American journal of human genetics最新文献

The role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and environmental (E) variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (r_g) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRSs) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP heritability across E bins. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; average n = 325,000) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs with r_g significantly <1 (false discovery rate < 5%); 28 trait-E pairs with significant PRSxE and significant SNP heritability differences across E bins; and 15 trait-E pairs with significant PRSxE but no SNP heritability differences across E bins. Across the three scenarios, eight of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of these scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait variance.

Autism is four times more prevalent in males than females. To study whether this reflects a difference in genetic predisposition attributed to autosomal rare variants, we evaluated sex differences in effect size of damaging protein-truncating and missense variants on autism predisposition in 47,061 autistic individuals using a liability model with differing thresholds. Given the sex differences in the rates of cognitive impairment among autistic individuals, we also compared effect sizes of rare variants between individuals with and without cognitive impairment or motor delay. Although these variants mediated different likelihoods of autism with versus without cognitive or motor difficulties, their effect sizes on the liability scale did not differ significantly by sex exome wide or in genes sex-differentially expressed in the cortex. De novo mutations were enriched in genes with male-biased expression in the adult cortex, but these genes did not show a significant sex difference on the liability scale, nor did the liability conferred by these genes differ significantly from other genes with similar loss-of-function intolerance and sex-averaged cortical expression. Exome-wide female bias in de novo protein-truncating mutation rates on the observed scale was driven by high-confidence and syndromic autism-predisposition genes. In summary, autosomal rare and damaging coding variants confer similar liability for autism in females and males.

Understanding how genetic variation contributes to phenotypic variation is a fundamental question in genetics. Genome-wide association studies (GWASs) have discovered numerous genetic associations with various human phenotypes, most of which contain co-inherited variants in strong linkage disequilibrium (LD) with indistinguishable statistical significance. The experimental and analytical difficulty in identifying the "causal variant" among the co-inherited variants has traditionally led mechanistic studies to focus on relatively simple loci, where a single functional variant is presumed to explain most of the association signal and affect a target gene. The notion that a single causal variant is responsible for an association signal, while other variants in LD are merely correlated, has often been assumed in functional studies. However, emerging evidence powered by high-throughput experimental tools and context-specific functional databases argues that even a single independent signal may involve multiple functional variants in strong LD, each contributing to the observed genetic association. In this perspective, we articulate this evolving understanding of causal variants through examples from both traditional locus-by-locus approaches and more recent high-throughput functional studies. We then discuss the implications and prospects of this notion in understanding the genetic architecture of complex traits and interpreting the variant-level causality in GWAS follow-up studies.

Viable human aneuploidy can be challenging to model in rodents due to syntenic boundaries or primate-specific biology. Human monosomy-X (45,X) causes Turner syndrome (TS), altering craniofacial, skeletal, endocrine, and cardiovascular development, which in contrast remain unaffected in X-monosomic mice. To learn how monosomy-X may impact embryonic development, we turned to 45,X and isogenic euploid human induced pluripotent stem cells (hiPSCs) from male and female mosaic donors. Because the neural crest (NC) is hypothesized to give rise to craniofacial and cardiovascular changes in TS, we assessed differential expression of hiPSC-derived anterior NC cells (NCCs). Across three independent isogenic panels, 45,X NCCs show impaired acquisition of PAX7⁺SOX10⁺ markers and disrupted expression of other NCC-specific genes relative to isogenic euploid controls. Additionally, 45,X NCCs increase cholesterol biosynthesis genes while reducing transcripts with 5' terminal oligopyrimidine (TOP) motifs, including those of ribosomal and nuclear-encoded mitochondrial proteins. Such metabolic pathways are also over-represented in weighted co-expression modules that are preserved in monogenic neurocristopathy and reflect 28% of all TS-associated terms of the human phenotype ontology. We demonstrate that 45,X NCCs reduce protein synthesis despite activation of mammalian target of rapamycin (mTOR) but are partially rescued by mild mTOR suppression. Our analysis identifies specific sex-linked genes that are expressed from two copies in euploid males and females alike and qualify as candidate haploinsufficient drivers of TS phenotypes in NC-derived lineages. This study demonstrates that isogenic hiPSC-derived NCC panels representing monosomy-X can serve as powerful models of early NC development in TS and inform new hypotheses toward its etiology.

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using local ancestry inference (LAI). Accurate LAI is crucial to ensure that downstream analyses accurately reflect the genetic ancestry of research participants. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries-African (AFR), Amerindigenous (AMR), and European (EUR). Simulating linkage-disequilibrium-informed admixed haplotypes under a variety of 2- and 3-way admixture models, we implemented a standard LAI pipeline, testing the impact of reference panel composition, DNA data type, demography, and software parameters to quantify ancestry-specific LAI accuracy. We observe that across all models, AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts, with true positive rate means for AMR ranging from 88% to 94%, EUR from 96% to 99%, and AFR from 98% to 99%. When LAI miscalls occurred, they most frequently erroneously called EUR ancestry in true AMR sites. Concerning reference panel curation, we find that using a reference panel well matched to the target population, even with a smaller sample size, was accurate and the most computationally efficient. Imputation did not harm LAI performance in our tests; rather, we observed that higher variant density improved accuracy. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across admixed populations. Our findings reinforce the need for the inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

Genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex traits, yet the biological interpretation remains challenging, especially for variants in non-coding regions. Expression quantitative trait locus (eQTL) studies have linked these variations to gene expression, aiding in identifying genes involved in disease mechanisms. Traditional eQTL analyses using bulk RNA sequencing (bulk RNA-seq) provide tissue-level insights but suffer from signal loss and distortion due to unaddressed cellular heterogeneity. Recently, single-cell RNA-seq (scRNA-seq) has provided higher resolution, enabling cell-type-specific eQTL (ct-eQTL) analyses. However, these studies are limited by their smaller sample sizes and technical constraints. In this paper, we present a statistical framework, IBSEP, which integrates bulk RNA-seq and scRNA-seq data for enhanced ct-eQTL prioritization. Our method employs a hierarchical linear model to combine summary statistics from both data types, overcoming the limitations while leveraging the advantages associated with each technique. Through extensive simulations and real data analyses, including peripheral blood mononuclear cells and brain cortex datasets, IBSEP demonstrated superior performance in identifying ct-eQTLs compared to existing methods. Our approach unveils transcriptional regulatory mechanisms specific to cell types, offering deeper insights into the genetic basis of complex diseases at a cellular resolution.

BCL11B is a Cys2-His2 zinc-finger (C2H2-ZnF) domain-containing, DNA-binding, transcription factor with established roles in the development of various organs and tissues, primarily the immune and nervous systems. BCL11B germline variants have been associated with a variety of developmental syndromes. However, genotype-phenotype correlations along with pathophysiologic mechanisms of selected variants mostly remain elusive. To dissect these, we performed genotype-phenotype correlations of 92 affected individuals harboring a pathogenic or likely pathogenic BCL11B variant, followed by immune phenotyping, analysis of chromatin immunoprecipitation DNA-sequencing data, dual-luciferase reporter assays, and molecular modeling. These integrative analyses enabled us to define three clinical subtypes of BCL11B-related disorders. It is likely that gene-disruptive BCL11B variants and missense variants affecting zinc-binding cysteine and histidine residues cause mild to moderate neurodevelopmental delay with increased propensity for behavioral and dental anomalies, allergies and asthma, and reduced type 2 innate lymphoid cells. Missense variants within C2H2-ZnF DNA-contacting α helices cause highly variable clinical presentations ranging from multisystem anomalies with demise in the first years of life to late-onset, hyperkinetic movement disorder with poor fine motor skills. Those not in direct DNA contact cause a milder phenotype through reduced, target-specific transcriptional activity. However, missense variants affecting C2H2-ZnFs, DNA binding, and "specificity residues" impair BCL11B transcriptional activity in a target-specific, dominant-negative manner along with aberrant regulation of alternative DNA targets, resulting in more severe and unpredictable clinical outcomes. Taken together, we suggest that the phenotypic severity and variability is largely dependent on the DNA-binding affinity and specificity of altered BCL11B proteins.

More than 50% of families with suspected rare monogenic diseases remain unsolved after whole-genome analysis by short-read sequencing (SRS). Long-read sequencing (LRS) could help bridge this diagnostic gap by capturing variants inaccessible to SRS, facilitating long-range mapping and phasing and providing haplotype-resolved methylation profiling. To evaluate LRS's additional diagnostic yield, we sequenced a rare-disease cohort of 98 samples from 41 families, using nanopore sequencing, achieving per sample ∼36× average coverage and 32-kb read N50 from a single flow cell. Our Napu pipeline generated assemblies, phased variants, and methylation calls. LRS covered, on average, coding exons in ∼280 genes and ∼5 known Mendelian disease-associated genes that were not covered by SRS. In comparison to SRS, LRS detected additional rare, functionally annotated variants, including structural variants (SVs) and tandem repeats, and completely phased 87% of protein-coding genes. LRS detected additional de novo variants and could be used to distinguish postzygotic mosaic variants from prezygotic de novos. Diagnostic variants were established by LRS in 11 probands, with diverse underlying genetic causes including de novo and compound heterozygous variants, large-scale SVs, and epigenetic modifications. Our study demonstrates LRS's potential to enhance diagnostic yield for rare monogenic diseases, implying utility in future clinical genomics workflows.

Mosaic loss of Y (mLOY) is the most common somatic chromosomal alteration detected in human blood. The presence of mLOY is associated with altered blood cell counts and increased risk of Alzheimer disease, solid tumors, and other age-related diseases. We sought to gain a better understanding of genetic drivers and associated phenotypes of mLOY through analyses of whole-genome sequencing (WGS) of a large set of genetically diverse males from the Trans-Omics for Precision Medicine (TOPMed) program. We show that haplotype-based calling methods can be used with WGS data to successfully identify mLOY events. This approach enabled us to identify differences in mLOY frequencies across populations defined by genetic similarity, revealing a higher frequency of mLOY in the European (EUR) ancestry group compared to other ancestries. We identify multiple loci associated with mLOY susceptibility and show that subsets of human hematopoietic stem cells are enriched for the activity of mLOY susceptibility variants. Finally, we found that certain alleles on chromosome Y are more likely to be lost than others in detectable mLOY clones.

How might members of a large, multi-institutional research and resource consortium foster justice, equity, diversity, and inclusion as central to its mission, goals, governance, and culture? These four principles, often referred to as JEDI, can be aspirational-but to be operationalized, they must be supported by concrete actions, investments, and a persistent long-term commitment to the principles themselves, which often requires self-reflection and course correction. We present here the iterative design process implemented across the Clinical Genome Resource (ClinGen) that led to the development of an action plan to operationalize JEDI principles across three major domains, with specific deliverables and commitments dedicated to each. Active involvement of consortium leadership, buy-in from its members at all levels, and support from NIH program staff at pivotal stages were essential to the success of this effort. The ClinGen JEDI action plan that resulted from our process is a living document and roadmap whose target goals and deliverables will continue to evolve. Here, we offer a transparent account of how a large, multi-site biomedical research consortium achieved this, as well as the challenges and opportunities we encountered on this first step in our journey toward enacting JEDI principles in our sphere of influence. We hope that others seeking to engage in this work will gain valuable insights from our process, experience, and lessons learned.