American journal of human genetics最新文献

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

Extra-axial cavernous hemangiomas (ECHs) are complex vascular lesions mainly found in the spine and cavernous sinus. Their removal poses significant risk due to their vascularity and diffuse nature, and their genetic underpinnings remain incompletely understood. Our approach involved genetic analyses on 31 tissue samples of ECHs employing whole-exome sequencing and targeted deep sequencing. We explored downstream signaling pathways, gene expression changes, and resultant phenotypic shifts induced by these mutations, both in vitro and in vivo. In our cohort, 77.4% of samples had somatic missense variants in GNA14, GNAQ, or GJA4. Transcriptomic analysis highlighted significant pathway upregulation, with the GNAQ c.626A>G (p.Gln209Arg) mutation elevating PI3K-AKT-mTOR and angiogenesis-related pathways, while GNA14 c.614A>T (p.Gln205Leu) mutation led to MAPK and angiogenesis-related pathway upregulation. Using a mouse xenograft model, we observed enlarged vessels from these mutations. Additionally, we initiated rapamycin treatment in a 14-year-old individual harboring the GNAQ c.626A>G (p.Gln209Arg) variant, resulting in gradual regression of cutaneous cavernous hemangiomas and improved motor strength, with minimal side effects. Understanding these mutations and their pathways provides a foundation for developing therapies for ECHs resistant to current therapies. Indeed, the administration of rapamycin in an individual within this study highlights the promise of targeted treatments in treating these complex lesions.

Highlighting the Distinguished Speakers Symposium on "The Future of Human Genetics and Genomics," this collection of articles is based on presentations at the ASHG 2023 Annual Meeting in Washington, DC, in celebration of all our field has accomplished in the past 75 years, since the founding of ASHG in 1948.

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders.

Transcriptomics is a powerful tool for unraveling the molecular effects of genetic variants and disease diagnosis. Prior studies have demonstrated that choice of genome build impacts variant interpretation and diagnostic yield for genomic analyses. To identify the extent genome build also impacts transcriptomics analyses, we studied the effect of the hg19, hg38, and CHM13 genome builds on expression quantification and outlier detection in 386 rare disease and familial control samples from both the Undiagnosed Diseases Network and Genomics Research to Elucidate the Genetics of Rare Disease Consortium. Across six routinely collected biospecimens, 61% of quantified genes were not influenced by genome build. However, we identified 1,492 genes with build-dependent quantification, 3,377 genes with build-exclusive expression, and 9,077 genes with annotation-specific expression across six routinely collected biospecimens, including 566 clinically relevant and 512 known OMIM genes. Further, we demonstrate that between builds for a given gene, a larger difference in quantification is well correlated with a larger change in expression outlier calling. Combined, we provide a database of genes impacted by build choice and recommend that transcriptomics-guided analyses and diagnoses are cross referenced with these data for robustness.

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

The neurodevelopmental disorders Prader-Willi syndrome (PWS) and Schaaf-Yang syndrome (SYS) both arise from genomic alterations within human chromosome 15q11-q13. A deletion of the SNORD116 cluster, encoding small nucleolar RNAs, or frameshift mutations within MAGEL2 result in closely related phenotypes in individuals with PWS or SYS, respectively. By investigation of their subcellular localization, we observed that in contrast to a predominant cytoplasmic localization of wild-type (WT) MAGEL2, a truncated MAGEL2 mutant was evenly distributed between the cytoplasm and the nucleus. To elucidate regulatory pathways that may underlie both diseases, we identified protein interaction partners for WT or mutant MAGEL2, in particular the survival motor neuron protein (SMN), involved in spinal muscular atrophy, and the fragile-X-messenger ribonucleoprotein (FMRP), involved in autism spectrum disorders. The interactome of the non-coding RNA SNORD116 was also investigated by RNA-CoIP. We show that WT and truncated MAGEL2 were both involved in RNA metabolism, while regulation of transcription was mainly observed for WT MAGEL2. Hence, we investigated the influence of MAGEL2 mutations on the expression of genes from the PWS locus, including the SNORD116 cluster. Thereby, we provide evidence for MAGEL2 mutants decreasing the expression of SNORD116, SNORD115, and SNORD109A, as well as protein-coding genes MKRN3 and SNRPN, thus bridging the gap between PWS and SYS.

Highlighting the Distinguished Speakers Symposium on "The Future of Human Genetics and Genomics," this collection of articles is based on presentations at the ASHG 2023 Annual Meeting in Washington, DC, in celebration of all our field has accomplished in the past 75 years since the founding of ASHG in 1948.

Highlighting the Distinguished Speakers Symposium on "The Future of Human Genetics and Genomics," this collection of articles is based on presentations at the ASHG 2023 Annual Meeting in Washington, DC, in celebration of all our field has accomplished in the past 75 years, since the founding of ASHG in 1948.

Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.