Type 2 diabetes (T2D) is a highly heritable, polygenic disease with over 600 loci identified through genome-wide association studies (GWAS). However, despite possessing unique genetic variation shaped by demographic history and admixture, Latin American populations remain markedly underrepresented in global genomic research. To address this gap, we conducted genome- and exome-wide analyses of 19,431 T2D cases and 105,611 controls from the Mexico City Prospective Study (MCPS). We identified 86 independent GWAS associations, including 21 novel signals, 15 of which replicated in external cohorts. Risk alleles at novel loci were enriched in individuals with Indigenous American ancestry. Exome analyses revealed rare and ultra-rare missense variants with substantial risk effects at HNF1A and GCK , as well as a protein-damaging variant in SLC30A8 that reduced T2D risk by 45% in carriers. Integrative analyses indicate that T2D genetic architecture in Mexico is predominantly driven by common regulatory variation acting in the endocrine pancreas. Polygenic risk scores strongly stratified T2D risk and transferred to Indigenous Mexican populations. These findings demonstrate the power of large-scale genetic discovery in diverse populations to refine disease architecture and identify loci with potential therapeutic relevance.

Both short and long sleep duration have been associated with poor glycemic control and an increased risk of developing type 2 diabetes mellitus. Although sleep duration may differentially modify the effects of genetic risk factors for type 2 diabetes, this has not been systematically investigated. In the present study, we conducted genome-wide gene by sleep duration meta-analyses, separately assessing interactions of short and long sleep, for fasting glucose, fasting insulin, and hemoglobin A1c in up to 489,309 individuals without diabetes from seven different population groups. In total, 16 loci were identified to interact with sleep duration - six with short sleep and ten with long sleep. Of these, four loci were identified through cross-population meta-analysis. Mapped genes exhibit pathway connections to pericyte apoptosis, NMDA receptor activity, the GLUT1 receptor, neurological health, and sleep architecture. Eleven loci (VRK2, PCDH7, TFAP2A, CAP2, PAPPA, ZCCHC2, MYH9, SGIP1, JAKMIP3, RRAS2, MAPT) have not been reported in previous glycemic trait genome-wide association studies. Interaction loci identify divergent biological mechanisms for short and long sleep duration influencing glycemic control, suggesting specific pathways of intervention for precision medicine approaches to diabetes prevention and management.

Histopathological assessment has served as the gold standard for diagnosing Alzheimer's disease (AD). Emerging technological advancements, including the development of amyloid positron emission tomography (PET), have enabled early detection of amyloid pathology, one of the neuropathological hallmarks of AD. Genome-wide association study (GWAS) across cohorts of aging and AD, leveraging different measurements of amyloid burden, may facilitate the identification of novel genetic variants that drive the earliest neuropathological changes in AD. This study presents the largest GWAS of brain amyloidosis to date, leveraging amyloid β (Aβ) measured by in vivo amyloid PET and postmortem histopathology from 13,555 individuals of European ancestry. Amyloid positivity was defined as moderate or frequent neuritic plaques according to the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) staging scores for each postmortem cohort. A Gaussian mixture model (GMM) was applied to each amyloid PET cohort to identify the cohort and tracer-specific cut-offs that differentiate amyloid positive and negative populations. In silico and ex vivo analyses further characterized implicated loci, including interrogating the association between bulk and single-nucleus gene expression profiles and AD-related traits. Genetic covariance analysis assessed the extent amyloid PET and postmortem measures reflect the shared genetic architecture of brain amyloidosis. Our combined amyloidosis GWAS identified three established AD risk loci: BIN1 (rs6733839, OR = 1.20, 95% CI 1.14-1.26, P = 1.32 × 10-11), CR1 (rs4844610, OR = 1.24, 95% CI = 1.16-1.32, P = 4.21 × 10-10), APOE (rs429358, OR = 4.01, 95% CI = 3.66-4.38, P = 4.54 × 10-201), and a newly identified brain amyloidosis-associated variant on chromosome 17 (rs35635959, OR = 1.18, 95% CI = 1.12-1.25, P = 1.47 × 10-8). SuSiE fine-mapping identified a single credible set of 15 putative causal variants with rs35635959 as the lead variant. Subsequent eQTL and SuSiE-based colocalization analyses prioritized rs35635959 as a strong eQTL for TUBG2, encoding tubulin gamma 2, which is involved in microtubule organization and synaptic plasticity. Further cell-type-specific characterization of this gene in neurons from dorsolateral prefrontal cortex tissue indicated that decreased TUBG2 expression was associated with increased Aβ burden and AD case status (PFDR < 0.045). Furthermore, our study is the first to report a modest genetic covariance (covariance=0.17, P < 6.54 × 10-8) between the genetic architecture of amyloid burden captured by different modalities. While APOE showed a strong association with both amyloid endophenotypes, the observed genetic covariance was not substantially attenuated after excluding variants within the APOE region (covariance=0.16, P < 1.32 × 10-7). Our results highlight the benefits of leveraging compatible, harmonized AD endophenotypes to increase power to uncover new molecular insights into the etiology of AD neuropathology. Wang et al. present the largest GWAS of brain amyloidosis to date, analysing 13,555 individuals using amyloid PET and postmortem Aβ measures. They identify a novel amyloidosis-associated variant on chromosome 17, demonstrate genetic covariance between modalities, and highlight complex traits sharing genetic architecture with Aβ burden.

Hypophosphatasia (HPP) is a heritable multisystem disorder caused by pathogenic variants in the tissue nonspecific alkaline phosphatase (ALP)-coding gene ALPL. The genotype–phenotype correlation in heterozygous adults with HPP remains incompletely understood. In this genotype-based study, we aimed to measure the prevalence of pathogenic or likely-pathogenic ALPL variants and to test the hypothesis that HPP penetrance is low in adult carriers. A total of 37 147 genomes from unselected individuals visiting a tertiary care, academic medical center were investigated. Variants classified as pathogenic or likely-pathogenic were observed with a prevalence of 0.3% (n = 109) or 1/341. Variant c.571G>A was most frequent (67.9%). A subset of 70 individuals had linked electronic health records (EHRs) and were termed ALPL+. All 70 ALPL+ individuals showed mild, mainly neurological, symptoms often reported in adults with HPP. However, low serum ALP, a hallmark of HPP, was found in only 65.7% (38/70) of ALPL+ individuals, and 12.9% (9/70) met the diagnostic criteria for HPP based on consensus guidelines, thus complete penetrance was low. Compared to controls lacking pathogenic or likely-pathogenic variants (ALPL−), the ALPL+ individuals had a higher probability of progression for mobility issues (median age 73 yr ALPL+ vs 82 yr ALPL−, p = .03), as well as a similar probability of progression for fatigue, arthritis, or dental problems. Unexpectedly, 3.4% (5/148) of individuals in the ALPL- group met the diagnostic criteria for HPP, possibly due to unidentified variants or non-ALPL genetic factors. Overall, the data support our hypothesis and aids the management of carriers of pathogenic ALPL variants.

Prosody perception is an often overlooked aspect of human language despite its importance in facilitating spoken language comprehension. Sensitivity to prosodic cues varies between individuals, and prosody perception skills are shown to be associated with various language- and reading-related outcomes. Despite the importance of prosody perception in human communication, its underlying biology is poorly understood. This study investigates the genetic architecture of prosody (speech rhythm) perception and explores its evolutionary roots. We conducted a GWAS of prosody (n = 1,501) as measured by scores on the Test of Prosody via Syllable Emphasis ("TOPsy"). GWAS results yielded 14 suggestive significant signals (p < 5.00 × 10-6). Gene set enrichment analysis identified shared genetic architecture between human prosody perception and key vocal learning brain regions in songbirds, suggesting that human prosody perception may have evolutionary convergence in communication mechanisms in animal vocal learning. Additionally, cross-trait polygenic score analyses suggest shared genetic influences between prosody perception and both word reading and musical beat synchronization, emphasizing how genetics influence prosody perception and its associations with communication-, education-, and music-related traits. These initial efforts could inform advances in communication sciences and disorders as well as educational contexts.

There is a need for genetic analytical methods that integrate multi-individual identity-by-descent (IBD) tools with phenotypic enrichment testing to discover novel shared haplotypes contributing to disease traits. Existing tools are designed to identify IBD sharing and leave interpretation and phenotype association tests to further analyses. Here we present Distant Relatedness for Identification and Variant Evaluation (DRIVE) v3, a python command-line interface tool that identifies networks of participants who share an identical haplotype at a given genomic location. Given phenotypic data, DRIVE additionally estimates significant enrichment of dichotomous traits within networks. DRIVE is designed for efficient use across large-scale genetic data resources, featuring a versatile application programming interface and a backend structure designed for flexible integration into existing analytical pipelines. In this work, we describe the implementation of DRIVE v3 and illustrate two applications of the tool to an autosomal dominant condition and to an autosomal recessive condition, cardiomyopathy and cystic fibrosis, respectively. These applications highlight the substantial performance improvements between v1 and v3 and demonstrate practically how the newer features of DRIVE such as the enrichment test can be used in the interpretation of the identified networks.

While lipids have been extensively investigated, genetic regulation of the circulating lipidome in diverse populations remains poorly understood. We conducted a lipidome-wide GWAS of 830 lipid species in 2,287 Hispanic/Latino participants and performed predictive modeling across omics layers. We identified 7,593 genome-wide significant SNPs mapping to 208 genes. Conditional analysis disentangled the long-range linkage disequilibrium artifacts from the pleiotropic FADS1/2/3 cluster. Separately, we discovered an association at the GPLD1 locus for a circulating ceramide. Colocalization revealed shared genetic architecture with conventional lipids alongside distinct, species-specific pathways. Incorporating Native/Indigenous American eQTLs within a multi-omic framework uncovered 62 likely regulatory genes missed by European-centric gene expression models. Finally, genetically regulated predictive models demonstrated performance declining from transcriptomics to proteomics to lipidomics, reflecting increased distance from gene action along the molecular cascade. Our study provides a genetic landscape of lipid metabolism in a highly burdened population and highlights the challenges in predicting lipid abundance.

CONTEXT:Efforts to characterize the shared molecular risk factors that contribute to obesity and the downstream disease sequelae it triggers have been limited. OBJECTIVE:We aimed to identify functional genes with evidence for both causal and consequential effects on obesity-related traits and their downstream sequalae using integrated genomic and proteomic data. METHODS:We investigated the association of obesity-related traits with 2912 plasma proteins in 259 individuals from the Cameron County Hispanic Cohort with validation of results in ∼45 000 participants from the UK Biobank. Through colocalization and Mendelian randomization, we assessed evidence for the shared underpinning and the causal direction of significant proteins with respect to obesity and obesity-associated illnesses. We used gene ontology and cell- and tissue-specific protein and transcriptional activity patterns of the genes encoding target proteins to illuminate the functional relevance of implicated pathways. We additionally investigated the suitability of target proteins as potential therapeutic targets for drug development. RESULTS:Of the 122 significantly associated proteins with obesity metrics at a false discovery adjusted level (false discovery rate < 0.05), 121 replicated in UKBB. Most function in adipogenesis, inflammation, glucose metabolism, and neural and appetite regulation. Eighty of 121 replicated proteins showed evidence of statistical causality for obesity or obesity-associated illnesses. Those causally linked showed elevated transcript abundance in adipose and brain tissues and adipocytes. The promising weight reduction potential of several target proteins highlights their suitability for future pharmaceutical repurposing. CONCLUSION:Our analyses revealed key regulatory mechanisms influenced by and influencing obesity, offering valuable targets for biomarkers and clinical interventions.

BACKGROUND:Polygenic risk scores (PRSs) improve prediction of the development of type 2 diabetes over the use of clinical risk factors alone; however, they perform poorly in populations of non-European ancestry, limiting their global clinical utility. We aimed to deliver comprehensive and rigorously tested multi-ancestry PRSs for prediction in type 2 diabetes. METHODS:We conducted meta-analyses using data from type 2 diabetes genome-wide association studies (GWAS) across cohorts from five major global ancestries: European, African or African American, Admixed American, South Asian, and East Asian. We used summary statistics from the GWAS to construct single-ancestry PRSs (using the continuous-shrinkage PRS-CS method) and multi-ancestry PRSs (using the PRS-CSx method), and constructed ancestry-specific linkage disequilibrium panels to model pairwise correlations between single-nucleotide polymorphisms in GWAS during PRS construction. Models were validated for association with type 2 diabetes in at least four independent cohorts per ancestry. The effect sizes of PRSs were estimated as the odds ratio (OR) per SD of the PRS, and ORs for individuals at the 90th, 95th, and 97·5th PRS percentiles were compared with the IQR as a reference. We also tested our PRS models for prediction of diabetes incidence with or without additional clinical factors, as well as microvascular complications and comorbidities. FINDINGS:Our analysis used data from 409 959 individuals with type 2 diabetes and 1 983 345 controls: respectively, 359 819 and 1 825 729 indivduals were included in the GWAS dataset, with 10 992 and 31 792 individuals in the training dataset and 39 148 and 125 824 individuals in the validation dataset. The best predictive performance for the single-ancestry PRSs was in European (incremental AUC 0·07-0·14) and East Asian (0·02-0·16) ancestries, whereas prediction was poorer for African or African American (0·02-0·03), Admixed American (0·02-0·04), and South Asian (0·02-0·04) ancestries, correlating with sample sizes in the GWAS. Compared with single-ancestry PRSs, our multi-ancestry PRSs showed higher effect sizes and smaller 95% CIs across all ancestries: OR per SD 1·73 (95% CI 1·67-1·80) in African or African American, 2·82 (2·67-2·97) in Admixed American, 2·45 (2·36-2·54) in East Asian, 2·36 (2·32-2·41) in European, and 2·23 (2·05-2·42) in South Asian ancestries. Individuals in the 97·5th PRS percentile had a 3-7 times increased risk of type 2 diabetes compared with those in the IQR (OR 3·43 [95% CI 2·80-4·21] in African or African American, 7·47 [5·64-9·89] in Admixed American, 6·62 [5·58-7·85] in East Asian, 6·25 [5·72-6·82] in European, and 4·50 [2·70-7·53] in South Asian ancestries). These PRSs were also associated with earlier onset of type 2 diabetes, higher risk of developing microvascular complications, and provide additional predictive value beyond clinical factors. In individuals with type 2 diabetes, the association between multi-ancestry PRSs and risk of microvascular complications and comorbidity was studied in populations of African, Admixed American, and European ancestries and was significant in all three ancestry groups for diabetic retinopathy (ORs per SD 1·28-1·57), diabetic nephropathy (1·25-1·58), proliferative diabetic retinopathy (1·39-2·08), and end-stage diabetic nephropathy (1·44-1·87); PRS was associated with coronary artery disease in the Admixed American ancestry group only (1·16 [95% CI 1·08-1·25]). INTERPRETATION:These validated, publicly available PRSs can improve risk stratification for type 2 diabetes onset and complications across diverse ancestries, supporting their further evaluation in clinical settings. FUNDING:The National Human Genome Research Institute of the US National Institutes of Health.

Abstract Speech and language are fundamental to the human experience, allowing for the sharing of thoughts and emotions through the coordination of many neurological and linguistic systems. Disruptions in these processes can lead to speech and language disorders, including stuttering, which is characterized by prolongations, blocks, and repetitions of speech sounds. To date, almost 60 genome-wide significant loci have been associated with stuttering. Alas, most of these signals appear in non-coding regions of the genome and thus remain largely uncharacterized. In this study, we probed functionality by leveraging the largest genome-wide association studies (GWAS) of self-reported stuttering in individuals with European genetic ancestry (N case = 78,394, N control = 865,956). We performed transcriptome-wide association studies (TWAS), tested causal effects via Mendelian randomization (MR), and assessed neuroimaging features associated with stuttering. Stuttering was associated with the genetically regulated gene expression (GReX) of 2,875 significant gene-tissue pairs (236 independent signals). Many of these GReX genes were enriched for neurological processes, including synapse organization and nervous system development, and 12 genes were independently supported in a clinically ascertained stuttering cohort. Our MR analysis identified 150 unique causal stuttering genes (53 distinct signals). Additionally, our neuroimaging analysis identified stuttering-associated genetic signals functionally linked with basal ganglia, cerebellum, and superior longitudinal fasciculus neuroimaging features. Together, these findings characterize transcriptomic signatures of stuttering and illuminate the neurological mechanisms driving this complex trait.

INTRODUCTION: Neuroimaging genetics has advanced our understanding of Alzheimer's disease (AD); however, frameworks using functional genomics are needed to elucidate mechanisms connecting loci to neurological outcomes. To address this need, we explored relationships between AD-associated variants and disease via their impact on gene expression and neuroanatomical phenotypes. METHODS: We mapped established AD genes to neuroimaging traits using the NeuroimaGene Atlas and predicted transcript-driven neurological features of AD by comparing gene-derived neuroimaging features with clinical neuroimaging data. Genetic covariance analyses were performed to characterize shared genetic architecture between AD endophenotypes and neuroimaging features, and to identify neuroimaging features associated with a family history of dementia. RESULTS: Our analyses implicate PSMC3 as a contributor to AD pathophysiology and identify AD endophenotypes, including dementia family history, linked to frontal cortex thickness and volume, as well as changes in cerebrospinal fluid volume. DISCUSSION: Our findings prioritize AD genes whose regulation is associated with vulnerable brain regions, offering a potential mechanistic framework for downstream functional validation.

INTRODUCTION:Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer's disease (AD), but genetic drivers are unclear. METHODS:In 2614 older adults (mean age ± SD: 73.7 ± 9.8 years; 26% cognitively impaired) from seven harmonized cohorts enriched for cognitive impairment, we quantified free-water-corrected diffusion MRI (dMRI) metrics in seven limbic tracts. We estimated single nucleotide polymorphism (SNP) heritability, performed cohort genome-wide association studies (GWASs) with meta-analysis, evaluated shared genetic architecture and enriched pathways, and assessed AD relevance using brain RNA-seq data. RESULTS:Limbic WM is heritable (h2 = 0.26-0.60; pFDR < 0.05). Meta-GWAS identified six loci (p < 5 × 10- 8), including a signal implicating CDH19, an oligodendrocyte-enriched cell-adhesion gene. Additional loci were near the KC6, SENP5, RORA, FAM107B, and MIR548A1 genes. In brain tissue, RORA, FAM107B, and KC6 expression was associated with cognition and AD neuropathology. Results converged on insulin and immune biology and shared genetic architecture with lipid and cardiovascular traits. DISCUSSION:Limbic WM microstructure is genetically influenced and links oligodendrocyte and vascular-inflammatory biology to AD-relevant outcomes.

Genetic variants that influence transcript abundance, called expression quantitative trait loci (eQTL), are fundamental to understanding gene regulation and disease etiology. However, eQTL studies have overlooked the influence of the ancestral origin of a gene on its regulation. We implemented a new statistical framework that maps local ancestry-specific regulatory variation, revealing pervasive ancestry-specific effects on gene expression in Hispanic/Latino and African American populations. Enriched in open chromatin regions, these variants better explain genetic disease risk in these populations. The widespread heterogeneity of local ancestry-based eQTL effects offers mechanistic explanations for inconsistencies in genomic and multi-omic studies across populations. Our findings expand existing models of gene regulation and the importance of applying local genomic context in genetic studies to advance precision medicine and address health disparities.

BACKGROUND:While GWAS (genome-wide association studies) have identified over 1000 obesity-associated loci, their functional impact on gene expression remains unclear. Moreover, many studies have not fully captured the genetic architecture of obesity in high-risk populations or considered the complexity of adiposity beyond traditional measures. To address these gaps, this study explores the genetic and transcriptomic pathways of obesity using diverse obesity phenotypes in a high-risk population. METHODS:We analyzed genomic and whole-blood transcriptomic data from the CCHC (Cameron County Hispanic Cohort), performing GWAS on 13 obesity-related traits. Differential expression analysis was conducted for genes near GWAS-identified single nucleotide polymorphisms (P<5×10-6) followed by expression quantitative trait loci mapping and GWAS-expression quantitative trait loci colocalization. RESULTS:GWAS identified 486 trait associations, including 6 genome-wide significant (P<5×10-8) loci, with 3 novel signals linked to abdominal subcutaneous adipose tissue, body fat percentage, and waist circumference. Among 3024 genes near these loci, 60 showed differential expression. Further expression quantitative trait loci analysis suggested 2 single nucleotide polymorphism-gene-trait relationships: rs543314376-MAPK11, associated with subcutaneous adipose tissue volume in females, and rs963018484-PER1, linked to body mass index in females. Both genes play key roles in obesity-related pathways, including inflammation and circadian rhythm regulation. CONCLUSIONS:This integrative genomic-transcriptomic analysis uncovers 2 novel candidate genes for obesity and underscores the critical need for involving all populations and comprehensive adiposity measures in obesity research. By expanding beyond body mass index in a Hispanic/Latino population, we move closer to a deeper and more inclusive understanding of obesity's genetic architecture.

This cross-sectional study estimates excess adiposity in Mexican-American individuals with and without obesity enrolled in the Cameron County Hispanic Cohort.

BACKGROUND:von Willebrand disease (VWD) is a common inherited bleeding disorder caused by low levels or activity of circulating von Willebrand factor (VWF). Genetic susceptibility to VWF antigen (VWF:Ag) below normal (≤ 50 IU/dL) in the general population is underexplored. OBJECTIVES:To identify genetic variants influencing VWF:Ag levels ≤ 50 IU/dL. METHODS:We performed a genome-wide association study in 926 cases with VWF:Ag levels ≤ 50 IU/dL and 12 846 controls from 7 studies from the Trans-Omics for Precision Medicine program. We then examined whether significant genome-wide findings were also associated with clinical diagnosis of VWD in 5 biobanks with 708 VWD cases and 1 286 069 controls, and with 6 bleeding and thrombotic disorders in FinnGen. RESULTS:Variants at 2 loci were associated (P < 5 × 10-9) with VWF:Ag levels ≤ 50 IU/dL: ABO and VWF. The VWF index variant, p.Tyr1584Cys, is a rare (0.22%) missense variant with odds ratio (OR) of 78.58, while the ABO index variant is a common intronic variant with a smaller effect (OR = 2.52). Notably, both VWF (OR = 7.16) and ABO (OR = 1.57) variants were also associated (P < .025) with diagnosed VWD. Among p.Tyr1584Cys heterozygotes, the penetrance of VWF:Ag levels ≤ 50 IU/dL was 24.2% and the penetrance of diagnosed VWD was 0.3%. p.Tyr1584Cys was associated (P < .0042) with increased odds of heavy menstrual bleeding (OR = 1.27), iron deficiency anemia (OR = 1.55), and intrapartum hemorrhage (OR = 2.20), but decreased odds of deep vein thrombosis (OR = 0.54). CONCLUSIONS:Although there are currently conflicting interpretations of pathogenicity p.Tyr1584Cys, our results suggest that it is a low penetrance pathogenic variant that contributes to VWF:Ag levels ≤ 50 IU/dL, bleeding, and VWD.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data from up to 5619 samples in the Framingham Heart Study (FHS). Using 3992 single-nucleotide polymorphisms (SNPs) at 97 BMI loci and 1408 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). Transcripts were prioritized if we identified transcripts that met Bonferroni-corrected significance within each omic, showed stronger associations in the correlated meta-analysis than each omic, and had corresponding SNPs in the SNP-transcript-BMI association that were at least nominally associated with BMI in FHS data. We tested for generalization of identified association in a Hispanic ancestry sample of blood gene expression data and other samples in hypothalamus, nucleus accumbens, liver, and visceral adipose tissue (VAT) with significant threshold: PMETA < 0.05 & PMETA < PSNP & PMETA < PBMI. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. We generalized results for SNAPC3 and YPEL3 in Hispanic ancestry sample, for YPEL3 in the nucleus accumbens, ZNF646 and GSTM3 in VAT, and NT5C2, SNAPC3, TMEM245, YPEL3, and ZNF646 in liver. The identified genes help link the genetic variation at obesity-risk loci to biological mechanisms and health outcomes, thus translating GWAS findings to function.

PURPOSE: Converging etiological evidence supports a genetic risk for developmental stuttering; however, major gaps detailing the genetic architecture remain. Technological advances in genetics have allowed us to explore novel approaches to analyzing this complex trait, but conducting robust and replicable genetic studies requires large, well-phenotyped cohorts of subjects. This article reviews previous research strategies employed to overcome these challenges in identifying genetic variants associated with stuttering and translating stuttering-associated variants into molecular and cellular mechanisms. METHOD: We present an overview of data sources and strategies research teams have utilized for the genetic study of stuttering, highlighting the advantages and limitations of each approach. Primary data sources include (a) the International Stuttering Project, (b) the National Longitudinal Study of Adolescent to Adult Health, (c) BioVU, and (d) 23andMe, Inc. In addition to genome-wide association studies (GWASs), we review multiple post-GWAS follow-up analyses to probe the functional impact of stuttering-associated genetic variants and offer new transcriptome-wide analyses. RESULTS: To date, a diverse array of approaches has resulted in the identification of over 50 stuttering-associated genes. Many genetic associations were near or within genes previously linked to known neurological traits, highlighting a neurological role in stuttering. Additionally, validation studies using polygenic risk scores suggested a high level of genetic concordance between our samples. Functional follow-up studies suggest stuttering-associated variants may affect gene expression in tissues relevant to speech-related structures and neural correlates. CONCLUSIONS: While understanding how specific regions of the genome contribute to stuttering risk remains complex, research from our team and others has utilized a variety of data sources in an attempt to overcome previous limitations in the identification of genetic variation associated with stuttering. As the field of genetics evolves toward large-scale biobanks for research and discovery, prioritizing inclusion of traits such as stuttering will be key. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.30299764.