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1. ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden

   https://doi.org/10.1186/s13073-023-01199-y  

   Sharo, Andrew G.; Zou, Yangyun; Adhikari, Aashish N.; Brenner, Steven E. (July 2023, Genome Medicine)

   Background Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines. Methods Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD. Results While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar’s lower false-positive rate. Conclusions Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement. 

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2. Genome-wide association study of pancreatic fat: The Multiethnic Cohort Adiposity Phenotype Study

   https://doi.org/10.1371/journal.pone.0249615  

   Streicher, Samantha A.; Lim, Unhee; Park, S. Lani; Li, Yuqing; Sheng, Xin; Hom, Victor; Xia, Lucy; Pooler, Loreall; Shepherd, John; Loo, Lenora W.; et al (July 2021, PLOS ONE)

   Buchner, David A. (Ed.)

   Several studies have found associations between higher pancreatic fat content and adverse health outcomes, such as diabetes and the metabolic syndrome, but investigations into the genetic contributions to pancreatic fat are limited. This genome-wide association study, comprised of 804 participants with MRI-assessed pancreatic fat measurements, was conducted in the ethnically diverse Multiethnic Cohort-Adiposity Phenotype Study (MEC-APS). Two genetic variants reaching genome-wide significance, rs73449607 on chromosome 13q21.2 (Beta = -0.67, P = 4.50x10 -8 ) and rs7996760 on chromosome 6q14 (Beta = -0.90, P = 4.91x10 -8 ) were associated with percent pancreatic fat on the log scale. Rs73449607 was most common in the African American population (13%) and rs79967607 was most common in the European American population (6%). Rs73449607 was also associated with lower risk of type 2 diabetes (OR = 0.95, 95% CI = 0.89–1.00, P = 0.047) in the Population Architecture Genomics and Epidemiology (PAGE) Study and the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM), which included substantial numbers of non-European ancestry participants (53,102 cases and 193,679 controls). Rs73449607 is located in an intergenic region between GSX1 and PLUTO , and rs79967607 is in intron 1 of EPM2A . PLUTO , a lncRNA , regulates transcription of an adjacent gene, PDX1 , that controls beta-cell function in the mature pancreas, and EPM2A encodes the protein laforin, which plays a critical role in regulating glycogen production. If validated, these variants may suggest a genetic component for pancreatic fat and a common etiologic link between pancreatic fat and type 2 diabetes. 

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3. A large-scale multi-ancestry mitochondrial variant association analysis for cardiometabolic traits

   https://doi.org/10.1038/s41467-026-74939-4  

   Zhou, Jin J; Jensen, Aubrey; Samuels, David C; Markianos, Kyriacos; Zhou, Hua; Zhang, Hinn; Vujkovic, Marijana; Lynch, Julie A; Dinatale, Tia; Joseph, Jacob; et al (July 2026, Nature Communications)

   Abstract Studies linking mitochondrial DNA (mtDNA) with complex traits are often limited by small sample sizes or focused on specific phenotypes in clinically selected cohorts. Here, we use data from >600,000 participants in the Million Veteran Program (MVP) to perform a multi-ancestry analysis of mitochondrial DNA (mtDNA) variation and cardiometabolic phenotypes across European (EUR), African (AFR), Admixed American (AMR), and East Asian (EAS) populations. After validating 248 mtDNA loci, we identify 10 ancestry-stratified single-variant associations (8 EUR, 2 AFR) and 23 additional signals in sex- and type 2 diabetes (T2D)–stratified analyses. Four variants tagging haplogroup J, D-loop MT228G > A,MT-ND3MT10398A > G (p.Thr114Ala),MT-ND5MT13708G > A (p.Ala458Thr), andMT-CYBMT14798T > C (p.Phe18Leu), are associated with hypothyroidism in EUR and replicated in UK Biobank (UKBB) with concordant effects. In EUR females,MT-RNR1MT1555A > G increases the risk of carditis and heart failure phenotypes, supporting prior reports of maternally inherited cardiomyopathy. Gene-based rare-variant tests (minor allele frequency ≤2%) yield 26 associations (12 EUR, 10 AFR, 2 AMR, 2 EAS), including mitochondrial tRNA burdens linked to primary cardiomyopathy (females) and exophthalmos (males). Twenty-three of the 33 single-variant signals map to the endocrine/metabolic category, indicating significant enrichment (Fisher’s exactP = 0.003). These results define ancestry- and context-specific contributions of mtDNA to cardiometabolic disease, with a notable concentration in endocrine traits, and provide a framework for mtDNA analysis across diverse biobank cohorts. 

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4. Insights from the Biorepository and Integrative Genomics pediatric resource

   https://doi.org/10.1038/s41467-025-59375-0  

   Buonaiuto, Silvia; Marsico, Franco; Mohammed, Akram; Chinthala, Lokesh K; Amos-Abanyie, Ernestine K; Baras, Aris; Abecasis, Goncalo; Ferrando, Adolfo; Coppola, Giovanni; Deubler, Andrew; et al (December 2025, Nature Communications)

   Abstract The Biorepository and Integrative Genomics (BIG) Initiative in Tennessee has developed a pioneering resource to address gaps in genomic research by linking genomic, phenotypic, and environmental data from a diverse Mid-South population, including underrepresented groups. We analyzed 13,152 exomes from BIG and found significant genetic diversity, with 50% of participants inferred to have non-European or several types of admixed ancestry. Ancestry within the BIG cohort is stratified, with distinct geographic and demographic patterns, as African ancestry is more common in urban areas, while European ancestry is more common in suburban regions. We observe ancestry-specific rates of novel genetic variants, which are enriched for functional or clinical relevance. Disease prevalence analysis linked ancestry and environmental factors, showing higher odds ratios for asthma and obesity in minority groups, particularly in the urban area. Finally, we observe discrepancies between self-reported race and genetic ancestry, with related individuals self-identifying in differing racial categories. These findings underscore the limitations of race as a biomedical variable. BIG has proven to be an effective model for community-centered precision medicine. We integrated genomics education, and fostered great trust among the contributing communities. Future goals include cohort expansion, and enhanced genomic analysis, to ensure equitable healthcare outcomes. 

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5. Leveraging base-pair mammalian constraint to understand genetic variation and human disease

   https://doi.org/10.1126/science.abn2937  

   Sullivan, Patrick F.; Meadows, Jennifer R.; Gazal, Steven; Phan, BaDoi N.; Li, Xue; Genereux, Diane P.; Dong, Michael X.; Bianchi, Matteo; Andrews, Gregory; Sakthikumar, Sharadha; et al (April 2023, Science)

   INTRODUCTION Thousands of genetic variants have been associated with human diseases and traits through genome-wide association studies (GWASs). Translating these discoveries into improved therapeutics requires discerning which variants among hundreds of candidates are causally related to disease risk. To date, only a handful of causal variants have been confirmed. Here, we leverage 100 million years of mammalian evolution to address this major challenge. RATIONALE We compared genomes from hundreds of mammals and identified bases with unusually few variants (evolutionarily constrained). Constraint is a measure of functional importance that is agnostic to cell type or developmental stage. It can be applied to investigate any heritable disease or trait and is complementary to resources using cell type– and time point–specific functional assays like Encyclopedia of DNA Elements (ENCODE) and Genotype-Tissue Expression (GTEx). RESULTS Using constraint calculated across placental mammals, 3.3% of bases in the human genome are significantly constrained, including 57.6% of coding bases. Most constrained bases (80.7%) are noncoding. Common variants (allele frequency ≥ 5%) and low-frequency variants (0.5% ≤ allele frequency < 5%) are depleted for constrained bases (1.85 versus 3.26% expected by chance, P < 2.2 × 10 −308 ). Pathogenic ClinVar variants are more constrained than benign variants ( P < 2.2 × 10 −16 ). The most constrained common variants are more enriched for disease single-nucleotide polymorphism (SNP)–heritability in 63 independent GWASs. The enrichment of SNP-heritability in constrained regions is greater (7.8-fold) than previously reported in mammals and is even higher in primates (11.1-fold). It exceeds the enrichment of SNP-heritability in nonsynonymous coding variants (7.2-fold) and fine-mapped expression quantitative trait loci (eQTL)–SNPs (4.8-fold). The enrichment peaks near constrained bases, with a log-linear decrease of SNP-heritability enrichment as a function of the distance to a constrained base. Zoonomia constraint scores improve functionally informed fine-mapping. Variants at sites constrained in mammals and primates have greater posterior inclusion probabilities and higher per-SNP contributions. In addition, using both constraint and functional annotations improves polygenic risk score accuracy across a range of traits. Finally, incorporating constraint information into the analysis of noncoding somatic variants in medulloblastomas identifies new candidate driver genes. CONCLUSION Genome-wide measures of evolutionary constraint can help discern which variants are functionally important. This information may accelerate the translation of genomic discoveries into the biological, clinical, and therapeutic knowledge that is required to understand and treat human disease. Using evolutionary constraint in genomic studies of human diseases. ( A ) Constraint was calculated across 240 mammal species, including 43 primates (teal line). ( B ) Pathogenic ClinVar variants ( N = 73,885) are more constrained across mammals than benign variants ( N = 231,642; P < 2.2 × 10 −16 ). ( C ) More-constrained bases are more enriched for trait-associated variants (63 GWASs). ( D ) Enrichment of heritability is higher in constrained regions than in functional annotations (left), even in a joint model with 106 annotations (right). ( E ) Fine-mapping (PolyFun) using a model that includes constraint scores identifies an experimentally validated association at rs1421085. Error bars represent 95% confidence intervals. BMI, body mass index; LF, low frequency; PIP, posterior inclusion probability. 

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