The genetics of complex traits in Africa has been historically understudied, which can contribute to healthcare inequalities. Here, we present observations of 27 anthropometric, cardiovascular, and blood biomarker measurements across 2,124 individuals from sub-Saharan Africa for whom we also have dense genotype data. First, we identified trait values that differ significantly across populations and subsistence lifestyles (e.g., hemoglobin levels and height). We then identified traits with high degrees of sexual dimorphism (e.g., weight and grip strength). ADMIXTURE analyses revealed substantial population structure in our dataset, and many of the phenotypes studied here are correlated with genetic ancestry components, particularly skin color and body size traits. A variance partitioning approach further revealed traits in which much of the SNP heritability is due to polymorphisms that also contribute to differences between ancestry components. Following genomic imputation, we performed genome-wide association studies (GWASs) for all 27 traits and identified >100 independent autosomal SNPs with genome-wide significant associations for at least one trait (p < 5 × 10[-8]). Many of these trait-associated variants are rare outside of Africa (minor-allele frequency [MAF] < 1%). We found that 100 kb windows surrounding the top GWAS hits from our African-ancestry cohort were enriched for trait associations in an identically sized European cohort and vice versa. We performed a more detailed analysis of height prediction from genetic data, finding that genome-wide admixture proportions predict height in Africans better than polygenic predictors based on large-scale European height GWASs.

Pathogenic variants in the autoimmune regulator (AIRE) cause autoimmune polyendocrine syndrome type 1 (APS-1), a rare primary immunodeficiency disease with symptoms including hypoparathyroidism, adrenal insufficiency, and chronic mucocutaneous candidiasis. AIRE increases the expression and presentation of tissue-specific genes expressing "self" antigens in the developing T cell niche, thus triggering the elimination of self-reactive T cells and preventing autoimmunity. Earlier diagnoses can benefit those with APS-1, and APS-1 diagnosis by AIRE sequencing is increasingly common. However, two-thirds of reported clinical variants are missense, and more than half of these are variants of uncertain significance (VUSs). Cell-based variant functional assays can provide strong evidence toward more informative variant classification, but these are carried out reactively, often years after clinical presentation. By contrast, proactively assessing all possible missense variants could provide immediate evidence to guide genetic diagnosis, even for never-before-seen variants. Here, we used an insulin-promoter-driven reporter to proactively assess the function of 9,790 AIRE missense variants. The resulting AIRE variant effect map both validates and extends current biochemical knowledge, concords with pathogenicity annotations, and provides proactive evidence for 70% of previously reported VUSs. Placing our map in the context of both an international APS-1 cohort and the UK Biobank revealed quantitative genotype-phenotype correlations. Moreover, evidence from our variant effect map resolved 32% of current VUSs. Together, our proactive resource of AIRE variant impacts offers the potential to improve outcomes via more rapid and definitive APS-1 diagnosis.

RNA-binding proteins (RBPs) regulate gene expression, and a number of RBPs have been implicated in brain function and behavior. Here, we report 16 individuals with a neurodevelopmental disorder and de novo heterozygous variants in ELAVL2, encoding an RBP not previously linked to Mendelian disease. Thirteen individuals were identified through GeneMatcher. Their ELAVL2 variants include two structural, five nonsense, and six missense variants, supporting haploinsufficiency as the primary disease mechanism. The cohort presented with developmental delay, intellectual disability, autism spectrum disorder, seizures, sleep problems, sensory processing issues, emotional instability, and difficulty with socialization. Three additional variants (two missense and one terminal exon truncation), each previously reported in a different large cohort study, were also included for follow-up investigations. We provide multiple lines of evidence linking variants in ELAVL2 to the observed neurodevelopmental and behavioral phenotypes. First, we show that common genetic variants in ELAVL2 are significantly associated with intelligence, motor development, sleep-related traits, and sociability in the general population. Drosophila loss-of-function models provide further independent evidence for a conserved role in the regulation of seizure-like behavior, sensory processing, and sleep. Molecular studies confirm that some of the missense variants are deleterious, leading to decreased protein levels. Together, our integrative study combining Mendelian genetics, clinical and association studies, and animal and molecular modeling supports variants in ELAVL2 as a cause of a neurodevelopmental disorder, with haploinsufficiency as the disease mechanism, and identifies crucial roles of ELAVL2 in neuronal function, cognition, and behavior.

The capacity of cells to proliferate and survive is central to development and disease. Assays that measure cell fitness are therefore a cornerstone of biology, but traditional techniques lack donor diversity and have high technical variability that impedes scale and reproducibility. To overcome these barriers, we designed and validated a "cell village"-based fitness screening approach using pooled cultures of 12-39 genetically distinct human neural progenitor cell (NPC) lines. We also developed Townlet to establish a foundational statistical framework based on Dirichlet regression for analyzing proportional data from cell villages. Applying these systems, we identified hyperproliferation in NPCs harboring the autism risk factor chromosome 16p11.2 deletion, mapped common genetic variants near ZFHX3 associated with NPC proliferation rate, and discovered genetic modifiers of lead (Pb) sensitivity implicating ARNT2. Together, these experimental and analytical tools advance a scalable, genetically diverse in vitro platform for dissecting human variation in cell fitness and gene-environment interactions.

The discovery of trans-acting expression quantitative trait loci (trans-eQTLs) remains plagued by false positives. One unaddressed source of these false positives is polymorphic duplications absent in the reference genome. Specifically, RNA sequencing (RNA-seq) reads from a non-reference gene duplicate have the potential to erroneously map to the single reference copy of the gene. These mismapped RNA-seq reads could generate spurious trans-eQTLs that localize near the insertion site of the non-reference duplicate but appear to regulate the reference copy of the gene. In such scenarios, similarly mismapped DNA-seq reads would create apparent long-range linkage disequilibrium between the spurious trans-eQTL and its target gene's locus. We call this footprint "mismapping linkage disequilibrium" and use it to flag spurious trans-eQTLs. We applied this strategy to published trans-eQTLs from Genotype-Tissue Expression (GTEx) and newly generated trans-eQTLs from MAGE, both already filtered for calls attributable to cross-mappability of RNA-seq reads between reference duplicates. This strategy demonstrated that at least 15.4% and 6.1% of trans-eQTLs in GTEx and MAGE, respectively, are spurious due to non-reference polymorphic duplications. We find that spurious trans-eQTLs are more likely to have large effect sizes. Thus, we expect large-sample studies such as eQTLGen (n > 30,000), which are powered to detect many more small-effect trans-eQTLs, to harbor smaller proportions of spurious trans-eQTLs than smaller studies such as GTEx (n = 838) and MAGE (n = 731). The polymorphic duplication of DUSP22 demonstrates how mismapping linkage disequilibrium can uncover both false-positive trans-eQTLs and trans-acting splicing QTLs (trans-sQTLs). Overall, our study offers a practical metric to improve trans-eQTL filtering.

Pathogenic rewiring of the three-dimensional (3D) genome architecture is increasingly being identified as the cause of genetic diseases, but recognizing the cis-regulatory effects of structural variation remains a challenge. The Xq27.1 region contains a quasi-palindrome identified as a pleiotropic hotspot for disease-causing interchromosomal insertions. In a large Danish family affected by X-linked recessive complex spastic paraplegia, we identified the segregation of a 149-kb interchromosomal insertion at Xq27.1 originating from 4q24. To understand the disease mechanism, we generated induced pluripotent stem cells (iPSCs) from affected individuals. Using CRISPR perturbation and neural differentiation experiments combined with high-throughput chromatin conformation capture (Hi-C) and transcriptomic analyses, we identify a 3D regulatory rewiring of SOX3 and transcriptional dysregulation of SOX3 targets in iPSC-derived neurons. Consistent with regulatory partitioning of the SOX3 topologically associating domain (TAD) in affected individuals, our experiments show that upstream cis-regulatory elements have a reduced ability to activate SOX3 expression and that the observed dysregulation depends on CTCF-binding sites within the insertion. This work provides mechanistic evidence that a position effect at the SOX3 locus can cause hereditary spastic paraplegia.

Understanding how selection shapes disease risk remains challenging. Variants influencing complex traits, including common diseases, can also impact fitness and thus be constrained by purifying selection. Consequently, genetic variance underlying disease susceptibility may be attributed to low-frequency, population-specific variants. We analyzed 509,817 genome-wide variants from 72,635 Han Taiwanese individuals to identify loci showing age-dependent allele frequency shifts that signal ongoing selection. After adjusting for potential age-related population structure, we detected 168 variants deviating from neutrality, with most showing declining frequencies in younger generations, consistent with purifying selection on deleterious alleles influencing disease risk. These variants were enriched for rare alleles (≤0.1%) and disease-associated variants. At BRCA1, we identified 16 rare pathogenic variants in strong linkage disequilibrium undergoing purifying selection that coexist with a positively selected haplotype, revealing temporally fluctuating selection; comparable patterns at BRCA2 and MLH1 suggest recurrent selective trade-offs in DNA repair genes. Phenome-wide association analysis across 30 hematologic and cardiometabolic traits linked a subset of candidates to increased erythrocyte volume and reduced hemoglobin concentration, suggesting subclinical physiological effects. These results demonstrate ongoing natural selection on disease-relevant variation, particularly affecting hematologic traits in the Han Taiwanese population, and highlight opportunities to refine precision-medicine risk models.

DNA methylation (DNAm) episignatures are stable disorder-specific epigenetic patterns that serve as valuable biomarkers for assessing variant pathogenicity and phenotypic outcomes in neurodevelopmental disorders (NDDs). However, episignatures derived from whole blood are inherently tissue- and cell-type specific, limiting their applicability in prenatal diagnostics. To explore the feasibility of developing episignatures capable of informing variant pathogenicity across tissues and developmental stages, we conducted a proof-of-concept study using Down syndrome, a common NDD caused by trisomy 21 (T21). We generated a blood-derived T21 episignature using a large cohort of 266 samples. Next, we used that episignature and publicly available DNAm data for 850 T21 and control samples across six different pre- and postnatal tissues to train machine-learning models, thereby enabling accurate prediction of T21 status across all tested tissues. Notably, our results show that models trained on postnatal blood-derived signatures as well as other tissues can generate cell-type-agnostic disease-specific patterns. This method also supported integrating well-characterized postnatal episignatures with a limited set of prenatal samples, which generated an episignature capable of accurately classifying prenatal samples. This approach forges a path for cross-tissue DNAm biomarker development and lays the groundwork for a workflow to rapidly integrate episignatures into prenatal diagnostics.

Whether polygenic risk, monogenic familial hypercholesterolemia (FH), and family history (FamHx) are additively informative for coronary heart disease (CHD) risk prediction across self-identified race/ethnicity (SIRE) groups has not been established. In two diverse cohorts-Electronic Medical Records and Genomics (eMERGE) phase IV (eIV; n = 19,348) and All of Us (AoU; n = 239,645)-we quantified the associations of a polygenic risk score (PRSCHD), pathogenic/likely pathogenic variants in genes associated with FH, and FamHx with CHD and evaluated their incremental value when added to the pooled cohort equations (PCEs). CHD was defined as myocardial infarction, unstable angina, or coronary revascularization. We modeled associations with multivariable logistic regression (prevalent CHD in eIV) and Cox proportional hazards (incident CHD in AoU) and characterized predictive performance with the c-statistic and reclassification and decision-curve net benefits across actionable 10-year risk thresholds. The effects of PRSCHD and FamHx were independent and additive in both cohorts and consistent across White, Black, and Latino SIRE groups. In eIV, adding PRSCHD and FamHx to the PCE increased the c-statistic for prevalent CHD from 0.719 to 0.753 (p-diff = 9.1 × 10[-3]) and reclassified 18.8% of participants at the 7.5% 10-year threshold, yielding approximately 4 additional true-positive CHD identifications per 1,000 screened. Net benefit gains were observed between the 7.5% and 10% thresholds across all three SIRE groups. In conclusion, PRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the United States (US), motivating the addition of these factors to clinical risk algorithms.

Early postzygotic mutations (PZMs) that arise after fertilization but prior to primordial germ cell specification may be present in both somatic and germ cells, causing mosaicism in a parent and constitutive inheritance in their offspring. In clinical family-trio whole-genome sequencing (WGS), such variants are systematically missed because their sub-heterozygous variant allele fraction (VAF) prevents heterozygous calling in the parent, while residual parental allele support disqualifies the variant as a candidate germline de novo mutation (DNM) in the child. Here, we developed a bioinformatic approach to ascertain parental PZMs from unfiltered DNM candidates in standard-depth (∼30×) trio WGS and applied it to 12,015 trios from the Genomics England 100,000 Genomes Project. We identified 1,015 high-confidence early autosomal parental PZMs, a large single-source catalog of this mutation class. These exhibited a monomodal VAF distribution centered around 5% in parental blood, consistent with empirically characterized ascertainment boundaries imposed by standard-depth sequencing and germline variant calling. PZMs showed no parental age or sex bias and displayed a mutational spectrum distinct from that of DNMs, with enrichment for C>A and T>A substitutions and depletion of T>C. Mutational signature analysis revealed that both mutation types are shaped by clock-like signatures SBS1 and SBS5 in similar proportions, suggesting that spectral differences reflect shifts within shared mutagenic processes. Exploratory genomic distribution analysis revealed a negative PZM association with GC content, in contrast to the positive association for DNMs. Among these, we found variants in DYNC1H1 and WT1 with potential clinical relevance that were missed by routine diagnostic pipelines.

Multiplexed assays of variant effects (MAVEs) represent a powerful approach to providing functional information for variants at scale. To harness the full utility of these systems, assay readout must be translated into a language that is accommodating to the clinical genomics community. We previously performed a MAVE to characterize variants in JAG1, the primary cause of the autosomal-dominant, multisystemic disease Alagille syndrome. Using this existing dataset, we calculated log-likelihood ratios of pathogenicity for each variant score, allowing for direct translation of variant data into recognized evidence weights utilized by the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) in clinical variant classification. Calibration resulted in improved separation of known benign and pathogenic variants and increased the classification rate of abnormal missense variants from 486 to 610, providing clear binning for strong (n = 1), moderate (n = 340), and supporting (n = 269) evidence toward pathogenicity. Retrospective application of this evidence to a cohort of 29 individuals with a JAG1 variant of uncertain significance (VUS) identified from clinical diagnostic sequencing yielded nine (31%) variants with abnormal data meeting supporting (n = 3) and moderate (n = 6) weight for pathogenicity, of which six (21%) were upgraded to likely pathogenic or pathogenic. Calibration of MAVE data to comply with the ACMG/AMP variant classification framework improves the diagnostic yield for JAG1 variants for Alagille syndrome. Moreover, our results support the broader application of these models to additional MAVEs, suggesting that they are likely to strongly impact variant classification across genes associated with disease.

Cohesin orchestrates gene expression via three-dimensional chromosome folding. Genes encoding cohesin and cohesin loaders have been associated with Mendelian disorders, whereas genes encoding cohesin release factors, including WAPL and its binding partners PDS5A and PDS5B, have not. We explored the relevance of cohesin release factors in Mendelian disease by phenotyping individuals with heterozygous predicted damaging variants in WAPL (n = 27), PDS5A (n = 8), and PDS5B (n = 8), by modeling WAPL deficiency in human cells and mice, and by aggregating disease association statistics from consortia studies. We identified a WAPL-related disorder featuring developmental delay, intellectual disability, and risk of other developmental anomalies. Similarities between individuals with damaging WAPL variants and those with large, recurrent 10q22.3q23.2 (10q) deletions encompassing WAPL nominate WAPL as a driver gene within this genomic disorder region. While individuals with PDS5A or PDS5B variants exhibited features of developmental disorders, neither cohort-based statistics nor subject phenotyping associated these genes with specific phenotypes. We used CRISPR to generate truncating variants in WAPL and 10q deletion or duplication in human induced pluripotent stem cells (iPSCs) and induced neurons. Transcriptomics identified significant overlap between WAPL haploinsufficiency and 10q deletion differentially expressed genes. Mice with 50% Wapl expression exhibited mild deficits of growth and learning/memory, whereas those with 25% residual Wapl displayed birth defects and postnatal lethality, revealing a dosage liability threshold below the level of heterozygosity. In summary, we delineated a genetic condition caused by cohesin release factor deficiency, nominated WAPL as a driver gene within a genomic disorder region, and further illuminated dosage sensitivity of human cohesin.