American journal of human genetics最新文献

By improving disease risk prediction, polygenic risk scores (PRSs) could have a significant impact on health promotion and disease prevention. Due to the historical oversampling of populations with European ancestry for genome-wide association studies, PRSs perform less well in other, understudied populations, leading to concerns that clinical use in their current forms could widen health care disparities. The PRIMED Consortium was established to develop methods to improve the performance of PRSs in global populations and individuals of diverse genetic ancestry. To this end, PRIMED is aggregating and harmonizing multiple phenotype and genotype datasets on AnVIL, an interoperable secure cloud-based platform, to perform individual- and summary-level analyses using population and statistical genetics approaches. Study sites, the coordinating center, and representatives from the NIH work alongside other NHGRI and global consortia to achieve these goals. PRIMED is also evaluating ethical and social implications of PRS implementation and investigating the joint modeling of social determinants of health and PRS in computing disease risk. The phenotypes of interest are primarily cardiometabolic diseases and cancer, the leading causes of death and disability worldwide. Early deliverables of the consortium include methods for data sharing on AnVIL, development of a common data model to harmonize phenotype and genotype data from cohort studies as well as electronic health records, adaptation of recent guidelines for population descriptors to global cohorts, and sharing of PRS methods/tools. As a multisite collaboration, PRIMED aims to foster equity in the development and use of polygenic risk assessment.

There has been considerable progress in building models to predict the effect of missense substitutions in protein-coding genes, fueled in large part by progress in applying deep learning methods to sequence data. These models have the potential to enable clinical variant annotation on a large scale and hence increase the impact of patient sequencing in guiding diagnosis and treatment. To realize this potential, it is essential to provide reliable assessments of model performance, scope of applicability, and robustness. As a response to this need, the ClinGen Sequence Variant Interpretation Working Group, Pejaver et al., recently proposed a strategy for validation and calibration of in-silico predictions in the context of guidelines for variant annotation. While this work marks an important step forward, the strategy presented still has important limitations. We propose core principles and recommendations to overcome these limitations that can enable both more reliable and more impactful use of variant effect prediction models in the future.

Secondary structures are non-canonical arrangements of nucleic acids due to intra-strand interactions, including base pairing, stacking, or other higher-order features that deviate from the standard double-helical conformation. While these structures are extensively studied in RNA, they can also form when DNA becomes single stranded, creating topological roadblocks that can impact essential DNA-based processes such as replication, transcription, and repair, ultimately affecting genome stability. The availability of a complete linear sequence of human genomes, including repetitive loci, enables the prediction of DNA secondary structures comparing across various regions. Here, we evaluate the intrinsic properties of linear single-stranded DNA sequences derived from sampling specialized human loci such as centromeres, pericentromeres, ribosomal DNA (rDNA), and coding regions from the CHM13 genome. Our comparative analysis of predicted secondary structures across human chromosomes revealed the heightened presence, complexity, and instability of secondary structures within the centromere, which gradually decreased toward the pericentromere onto chromosomes' arms, on average lowest in coding regions. Notably, centromeric repeats exhibited the highest level of topological complexity within both the active and divergent domains, even when compared to other repetitive tandem satellites, such as rDNA in acrocentric chromosomes. Our findings provide evidence of the intrinsic self-hybridizing properties of centromere repeats, which are capable of generating complex topological structures that may functionally correlate with chromosome missegregation, especially when centromeric chromatin is disrupted. Processes such as long non-coding RNA transcription, recombination, and other mechanisms that dechromatinize and unwind stretches of linear DNA in these regions create in vivo opportunities for the DNA acrobatics hereby predicted.

Population-level genetic studies are overwhelmingly biased toward European ancestries. Transferring genetic predictions from European ancestries to other ancestries results in a substantial loss of accuracy. Yet, it remains unclear how much various genetic factors, such as causal effect differences, linkage disequilibrium (LD) differences, or allele frequency differences, contribute to the loss of prediction accuracy across ancestries. In this study, we used gene expression levels in lymphoblastoid cell lines to understand how much each genetic factor contributes to lowered portability of gene expression prediction from European to African ancestries. We found that cis-genetic effects on gene expression are highly similar between European and African individuals. However, we found that allele frequency differences of causal variants have a striking impact on prediction portability. For example, portability is reduced by more than 32% when the causal cis-variant is common (minor allele frequency, MAF >5%) in European samples (training population) but is rarer (MAF <5%) in African samples (prediction population). While large allele frequency differences can decrease portability through increasing LD differences, we also determined that causal allele frequency can significantly impact portability when the impact from LD is substantially controlled. This observation suggests that improving statistical fine-mapping alone does not overcome the loss of portability resulting from differences in causal allele frequency. We conclude that causal cis-eQTL effects are highly similar in European and African individuals, and allele frequency differences have a large impact on the accuracy of gene expression prediction.

Female infertility is a common and complex health problem affecting millions of women worldwide. While multiple factors can contribute to this condition, the underlying cause remains elusive in up to 15%-30% of affected individuals. In our large genome-wide association study (GWAS) of 22,849 women with infertility and 198,989 control individuals from the Finnish population cohort FinnGen, we unveil a landscape of genetic factors associated with the disorder. Our recessive analysis identified a low-frequency stop-gained mutation in TATA-box binding protein-like 2 (TBPL2; c.895A>T [p.Arg299Ter]; minor-allele frequency [MAF] = 1.2%) with an impact comparable to highly penetrant monogenic mutations (odds ratio [OR] = 650, p = 4.1 × 10^-25). While previous studies have linked the orthologous gene to anovulation and sterility in knockout mice, the severe consequence of the p.Arg299Ter variant was evidenced by individuals carrying two copies of that variant having significantly fewer offspring (average of 0.16) compared to women belonging to the other genotype groups (average of 1.75 offspring, p = 1.4 × 10^-15). Notably, all homozygous women who had given birth had received infertility therapy. Moreover, our age-stratified analyses identified three additional genome-wide significant loci. Two loci were associated with early-onset infertility (diagnosed before age 30), located near CHEK2 and within the major histocompatibility complex (MHC) region. The third locus, associated with late-onset infertility, had its lead SNP located in an intron of a long non-coding RNA (lncRNA) gene. Taken together, our data highlight the significance of rare recessive alleles in shaping female infertility risk. The results further provide evidence supporting specific age-dependent mechanisms underlying this complex disorder.

Underrepresentation in clinical genomics research limits the generalizability of findings and the benefits of scientific discoveries. We describe the impact of patient-centered, data-driven recruitment and retention strategies in a pediatric genome sequencing study. We collaborated with a stakeholder board, conducted formative research with adults whose children had undergone genomic testing, and piloted and revised study approaches and materials. Our approaches included racially, ethnically, and linguistically congruent study staff, relational interactions, study visit flexibility, and data-informed quality improvement. Of 1,656 eligible children, only 6.5% declined. Their parents/legal guardians were 76.9% non-White, 65.6% had public health insurance for the child, 49.9% lived below the federal poverty level, and 52.8% resided in a medically underserved area. Among those enrolled, 87.3% completed all study procedures. There were no sociodemographic differences between those who enrolled and declined or between those retained and lost to follow-up. We outline stakeholder-engaged approaches that may have led to the successful enrollment and retention of diverse families. These approaches may inform future research initiatives aiming to engage and retain underrepresented populations in genomics medicine research.

Polycystic ovarian syndrome (PCOS) is an endocrine syndrome that affects a large portion of women worldwide. This proteogenomic and functional study aimed to uncover candidate therapeutic targets for PCOS. We comprehensively investigated the causal association between circulating proteins and PCOS using two-sample Mendelian randomization analysis. Cis-protein quantitative trait loci were derived from six genome-wide association studies (GWASs) on plasma proteome. Genetic associations with PCOS were obtained from a large-scale GWAS meta-analysis, FinnGen cohort, and UK Biobank. Colocalization analyses were performed to prioritize the causal role of candidate proteins. Protein-protein interaction (PPI) and druggability evaluation assessed the druggability of candidate proteins. We evaluated the enrichment of tier 1 and 2 candidate proteins in individuals with PCOS and a mouse model and explored the potential application of the identified drug target. Genetically predicted levels of 65 proteins exhibited associations with PCOS risk, with 30 proteins showing elevated levels and 35 proteins showing decreased levels linked to higher susceptibility. PPI analyses revealed that FSHB, POSTN, CCN2, and CXCL11 interacted with targets of current PCOS medications. Eighty medications targeting 20 proteins showed their potential for repurposing as therapeutic targets for PCOS. EGLN1 levels were elevated in granulosa cells and the plasma of individuals with PCOS and in the plasma and ovaries of dehydroepiandrosterone (DHEA)-induced PCOS mouse model. As an EGLN1 inhibitor, administration of roxadustat in the PCOS mouse model elucidated the EGLN1-HIF1α-ferroptosis axis in inducing PCOS and validated its therapeutic effect in PCOS. Our study identifies candidate proteins causally associated with PCOS risk and suggests that targeting EGLN1 provides a promising treatment strategy.

Individuals with obesity caused by biallelic pathogenic LEPR (leptin receptor) variants can benefit from setmelanotide, the novel MC4R agonist. An ongoing phase 3 clinical trial (NCT05093634) includes individuals with obesity who carry a heterozygous LEPR variant, although the obesogenic impact of these variants remains incompletely evaluated. The aim of this study was to functionally assess heterozygous variants in LEPR and to evaluate their effect on obesity. We sequenced LEPR in ∼10,000 participants from the French RaDiO study. We found 86 rare heterozygous variants. Each identified variant was then investigated in vitro using luciferase and western blot assays. Using the criteria of the American College of Medical Genetics and Genomics (ACMG), including the strong criterion related to functional assays, we found 12 pathogenic LEPR variants. Most heterozygotes did not present with obesity, and we found no association between these pathogenic variants and body mass index (BMI). This lack of association between pathogenic LEPR variants and obesity risk or BMI was confirmed using exome data from 200,000 individuals in the UK Biobank. In the literature, among 55 reported heterozygotes for of a rare pathogenic LEPR variant, only 27% had obesity. In conclusion, monoallelic pathogenic LEPR variants were functionally tested, and they do not elevate the risk of obesity or BMI levels. This raises questions about the use of setmelanotide, a costly drug with potential side effects, based solely on the presence of a heterozygous LEPR variant.

One of the regulatory mechanisms influencing the functional capacity of genes is alternative splicing (AS). Previous studies exploring the splicing landscape of human tissues have shown that AS has contributed to human biology, especially in disease progression and the immune response. Nonetheless, this phenomenon remains poorly characterized across human populations, and it is unclear how genetic and environmental variation contribute to AS. Here, we examine a set of 115 Indonesian samples from three traditional island populations spanning the genetic ancestry cline that characterizes Island Southeast Asia. We conduct a global AS analysis between islands to ascertain the degree of functionally significant AS events and their consequences. Using an event-based statistical model, we detected over 1,500 significant differential AS events across all comparisons. Additionally, we identify over 6,000 genetic variants associated with changes in splicing (splicing quantitative trait loci [sQTLs]), some of which are driven by Papuan-like genetic ancestry, and only show partial overlap with other publicly available sQTL datasets derived from other populations. Computational predictions of RNA binding activity reveal that a fraction of these sQTLs directly modulate the binding propensity of proteins involved in the splicing regulation of immune genes. Overall, these results contribute toward elucidating the role of genetic variation in shaping gene regulation in one of the most diverse regions in the world.

Vitiligo is a common autoimmune disease characterized by patches of depigmented skin and overlying hair due to destruction of melanocytes in the involved regions. We investigated the relationship between vitiligo risk and vitiligo age of onset (AOO) using a vitiligo polygenic risk score that incorporated the most significant SNPs from genome-wide association studies. We find that vitiligo genetic risk and AOO are strongly inversely correlated; subjects with higher common-variant polygenic risk tend to develop vitiligo at an earlier age. Nevertheless, the correlation is not simple. In individuals who carry a single high-risk major histocompatibility complex class II haplotype, the effect of additional polygenic risk on vitiligo AOO is reduced. Particularly among those with early-AOO vitiligo (onset ≤12 years of age), genetic risk can reflect contributions from high common-variant burden but also rare variants of high effect and sometimes both. While the heritability of vitiligo is relatively high, and we here show that genetic risk factors predict vitiligo AOO, vitiligo is never congenital, and thus environmental triggers also play an important role in disease onset.