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Type 2 diabetes mellitus (T2DM) risk is heavily influenced by genetics, yet current association tests have explained only parts of its heritability. We developed MEVA (Meta-Evolutionary Action), a meta-analytic framework that integrates three complementary methods—EAML, Sigma-Diff, and GeneEMBED—to assess the functional burden of protein-coding variants using evolutionary data. MEVA was applied to exome data from 28,115 T2DM cases and 28,115 controls in the UK Biobank (UKB), identifying 101 genes (p < 1e-5). MEVA outperformed its component methods, each of which substantially outperformed a conventional burden test (MAGMA), in recovering known T2DM genes (AUROC = 0.925) and maintaining robustness in progressively smaller cohorts (AUROC = 0.917). MEVA showed significant enrichment for T2DM-related loci (p = 6.8e-10, p = 2.0e-34), protein interactions (z = 4.6, z = 4.2), pathways (p = 1.3e-6, z = 2.0), phenotypes (p = 1.3e-21, z = 9.1), and literature mentions (z = 7.2). Replication in 16,915 T2DM cases and 16,915 controls from All of Us (AoU) yielded 99 genes (p < 1e-5), 23 of which were also recovered in the UKB cohort – far exceeding random chance. These included established genes ( SLC30A8, WFS1, HNF1A ) and less-characterized candidates ( NRIP1, ADAM30, CALCOCO2, TUBB1, ZFP36L2, WDR90 ). Notably, NRIP1 loss-of-function variants were associated with increased T2DM risk in both the UKB (OR = 1.09, FDR = 5.4e-4) and AoU (OR = 1.09, FDR = 0.046), and TUBB1 and CALCOCO2 gain-of-function variants showed consistent risk effects (FDR < 0.05). Pathway analyses revealed convergence on endoplasmic reticulum chaperone complexes (FDR = 0.02) and Hippo signaling (FDR = 8.5e-4). Finally, all 177 candidate genes were functionally prioritized using ten orthogonal criteria to guide experimental follow-up. These results demonstrate that combining complementary, impact-aware association tests increases sensitivity, improves replication, and expands the catalog of genetic risk factors for T2DM.

The incidence of Alzheimer’s Disease in females is almost double that of males. To search for sex-specific gene associations, we build a machine learning approach focused on functionally impactful coding variants. This method can detect differences between sequenced cases and controls in small cohorts. In the Alzheimer’s Disease Sequencing Project with mixed sexes, this approach identified genes enriched for immune response pathways. After sex-separation, genes become specifically enriched for stress-response pathways in male and cell-cycle pathways in female. These genes improve disease risk prediction in silico and modulate Drosophila neurodegeneration in vivo. Thus, a general approach for machine learning on functionally impactful variants can uncover sex-specific candidates towards diagnostic biomarkers and therapeutic targets. More females than males suffer from Alzheimer’s Disease for reasons not well understood. Here, using a novel machine learning approach focused on functionally impactful coding variants, the authors identify potential sex-specific modulators of neurodegeneration.

AD is a devastating condition that affects millions in the US. To advance gene discovery and therapeutic development for AD, the Alzheimer's Disease Sequencing Project (ADSP) was launched as a presidential initiative in 2012. Since its inception, ADSP has collected over 80,000 sequencing samples through the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). While investigating the genetic factors underlying AD using NIAGADS, we observed significant heterogeneity in the phenotypes of sub-cohorts, including sex, APOE genotypes, and age. We hypothesized that this heterogeneity could hinder robust AD risk discoveries and aimed to generate a common dataset. We utilized the latest NIAGADS WES (R2) and WGS (R4) datasets, comprising 56,000 AD and healthy subjects. After quality control including the genetic ancestry prediction, we analyzed sub-cohorts based on sex, age, age of onset, and APOE genotypes. Our pipelines were then applied to identify new AD candidate genes, which were subsequently validated through comprehensive computational and experimental methods to assess their performance. We observed significant differences in APOE and age distributions between AD and healthy subjects. However, we also identified highly diverse age and APOE profiles across sub-cohorts, leading us to pinpoint outlier sub-cohorts. Applying our pipelines to the diverse R2 and R4 NIAGADS datasets, we found that removing these outliers improved AD gene discovery by enhancing replication of known AD genes (p 1.2e-3 and 8.6e-5 for R2 and R4, respectively) and increasing connectivity to known AD genes in the STRING network (z 5.62 and 5.98, respectively). Further analyses of extremely young and old AD cases within the remaining cohorts also facilitated better gene discoveries. The component cohorts of NIAGADS R2-R4 exhibit significant heterogeneity, as demonstrated by the diverse APOE and age distributions. Our approaches showed that optimizing these diverse cohorts based on APOE and age distributions improves AD gene recovery. The heterogeneity of AD cohorts also provides new opportunities to study smaller sub-groups, such as age of onset and specific APOE profiles. These diverse NIAGADS cohorts could ultimately help define standard criteria for AD cases and control subjects, offering valuable insights for future research.

Background AD is a devastating condition that affects millions in the US. To advance gene discovery and therapeutic development for AD, the Alzheimer's Disease Sequencing Project (ADSP) was launched as a presidential initiative in 2012. Since its inception, ADSP has collected over 80,000 sequencing samples through the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS). While investigating the genetic factors underlying AD using NIAGADS, we observed significant heterogeneity in the phenotypes of sub‐cohorts, including sex, APOE genotypes, and age. Method We hypothesized that this heterogeneity could hinder robust AD risk discoveries and aimed to generate a common dataset. We utilized the latest NIAGADS WES (R2) and WGS (R4) datasets, comprising 56,000 AD and healthy subjects. After quality control including the genetic ancestry prediction, we analyzed sub‐cohorts based on sex, age, age of onset, and APOE genotypes. Our pipelines were then applied to identify new AD candidate genes, which were subsequently validated through comprehensive computational and experimental methods to assess their performance. Result We observed significant differences in APOE and age distributions between AD and healthy subjects. However, we also identified highly diverse age and APOE profiles across sub‐cohorts, leading us to pinpoint outlier sub‐cohorts. Applying our pipelines to the diverse R2 and R4 NIAGADS datasets, we found that removing these outliers improved AD gene discovery by enhancing replication of known AD genes (p 1.2e‐3 and 8.6e‐5 for R2 and R4, respectively) and increasing connectivity to known AD genes in the STRING network (z 5.62 and 5.98, respectively). Further analyses of extremely young and old AD cases within the remaining cohorts also facilitated better gene discoveries. Conclusion The component cohorts of NIAGADS R2–R4 exhibit significant heterogeneity, as demonstrated by the diverse APOE and age distributions. Our approaches showed that optimizing these diverse cohorts based on APOE and age distributions improves AD gene recovery. The heterogeneity of AD cohorts also provides new opportunities to study smaller sub‐groups, such as age of onset and specific APOE profiles. These diverse NIAGADS cohorts could ultimately help define standard criteria for AD cases and control subjects, offering valuable insights for future research.

We recently reported a deletion of exon 2 of the trimethyllysine hydroxylase epsilon (TMLHE) gene in a proband with autism. TMLHE maps to the X chromosome and encodes the first enzyme in carnitine biosynthesis, 6-N-trimethyllysine dioxygenase. Deletion of exon 2 of TMLHE causes enzyme deficiency, resulting in increased substrate concentration (6-N-trimethyllysine) and decreased product levels (3-hydroxy-6-N-trimethyllysine and γ-butyrobetaine) in plasma and urine. TMLHE deficiency is common in control males (24 in 8,787 or 1 in 366) and was not significantly increased in frequency in probands from simplex autism families (9 in 2,904 or 1 in 323). However, it was 2.82-fold more frequent in probands from male-male multiplex autism families compared with controls (7 in 909 or 1 in 130; P = 0.023). Additionally, six of seven autistic male siblings of probands in male-male multiplex families had the deletion, suggesting that TMLHE deficiency is a risk factor for autism (metaanalysis Z-score = 2.90 and P = 0.0037), although with low penetrance (2–4%). These data suggest that dysregulation of carnitine metabolism may be important in nondysmorphic autism; that abnormalities of carnitine intake, loss, transport, or synthesis may be important in a larger fraction of nondysmorphic autism cases; and that the carnitine pathway may provide a novel target for therapy or prevention of autism.

Posterior reversible encephalopathy syndrome (PRES) is a disorder characterized by vasogenic edema affecting the posterior brain region. We report a case of PRES in a 36-year-old woman with preeclampsia who underwent an emergency cesarean section with spinal anesthesia. After surgery, she developed right leg weakness, headache, and seizures. Imaging showed white matter edema consistent with PRES. The exact cause of PRES is unclear, but elevated blood pressure and endothelial dysfunction are implicated. Tight blood pressure control in PRES is crucial for management, and prompt recognition and treatment are essential for favorable outcomes.

LHFPL5, the gene for DFNB67, underlies autosomal recessive nonsyndromic hearing impairment. We identified seven Pakistani families that mapped to 6p21.31, which includes the LHFPL5 gene. Sanger sequencing of LHFPL5 using DNA samples from hearing impaired and unaffected members of these seven families identified four variants. Among the identified variants, two were novel: one missense c.452 G > T (p.Gly151Val) and one splice site variant (c.*16 + 1 G > A) were each identified in two families. Two known variants: c.250delC (p.Leu84*) and c.380 A > G (p.Tyr127Cys) were also observed in two families and a single family, respectively. Nucleotides c.452G and c.*16 + 1G and amino-acid residue p.Gly151 are under strong evolutionary conservation. In silico bioinformatics analyses predicted these variants to be damaging. The splice site variant (c.*16 + 1 G > A) is predicted to affect pre-mRNA splicing and a loss of the 5′ donor splice site in the 3′-untranslated region (3′-UTR). Further analysis supports the activation of a cryptic splice site approximately 357-bp downstream, leading to an extended 3′-UTR with additional regulatory motifs. In conclusion, we identified two novel variants in LHFPL5, including a unique 3′-UTR splice site variant that is predicted to impact pre-mRNA splicing and regulation through an extended 3′-UTR.

Hereditary hearing impairment (HI) displays extensive genetic heterogeneity. To date, 67 autosomal recessive nonsyndromic hearing impairment (ARNSHI) loci have been mapped, and 24 genes have been identified. This report describes three large consanguineous ARNSHI Pakistani families, all of which display linkage to marker loci located in the genetic interval of DFNB49 locus on chromosome 5q13. Recently, Riazuddin et al. ( Am J Hum Genet 2006; 79:1040–1051) reported that variants within the TRIC gene, which encodes tricellulin, are responsible for HI due to DFNB49. TRIC gene sequencing in these three families led to the identification of a novel mutation (IVS4 + 1G > A) in one family and the discovery of a previously described mutation (IVS4 + 2T > C) in two families. It is estimated that 1.06% (95% confidence interval 0.02–3.06%) of families with ARNSHI in Pakistan manifest HI due to mutations in the TRIC gene.

Type 2 diabetes mellitus (T2DM) risk is heavily influenced by genetics, yet current association tests have explained only parts of its heritability. We developed MEVA (Meta-Evolutionary Action), a meta-analytic framework that integrates three complementary methods-EAML, Sigma-Diff, and GeneEMBED-to assess the functional burden of protein-coding variants using evolutionary data. MEVA was applied to exome data from 28,115 T2DM cases and 28,115 controls in the UK Biobank (UKB), identifying 101 genes (p < 1e-5). MEVA outperformed its component methods, each of which substantially outperformed a conventional burden test (MAGMA), in recovering known T2DM genes (AUROC = 0.925) and maintaining robustness in progressively smaller cohorts (AUROC = 0.917). MEVA showed significant enrichment for T2DM-related loci (p = 6.8e-10, p = 2.0e-34), protein interactions (z = 4.6, z = 4.2), pathways (p = 1.3e-6, z = 2.0), phenotypes (p = 1.3e-21, z = 9.1), and literature mentions (z = 7.2). Replication in 16,915 T2DM cases and 16,915 controls from All of Us (AoU) yielded 99 genes (p < 1e-5), 23 of which were also recovered in the UKB cohort - far exceeding random chance. These included established genes (SLC30A8, WFS1, HNF1A) and less-characterized candidates (NRIP1, ADAM30, CALCOCO2, TUBB1, ZFP36L2, WDR90). Notably, NRIP1 loss-of-function variants were associated with increased T2DM risk in both the UKB (OR = 1.09, FDR = 5.4e-4) and AoU (OR = 1.09, FDR = 0.046), and TUBB1 and CALCOCO2 gain-of-function variants showed consistent risk effects (FDR < 0.05). Pathway analyses revealed convergence on endoplasmic reticulum chaperone complexes (FDR = 0.02) and Hippo signaling (FDR = 8.5e-4). Finally, all 177 candidate genes were functionally prioritized using ten orthogonal criteria to guide experimental follow-up. These results demonstrate that combining complementary, impact-aware association tests increases sensitivity, improves replication, and expands the catalog of genetic risk factors for T2DM.

A novel locus for autosomal recessive nonsyndromic hearing impairment (ARNSHI), DFNB96, was mapped to the 1p36.31–p36.13 region. A whole-genome linkage scan was performed using DNA samples from a consanguineous family from Pakistan with ARNSHI. A maximum two-point logarithm of odds (LOD) score of 3.2 was obtained at marker rs8627 (chromosome 1: 8.34 Mb) at θ =0 and a significant maximum multipoint LOD score of 3.8 was achieved at 15 contiguous markers from rs630075 (9.3 Mb) to rs10927583 (15.13 Mb). The 3-unit support interval and the region of homozygosity were both delimited by markers rs3817914 (6.42 Mb) and rs477558 (18.09 Mb) and contained 11.67 Mb. Of the 125 genes within the DFNB96 interval, the previously identified ARNSHI gene for DFNB36, ESPN , and two genes that cause Bartter syndrome, CLCNKA and CLCNKB , were sequenced, but no potentially causal variants were identified.