The American Journal of Human Genetics

Motivation: Horizontal pleiotropy remains a major challenge for Mendelian randomization (MR), particularly as increasingly large genome-wide association studies identify many genetic instruments for complex traits. Existing pleiotropy-robust methods rely on assumptions regarding the proportion or distribution of valid instruments that may be violated when pleiotropy is widespread. Results: We developed CaPri, a Bayesian MR framework that jointly estimates causal and pleiotropic effects. CaPri allows a potentially arbitrary proportion of instruments to contribute to a shared directional pleiotropic component and estimates the posterior probability and magnitude of the causal effect, instrument-specific posterior inclusion probabilities for pleiotropy, and the shared pleiotropic effect. Across simulations, CaPri maintained appropriate false positive control and accurate causal effect estimation regardless of the proportion of pleiotropic instruments, while accurately identifying pleiotropic instruments and estimating their shared effect. Performance remained robust across varying causal and pleiotropic effect sizes and exposure-outcome confounding, although sufficiently large outcome GWAS sample sizes were important for stable inference and limited prior dependence. In real data analyses, CaPri reported low posterior probabilities of causality across negative controls and strong support for established positive control relationships. Application to alcohol consumption and coronary artery disease further demonstrated its ability to identify potentially meaningful pleiotropic instruments. CaPri provides a complementary framework for strengthening causal inference and triangulation across MR methods with different identifying assumptions. Availability and implementation: CaPri is available at https://github.com/OuRen0104/CaPri.

Evolutionary theory offers a unifying explanation for the genetics of complex traits and diseases by modeling how a trait and its genetic basis are maintained by evolutionary forces. A growing body of evidence suggests that linkage disequilibrium (LD) couples a variant's evolution to that of the genetic background it resides on. However, widely used single-variant theories omit this coupling between variants and the background, or hold the background fixed. We close this gap by developing a model of linkage LD evolution accounting for stabilizing selection, recombination, and genetic drift. The model deduces the genetic architecture of individual variants subject to the co-evolving genetic background. More efficient selection in larger populations is partially offset by slower decay of existing LD and increased generation of new LD. Conversely, noisier LD in smaller populations exposes the variants to episodes of stronger selection. We ask whether ancestry differences in the relationship between effect-size magnitude and minor allele frequency require different selection strengths or can arise from differences in demographic history alone. Stabilizing selection needs to be more than three times stronger in Africans than in Europeans under the constant-size demography, whereas an Out-of-Africa demography alone can explain most of the observed frequency-dependence gap. As this example indicates, selection strength is in general not identifiable from the frequency--effect-size relationship alone, because different combinations of selection and demographic history can produce the same pattern in data with further redundancy added by LD.

Polygenic scores (PGS) are increasingly common in human genetics research, yet their performance and portability vary substantially across traits and populations. We systematically evaluate prediction accuracy and cross-ancestry portability for 3,994 published PGSs across the phenome in the ancestrally diverse CCPM biobank. We identify widespread trait-specific and cross-trait associations, demonstrating both the utility and breadth of existing polygenic predictors. However, predictive effects frequently differ across ancestry groups, highlighting persistent challenges for portability that threaten equity in downstream applications. We further find that accuracy and portability depend strongly on whether the PGS is continuous or thresholded. Finally, through hierarchical mixed modeling, we identify score-development, genome features, and cohort characteristics associated with improved PGS accuracy and portability. These results highlight key factors supporting future development of robust and portable scores for research and clinical translation.

Background Short-read genome sequencing generates the signal necessary to detect balanced reciprocal translocations at single-nucleotide resolution, but standard structural variant callers represent them as breakend pairs. Structural variant callers produce thousands of breakend records per genome, the majority of which are false positives arising from segmental duplications, repetitive elements, and reference assembly artifacts. This specificity problem has prevented balanced translocation detection from entering routine genome sequencing workflows. We developed a highly specific pipeline to resolve balanced reciprocal translocations and applied it across five clinically distinct cohorts to characterize the prevalence and diagnostic contribution of balanced reciprocal translocations. Methods Short-read genome sequencing data from 16,131 individuals across five cohorts were analyzed: healthy participants from the 1000 Genomes Project (n=3,202), probands with structural birth defects and their family members (CHOP BDB cohort; n=2,875, including 1017 probands), probands enrolled in the GREGoR consortium (GREGoR cohort; n=3,574 probands), probands with undiagnosed disease and their family members enrolled in the Undiagnosed Diseases Network (UDN cohort; n=6,071 including 2,027 probands) and neonates who underwent rapid genome sequencing (CHOP Baby Eagle cohort; n=409). A computational pipeline was developed to characterize breakend events from standard structural variant callers to high-confidence balanced translocation calls by exploiting well-characterized properties of true balanced translocation signatures in short-read data. Translocation breakpoints were resolved to single-nucleotide precision and interrogated for disruption of known disease genes or potential regulatory consequence otherwise. A subset of the identified breakpoints was confirmed by Sanger sequencing. Results Balanced reciprocal translocations were identified in 0.196% of structural birth defect probands (2/1,017), 0.25% of the GREGoR probands (9/3,574), 0.39% of the UDN probands (8/2,027) and 0.24% of neonates who underwent rapid genome sequencing (1/409). Parental carrier frequency in the birth defects cohort was 0.269% (5/1,858), consistent with previously reported estimates in similarly ascertained families, and 0.098% (3/3,040) in the UDN cohort. All six translocations identified in the birth defects cohort were orthogonally validated using Sanger sequencing, confirming pipeline specificity. Approximately 31% of translocations across the three affected cohorts disrupted a known disease gene at the breakpoint, establishing or refining a molecular diagnosis in five cases previously unresolved. The single translocation identified in the newborn cohort disrupted no disease gene and was classified as a variant of uncertain significance.

Protein coding variants span the gamut of structural and functional effects on proteins, and they have similarly heterogeneous effects on traits. Their functional effects can be predicted and categorized, particularly using machine learning models, and integrating these predictors benefits gene discovery. However, it remains unclear how well known structural features and predicted functional scores explain the heterogeneous effects of protein coding variants on human complex traits. We introduce the Kernel Allelic Series Model (KASM), which integrates multiple functional annotations to form a kernel describing how allelic effects within a gene covary. We apply it to exome data from 26 traits and 786,871 individuals from UK Biobank and All of Us, integrating 35 annotations describing protein structure and predicting allelic effects. We estimate that rare missense heritability is at least 1.27{+/-}0.09% on average, 1.83{+/-}0.11% under our most flexible model, exceeding pLoF heritability (0.87{+/-}0.05%) and far exceeding missense burden heritability as previously estimated (0.54{+/-}0.04%). AlphaMissense is the single most enriched missense predictor, but a composite score doubles its enrichment. A series of structural features is enriched for heritability, from amino acid chemical features and structural motifs to residue contacts and protein-protein interaction interfaces. Examining pairs of variants, we find that most missense effects are modestly correlated, but an exception is when residues are close in 3D structure but distant in 1D sequence; such pairs also covary across species. Finally, we analyze individual genes and identify gene-specific enrichments, like a 95-fold enrichment among PIEZO1 hydrogen bond residues, that nominate functional hypotheses.

Background Gene-level constraint and conservation scores are widely used to prioritise disease genes and to benchmark genomic language models. Purifying selection acts weakly on phenotypes manifesting after reproduction, so these scores are expected to carry less information about late-onset disease. An onset-dependent gradient has been demonstrated for human allele frequencies but not for the gene-level scores used in practice. Results We built an age-of-onset axis for 2,885 Mendelian diseases from Orphanet and fitted cross-species conservation (241-way phyloP) and human-population constraint (shet) on the same 5,322 gene-disease pairs against matched controls, to test whether one decays faster with onset. Their onset slopes differed by 0.23 logit units (97.5% confidence interval -0.10 to 0.56), equivalent to 0.024 units of area under the receiver operating characteristic curve (AUROC) in a design resolving 0.049; the interval excludes constraint losing discriminative power faster than conservation by more than 0.011 AUROC units, widening to 0.033 when somatic associations are dropped. The usual explanation, that early-onset disease genes are developmental, pleiotropic and haploinsufficient by construction, did not account for the contrast; conditioning barely moved it. The answer did, however, depend on the constraint metric: three alternatives yielded larger contrasts excluding zero, though paired comparison could not distinguish the estimates. The metrics also diverged on recessive disease genes, with shet ranked below the gene pool (AUROC 0.487) and loss-of-function depletion above it (0.533), though, against matched controls, both ranked them below (0.457 and 0.450). Evolutionary scores improved a non-evolutionary baseline by 17 to 23%, with no onset gradient. Conclusions Conservation and constraint do not lose their value at measurably different rates as disease onset rises, and with only 283 late-onset Mendelian diseases annotated genome-wide, the question cannot be settled more finely. The gene-class explanation does not account for the contrast. Metrics that rank genes near-identically can still yield opposite onset-stratified answers, so a result reported by one metric carries a sensitivity that its confidence interval does not show. No constraint metric identifies recessive disease genes against matched controls, so a pipeline with recessive disease in scope needs a correction that switching metric does not supply.

Local genetic correlation is estimated as a ratio of a local genetic covariance to the geometric mean of two local heritabilities. LAVA, the standard implementation, returns that ratio only where both traits clear a univariate heritability test at P < 0.05, a rule its documentation presents as a stabiliser. We show that it is a selection event acting on the denominator, and characterise what it does to the estimates that survive. Under the noncentral Wishart model LAVA's own interval routine assumes, the gate-conditional mean of the estimator is affine in the true correlation, with slope and intercept available in closed form from quantities every run already reports, that is, the components retained, the two univariate P-values, and the sample-overlap correlation. Attenuation is therefore a property of the analysis, not of the method. Across six psychiatric trait pairs the measured slope runs from 0.579 to 0.923 and the closed form predicts each to within 0.007 with nothing fitted to the panel, while within a single pair it runs from 0.371 to 0.971, so no single factor can correct it. Overlap and the filter jointly manufacture correlation where the truth is zero, most where power is lowest. We invert the closed form into a gate-aware correction, which reduces mean absolute bias from 0.247 to 0.026 over 48 simulation cells, and pair it with a diagnostic that decides whether the correction is identified. Three pairs fail it, all three sharing one trait whose local heritabilities are indistinguishable from a complete local null.

Psoriasis is associated with increased cardiovascular morbidity, but the extent and structure of the shared genetic architecture remain incompletely understood. We integrated large-scale European-ancestry genome-wide summary statistics for psoriasis and seven cardiovascular diseases to quantify genetic overlap, model shared liability and prioritize candidate mechanisms. Psoriasis showed modest genetic correlations with all cardiovascular outcomes, but mixture modelling estimated that more than half of psoriasis-associated non-zero variants were shared with five cardiovascular diseases. Genomic structural equation modelling of a latent psoriasis-CVD factor identified 167 independent risk loci, including 50 not genome-wide significant in any component trait. Multimodal prioritization nominated 473 candidate genes and 90 approved compounds with therapeutic relevance, including shared targets involving PDE4B, GLP1R, KEAP1 and PRDM16. Mediation and Mendelian randomization analyses implicated cardiometabolic traits, although no single trait accounted for more than half of the shared liability. These results prioritize genetic mechanisms and pharmacological hypotheses for psoriasis-associated cardiovascular multimorbidity.

Multiplexed assays of variant effect (MAVEs) and computational methods have advanced variant classification but have struggled to distinguish the molecular mechanism of variants with sufficient accuracy, limiting utility for multi-mechanism genes. Here, we coupled single-cell RNA-seq profiling with supervised machine learning to predict the mechanism of variants in CASR, a gene in which gain of function (GOF) variants cause hypocalcemia and loss of function (LOF) variants cause hypercalcemia. We engineered endogenous CASR-depleted HEK293 cells to express a single exogenous variant copy, measured their transcriptional profiles at single-cell resolution, trained a multi-class machine learning model on 96 expert-annotated variants (25 benign, 35 LOF, 36 GOF), and predicted mechanism for 157 variants of uncertain significance, of which 16 were predicted GOF and 54 LOF. The model showed high performance (macro F1 0.98, overall accuracy 0.98), and its predictions were corroborated by clinical phenotype and serum calcium measurements across more than 300,000 patients in a commercial genetic testing database. These results highlight the effectiveness of using molecular mechanism annotations with scRNA-seq to train models that distinguish molecular mechanisms of pathogenicity. The approach has immediate relevance for clinical variant classification and establishes a foundation for mechanism-guided clinical management in disorders with mechanism-dependent interventions, with potential application to other multi-mechanism genes.

Goh et al. recently reported that most recurrent copy number variants (CNVs) are far less penetrant for intellectual disability (ID) than previously estimated, and recommended that fourteen should no longer be reported. We show that the lower estimates arise mainly from assuming the population prevalence of ID alone, while case counts derive from broad diagnostic referral cohorts in which about half of individuals were referred for other indications. The proposed formula estimates a risk difference rather than penetrance, and cases and controls were selected, assayed and phenotyped differently. Restricting the outcome to ID also leaves the analysis unable to inform risk for autism, epilepsy, psychiatric, cardiac and other phenotypes. We recommend that families receive phenotype-specific absolute risks for carriers alongside background risks, and that reporting decisions be made locus by locus by guideline bodies, considering all relevant phenotypes. The recalculated estimates inform this debate but are not yet sufficient grounds for changing reporting practice.

Abstract INTRODUCTION Despite major advances in genomic sequencing and analytical pipelines for rare disorders, more than 50% of affected individuals remain without a molecular diagnosis. One challenge is the interpretation of inherited variants exhibiting incomplete penetrance (IP). MATERIALS AND METHODS We developed a pipeline to systematically investigate inherited variants as the cause of rare disease in 4,325 families, consisting of affected probands and unaffected parents, from the Broad Center for Mendelian Genomics, part of the GREGoR Consortium. As a proof-of-concept of further omics follow-up, epigenomic and transcriptomic analyses were performed in one family harboring an inherited disease-causing frameshift variant in CHD8. RESULTS We identified 81 families where the affected proband harbored a putative disease-causing variant inherited from a reportedly unaffected parent. Clinical re-evaluation identified 30 families in which an inherited variant was considered diagnostic, including six previously missed diagnoses. In this cohort, inherited IP variants account for 1.6% of the diagnosed cases, with an overall diagnostic yield of 0.4% in the cohort. Methylation analysis of the CHD8 family confirmed a positive CHD8 clinical episignature in the male proband, but also in the reportedly unaffected mother. Transcriptomic analysis showed that despite similar CHD8 haploinsufficiency, the proband and mother displayed divergent downstream transcriptional effects. DISCUSSION This study highlights the importance of considering parentally inherited IP variants in rare disease, with at least 1 in 60 diagnostic variants inherited from a reportedly unaffected parent, and demonstrates the potential of integrating omics evaluation to improve variant interpretation.

Polygenic scores (PGSs) have progressed toward clinical utility in recent years, demonstrating odds ratios that are comparable with both lifestyle-related and monogenic risk factors for several complex diseases. However, PGS implementation relies on developing analytically and clinically valid assays. Methods for developing polygenic models (PGMs) that incorporate tens of thousands to millions of variants require either genome-wide chip genotyping combined with imputation or whole genome sequencing, limiting their portability. Additionally, variants may be in regions where the local genomic sequence makes it difficult to assay, and multi-allelic or strand ambiguous variants can make bioinformatics more complicated. Because of these challenges, attempts to develop a reliable assay for published PGMs can produce multiple variations from the same underlying model, hindering routine clinical deployment and incorporation into multi-factorial risk prediction models. In this analysis, we present an adjustment to the previously published select and shrink with summary statistics (S4) PGM method designed to optimize performance for realistic clinical use by restricting to a pre-specified number of available SNPs that are well-captured by genotyping arrays. We compare the performance of this oligogenic S4 method (S4-Oligo) to currently published PGSs as well as the full S4 model for prediction of breast, colorectal, ovarian, and prostate cancers in the All of Us Research Program, Biobank Japan, and UK Biobank. We find that S4-Oligo matches or exceeds the accuracy of published PGMs at comparable model sizes while retaining between 88% and 99% of the performance of the original S4 model when limited to 1,000 variants.

While polygenic gene-by-environment (GxE) signals are increasingly detected in biobank data, it is often unclear whether they reflect the phenotype's measurement scale or one of two biologically distinct mechanisms: heterogeneous genetic effects across environmental strata (effect heterogeneity) or systematically larger but correlated effects in one stratum (amplification). We introduce REGENA (Robust Estimator of Gene-ENvironment Architectures), a variance-component method that disentangles these contributions at biobank scale by learning a phenotype transformation that minimizes scale-induced GxE, thereby assessing scale-dependence while jointly estimating the cross-environment genetic correlation and environment-specific genetic variances. In simulations, REGENA is well-calibrated and powerful while recovering the transformation underlying scale-dependent GxE. Applied to 43 quantitative traits and seven environmental exposures in 291,273 UK Biobank individuals, REGENA finds that roughly half of trait-environment pairs with significant GxE are scale-dependent. Among the remaining scale-independent trait-environment pairs, GxE heritability accounted for an average of 15% of additive heritability and was driven primarily by effect heterogeneity (accounting for 71% of GxE heritability on average) although there was substantial variation in its relative contribution across trait-environment pairs. Environment-specific effects tend to make a larger contribution at lower allele frequencies, accounting for 6% of genetic variance in low-frequency variants versus 3.9% at common variants (signed test p = 1.7e-24). Modeling these distinctions improves environment-specific polygenic prediction (14% on average), with the largest gains for pairs whose genetic effects are least correlated across environments (p = 4.0e-3; 3.8-fold for testosterone stratified by sex and 2.32-fold for waist-hip ratio stratified by alcohol consumption). Further, environment-stratified GWAS detects 174 loci missed by pooled GWAS with 67 having opposite-signed effects across strata.

Structural variants (SVs) are larger genetic rearrangements such as deletions, duplications, and insertions and other forms of structural alterations. Given their large size, they can have an outsized effect on phenotype, however, the contribution of SVs to the genetic architecture of orofacial clefts (OFCs) is not well understood. In this study, we sought to better understand the contribution SVs to OFC risk by characterizing de novo SVs (dnSVs) in 1,549 probands with an OFC. Overall, we called 375 dnSVs in 307 probands (0.242 dnSVs per genome). The dnSV rate per genome was highest among individuals with cleft lip (CL, 0.29), followed by cleft lip and palate (CLP, 0.25) and cleft palate only (CP, 0.20), though differences across subtypes were not statistically significant. There were also no differences between sex or syndromic status, but we did identify a significant difference in the rate of duplications across OFC subtypes (p=0.005). Compared to CP (0.028), duplication rates were significantly higher for both CL (0.121; p = 0.003) and CLP (0.096; p = 0.002), suggesting a potential etiologic mechanism for CL/P. We systematically curated each dnSV to classify its potential for influencing OFC risk by examining the genes and regulatory elements disrupted by the SV and evidence for their role in craniofacial development or OFCs. We identified 22 dnSVs in regions associated with recurrent genomic disorders, including seven deletions and one duplication of the well-established OFC risk locus 22q11.2. Additionally, we identified 12 large (>1Mb) SVs, with eight meeting criteria for a likely pathogenic classification based on total gene involvement, and seven dnSVs disrupting well-known genes associated with autosomal dominant forms of OFC. The most notable was SATB2, for which we found four dnSVs, all in individuals with CP. Next, by combining evidence from recurrent dnSVs and de novo single nucleotide variants with Hi-C contact maps and predicted chromatin states from developing craniofacial tissues, we found new candidate genes for OFC risk including FHAD1, NBEA, NR3C2, and SPHKAP. Overall, dnSVs classified as likely pathogenic accounted for putative molecular diagnoses in approximately 1.67% of the cohort, with an additional 0.96% having variants of unknown significance with plausible risk for OFCs. Our results support dnSVs as an important component of OFC etiology and suggest their inclusion in analyses may improve diagnostic yield for affected individuals.

Human genetics needs benchmarks for evaluating variant scores against observed phenotypic consequences. We therefore present UKBBGym, which evaluates coding and non-coding scores against plasma protein abundance and quantitative traits observed in carriers of ultra-rare variants in the UK Biobank. This design avoids ascertainment biases of clinical labels, potential physiological mismatch of experimental assays, and confounding pertaining to common-variant associations. UKBBGym recapitulates relative performance on pathogenicity prediction while enabling comparison across variant classes and molecular mechanisms. This shows that scores designed for coding and splicing variants correlate more strongly with protein abundance and quantitative traits than scores modelling transcriptional regulation. Moreover, experimental assays do not consistently outperform computational scores for predicting missense effects. Comparing captured to detectable variance indicates scope for improvement, particularly for indels and loss-of-function variants. Finally, for coding variants, UKBBGym can be reproduced from public summary statistics, providing an accessible population-phenotype benchmark complementary to clinical labels and experimental assays.

Childhood apraxia of speech (CAS) is a neurodevelopmental disorder of speech-motor planning and programming, associated with literacy, academic, and psychosocial difficulties. We performed whole-exome sequencing in 11 families comprising 21 affected and 15 unaffected individuals with phenotyping spanning speech, language, cognition, literacy, and motor function. We identified 60 prioritized variant observations representing 22 unique variants across 20 genes with family-based segregation analysis assessed in informative relatives; 5 arose de novo. Our cohort included multiple affected siblings and extended relatives, with several families carrying multiple prioritized variants, suggesting cumulative or oligogenic susceptibility. Speech and literacy impairment co-occurred with oral-motor, feeding, and fine-motor difficulties. BrainSpan co-expression analysis identified MECP2 and ANK2 as the genes with the highest observed connectivity in the sampled postnatal network. Exploratory analysis of the KOLF2.1J perturbation atlas in undifferentiated human induced pluripotent stem cells identified a positive LMO3 expression-response score following NIPBL repression and opposing LMO3 and ANK2 response directions within a cohesin-associated perturbation cluster. These observations motivate testing in human neural cells. Functional enrichment, protein-association, and developmental-expression analyses supported shared biological themes involving chromatin regulation, neuronal signaling, and extracellular-matrix organization. Among six families, multiple affected relatives shared prioritized variants across a unique set of several genes, demonstrating multilocus candidate profiles whose individual and combined effects may contribute to CAS susceptibility. We propose four novel CAS candidate genes: LMO3 and PCDH10, previously linked to human accelerated regions, as well as MYCBP2 and PTPRS. Our work provides multiple testable hypotheses for future variant-specific functional validation in speech-motor development.

Genetic prediction of clinically defined disease subtypes could advance precision medicine, but polygenic risk scores (PRSs) often show limited ability to distinguish subtypes, either because the subtypes share genetic architecture or because available PRSs are inaccurate. We introduce GenSep, a statistical framework that estimates the oracle case-case AUC attainable if the true additive genetic values of both subtypes were known, and the proportion of this genetic separation recovered by subtype-specific PRSs. Across 18 subtype pairs in UK Biobank, oracle AUCs ranged from 0.605 to 1.000, whereas current PRSs achieved a median observed case-case AUC of only 0.530 (range, 0.510-0.754), corresponding to recovery of a median of 4.1% (range, 0.3-15.5%) of the genetic separation variance available to a perfect predictor. Oracle AUCs were generally consistent across UK Biobank, All of Us, FinnGen and the Million Veteran Program, and across European, African and Admixed American ancestry groups. FinnGen-trained PRSs, despite 2.6-to 3.9-fold larger discovery samples, performed no better than internally trained PRSs, and substantially larger consortium GWAS raised case-case AUC by 0.06 on average (range, 0.003-0.089). GenSep separates subtype pairs limited by intrinsic genetic overlap from those limited by current PRS accuracy, identifying where larger studies and better predictors could improve genetic discrimination.

Tandem repeat expansions cause more than 60 Mendelian disorders but are difficult to detect and size accurately with short-read sequencing because of their length and repetitive structure. Long-read sequencing (LRS) resolves these loci directly, yet analytic tooling for nanopore sequencing data has lagged behind that available for other long-read platforms. Here we present Mahi-mahi, a tandem repeat genotyper designed for nanopore reads that reports allele-level repeat length, motif composition, interruptions, and read-level flags for systematic basecalling artifacts. Across six Genome in a Bottle samples, whole-genome and adaptive sampling (real-time targeted) data returned calls within one repeat unit of each other for 829 of 832 alleles, and Mahi-mahi calls were within one repeat unit of orthogonal HiFi data at 95.6% of compared alleles after excluding three loci at which the two tools define the repeat differently. In 245 samples from the 1000 Genomes Project, Mahi-mahi calls were within one repeat unit of assembly-derived calls for 98.6% of 10,927 alleles at 57 loci, and Mahi-mahi distinguished pathogenic from nonpathogenic RFC1 alleles of overlapping size by motif. Across 14 Coriell reference lines, Mahi-mahi identified the expected expansion in every line and agreed with orthogonal assays to within two repeat units at HTT, ATXN1, DMPK, and PABPN1. Finally, in 103 samples from three laboratories, Mahi-mahi detected an expanded allele in all samples in which prior testing had reported a pathogenic expansion, assigned the same category as the referring laboratory for 98% of 180 genotypes, and identified known ATXN1 interruption structure. Our results demonstrate that Mahi-mahi accurately identifies repeat expansions at disease-associated loci and could serve as a single assay to evaluate repeat expansions.

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

Partitioned-heritability studies have shown which tissues carry the heritability of complex traits, but whether the regulatory layer whose annotations are most informative for heritability within a tissue differs between traits has not been tested, because measured annotations for different layers come from different sets of biosamples. Sequence-to-function models predict the allelic effect of every variant on several regulatory layers with a single model, so that layers can be compared on the same variants within the same tissues. We grouped AlphaGenome Atlas predictions into 12 lineages and six regulatory layers and partitioned the heritability of 11 complex traits with stratified LD score regression. Traits localized largely to the expected lineages, and within these lineages the most informative predicted layer differed between traits (P = 3.6 x 10^-8). Predicted chromatin-accessibility (DNase) effects were the most informative for height and bone mineral density, whereas predicted transcription-initiation (CAGE) effects were the most informative for Alzheimer's disease, most brain traits and cholesterol levels. The difference persisted across sensitivity analyses, and the predicted contrast held for three related traits whose layers had been predicted in advance (P = 2 x 10^-7). Predicted CAGE effects also remained informative after conditioning on fine-mapped eQTLs and measured regulatory annotations. These results identify differences across traits in the heritability information captured by predicted regulatory layers and offer testable hypotheses for investigating the regulatory basis of complex traits.