American journal of human genetics最新文献

Regulatory single-nucleotide variants (rSNVs) in noncoding regions of the genome play a crucial role in gene transcription by altering transcription factor (TF) binding, chromatin states, and other epigenetic modifications. Existing expression quantitative trait locus (eQTL) methods identify genomic loci associated with gene-expression changes, but they often fall short in pinpointing causal variants. We introduce reg-eQTL, a computational method that incorporates TF effects and interactions with genetic variants into eQTL analysis. This approach provides deeper insights into the regulatory mechanisms, bringing us one step closer to identifying potential causal variants by uncovering how TFs interact with SNVs to influence gene expression. This method defines a trio consisting of a genetic variant, a target gene, and a TF and tests its impact on gene transcription. In comprehensive simulations, reg-eQTL shows improved power of detecting rSNVs with low population frequency, weak effects, and synergetic interaction with TF as compared to traditional eQTL methods. Application of reg-eQTL to GTEx data from lung, brain, and whole-blood tissues uncovered regulatory trios that include eQTLs and increased the number of eQTLs shared across tissue types. Regulatory networks constructed on the basis of these trios reveal intricate gene regulation across tissue types.

This article is based on the address given by the author at the 2024 meeting of The American Society of Human Genetics (ASHG) in Denver, CO. A video of the original address can be found at the ASHG website.

Various scientific and professional groups, including the American Medical Association (AMA), American Society of Human Genetics (ASHG), American College of Medical Genetics (ACMG), and the National Academies of Sciences, Engineering, and Medicine (NASEM), have appropriately clarified that certain population descriptors, such as race and ethnicity, are social and cultural constructs with no basis in genetics. Nevertheless, these conventional population descriptors are routinely collected during the course of clinical genetic testing and may be used to interpret test results. Experts who have examined the use of population descriptors, both conventional and ancestry based, in human genetics and genomics have offered guidance on using these descriptors in research but not in clinical laboratory settings. This perspective piece is based on a decade of experience in a clinical genomics laboratory and provides insight into the relevance of conventional and ancestry-based population descriptors for clinical genetic testing, reporting, and clinical research on aggregated data. As clinicians, laboratory geneticists, genetic counselors, and researchers, we describe real-world experiences collecting conventional population descriptors in the course of clinical genetic testing and expose challenges in ensuring clarity and consistency in the use of population descriptors. Current practices in clinical genomics laboratories that are influenced by population descriptors are identified and discussed through case examples. In relation to this, we describe specific types of clinical research projects in which population descriptors were used and helped derive useful insights related to practicing and improving genomic medicine.

Despite great progress, thousands of neurodevelopmental disorder (NDD) risk genes remain to be discovered. We present a computational approach that accelerates NDD risk gene identification using machine learning. First, we demonstrate that models trained solely on single-cell RNA sequencing data can robustly predict genes implicated in autism spectrum disorder (ASD), developmental and epileptic encephalopathy (DEE), and developmental delay (DD). Notably, we find differences in gene expression patterns of genes with monoallelic and bi-allelic inheritance patterns in the developing human cortex. We then integrate expression data with 300 orthogonal features, including intolerance metrics, protein-protein interaction data, and others, in a semi-supervised machine learning framework (mantis-ml) to train inheritance-specific models for these disorders. The models have high predictive power (area under the receiver operator curves [AUCs]: 0.84-0.95), and the top-ranked genes were up to 2-fold (monoallelic models) and 6-fold (bi-allelic models) more enriched for high-confidence NDD risk genes compared to genic intolerance metrics alone. Additionally, genes ranking in the top decile were 45 to 180 times more likely to have literature support than those in the bottom decile. Collectively, this work provides robust NDD risk gene predictions that can complement large-scale gene discovery efforts and underscores the importance of considering inheritance in gene risk prediction.

This article is based on the address given by the author at the 2024 meeting of The American Society of Human Genetics (ASHG) in Denver, CO. A video of the original address can be found at the ASHG website.

Access to a precise genetic diagnosis (PrGD) in critically ill newborns is limited and inequitable because the complex inclusion criteria used to prioritize testing eligibility omit many patients at high risk for a genetic condition. SeqFirst-neo is a program to test whether a genotype-driven workflow using simple, broad exclusion criteria to assess eligibility for rapid genome sequencing (rGS) increases access to a PrGD in critically ill newborns. All 408 newborns admitted to a neonatal intensive care unit between January 2021 and February 2022 were assessed, and of 240 eligible infants, 126 were offered rGS (i.e., intervention group [IG]) and compared to 114 infants who received conventional care in parallel (i.e., conventional care group [CCG]). A PrGD was made in 62/126 (49.2%) IG neonates compared to 11/114 (9.7%) CCG infants. The odds of receiving a PrGD were ∼9 times greater in the IG vs. the CCG, and this difference was maintained at 12 months follow-up. Access to a PrGD in the IG vs. CCG differed significantly between infants identified as non-White (34/74, 45.9% vs. 6/29, 20.7%; p = 0.024) and Black (8/10, 80.0% vs. 0/4; p = 0.015). Neonatologists were significantly less successful at predicting a PrGD in non-White than non-Hispanic White infants. The use of a standard workflow in the IG with a PrGD revealed that a PrGD would have been missed in 26/62 (42%) infants. The use of simple, broad exclusion criteria that increase access to genetic testing significantly increases access to a PrGD, improves access equity, and results in fewer missed diagnoses.

This article is based on the address given by the author at the 2024 meeting of The American Society of Human Genetics (ASHG) in Denver, CO. A video of the original address can be found at the ASHG website.

The five authors recently discovered their misattributed paternity (MP), two ascertaining that, decades ago, their pediatricians abetted the paternity deception. From their unique perspective, the authors critique medical organizations' current MP discovery guidance, identifying shortcomings, contradictions, and clinical and legal hazards. They also discuss opportunities to improve MP discovery management.

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.

Pathogenic variants resulting in protein phosphatase 2A (PP2A) dysfunction result in mild to severe neurodevelopmental delay. PP2A is a trimer of a catalytic (C) subunit, scaffolding (A) subunit, and substrate binding/regulatory (B) subunit, encoded by 19 different genes. De novo missense variants in PPP2R5D (B56δ) or PPP2R1A (Aα) and de novo missense and loss-of-function variants in PPP2CA (Cα) lead to syndromes with overlapping phenotypic features, known as Houge-Janssens syndrome (HJS) types 1, 2, and 3, respectively. Here, we describe an additional condition in the HJS spectrum in 26 individuals with variants in PPP2R5C, encoding the regulatory B56γ subunit. Most changes were de novo and of the missense type. The clinical features were well within the HJS spectrum with strongest resemblance to HJS type 1, caused by B56δ variants. Common features were neurodevelopmental delay and hypotonia, with a high risk of epilepsy, behavioral problems, and mildly dysmorphic facial features. Head circumferences were above average or macrocephalic. The degree of intellectual disability was, on average, milder than in other HJS types. All variants affected either substrate binding (2/19), C-subunit binding (2/19), or both (15/19). Five variants were recurrent. Catalytic activity of the phosphatase was variably affected by the variants. Of note, PPP2R5C total loss-of-function variants could be inherited from a non-symptomatic parent. This implies that a dominant-negative mechanism on substrate dephosphorylation or general PP2A function is the most likely pathogenic mechanism.