progressors and none (0/19) of non-progressors with non-dysplastic LULs (ND-LULs). CCND1, FGFs, and MIR548K amplification in chromosome 11q13.3 only occurred in progressors with high-grade intraepithelial neoplasia or above lesions. TP53 two-hit events, absence of NOTCH1 mutation, and presence of CDKN2A mutation/deletion were positively correlated with WGD and successfully distinguished all 5 progressed individuals from the 24 subjects at so-called "low risk" of progression (ND-LULs with a size of ≤5 mm) under current surveillance criteria. Collectively, TP53 two-hit events, absence of NOTCH1 mutation, and presence of CDKN2A mutation/deletion are extremely early events in the carcinogenesis of ESCC, providing early warning markers for the surveillance of high-risk precursor lesions of ESCC.