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PubMed2026

Polygenic risk scores in major depressive disorder: A systematic review across diagnostic, treatment, course/severity, and subtype domains.

BACKGROUND: Major depressive disorder (MDD) is heterogeneous across diagnostic, treatment-related, course/severity, and subtype domains. Polygenic risk score (PRS) studies have examined these domains, but differences in PRS sources, samples, methods, and endpoint definitions have fragmented the evidence. We synthesised findings and examined potential contributors to heterogeneity. METHODS: PubMed/MEDLINE, Embase, PsycINFO, and Web of Science were searched for studies published from January 2016 through 25 November 2025. Result records were synthesised using SWiM, and certainty was assessed with an adapted GRADE framework. RESULTS: Sixty studies contributed 493 retained records; 450 were descriptively classified as positive, null, or reverse, although records were not independent. Positive findings accounted for 44/56 diagnostic, 61/273 treatment-related, 64/100 course/severity, and 14/21 subtype records. For MDD/depression-derived PRSs and case-control MDD status, all 10 contributing studies showed higher liability in cases (exploratory exact sign test p = 0.002; FDR q = 0.004). The same PRS group showed positive findings for overall depressive symptom severity (14/18), although the study-level test was imprecise (5/5 studies; p = 0.063). Pharmacological response/remission findings for these PRSs were mostly null or directionally mixed (10 positive, 18 null, and 9 reverse). Treatment-resistant depression (TRD) findings differed by operational definition. Atypical and psychotic subtype signals arose mainly from single-study PRS and endpoint contrasts. CONCLUSIONS: PRS evidence was clearest for MDD diagnostic status and showed a tentative pattern for overall symptom burden. Treatment and subtype findings were less consistent or less replicated. Larger, ancestrally diverse studies with standardised endpoints and transparent PRS methods are needed.

Humans↗
PubMed2024

Distinct types of selection and genetic architecture shape molecular variation during the domestication of vegetable crops.

Humans select vegetable crops with desirable traits via a complex evolutionary process called domestication, generating a variety of cultivars worldwide. With advances in sequencing technologies, genomic scans for "signatures of selection" are widely used to identify target loci of selection. In the early phases of domestication, humans tended to favor similar sets of phenotypes in diverse crops, resulting in "domestication syndrome" and parallel evolution in multiple species. Subsequently, adaptation to distinct environments or different consumer preferences has diversified crop cultivars. Here, we review molecular and population genetic studies on genes affecting trait evolution during this complex process. We emphasize that, depending on interactions among different types of selection (directional selection within or divergent selection between groups), the genetic architecture of the target trait (Mendelian or polygenic), and the origin of the causal variant (new mutation or standing variation), the resulting molecular patterns of variation can be highly diverse. Situations in which the typical hard selective sweep model could be applied may be limited. Therefore, it is crucial to obtain a thorough understanding of the target species' historical, environmental, and ecological contexts.

Domestication↗
PubMed2026

Application of Omics Technologies for Cowpea Improvement.

Cowpea (Vigna unguiculata) is a vital crop for food security, nutrition, and climate resilience in sub-Saharan African and other semi-arid regions. However, its improvement is constrained by the complexity of polygenic traits such as drought tolerance, pest resistance, and seed quality. Conventional breeding, while foundational, remains insufficient to address these challenges at the required pace. Recent advances in multi-omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, provide new opportunities to dissect complex traits, identify candidate genes, and accelerate the development of resilient, high-yielding cultivars. This review presents a critical synthesis of current applications of omics technologies in cowpea improvement, highlighting their contributions to stress adaptation, nutritional enhancement, and precision breeding. The review also examines key technical and institutional constraints limiting the adoption of omics-assisted breeding in cowpea, including inadequate research infrastructure, challenges in multi-omics data integration, and limited technical capacity across breeding programs in sub-Saharan Africa. It discusses strategies to address these barriers through regional collaboration, investment in bioinformatics capacity, and the integration of computational approaches into breeding pipelines. Overall, the review concludes that combining multi-omics technologies with artificial intelligence and machine learning has strong potential to improve genotype-phenotype prediction, accelerate breeding decisions, and support the development of climate-resilient and nutritionally enhanced cowpea cultivars.

cowpea↗
PubMed2025

Genomic and Developmental Models to Predict Cognitive and Adaptive Outcomes in Autistic Children.

IMPORTANCE: Although early signs of autism are often observed between 18 and 36 months of age, there is considerable uncertainty regarding future development. Clinicians lack predictive tools to identify those who will later be diagnosed with co-occurring intellectual disability (ID). OBJECTIVE: To predict ID in children diagnosed with autism. DESIGN, SETTING, AND PARTICIPANTS: This prognostic study involved the development and validation of models integrating genetic variants and developmental milestones to predict ID. Models were trained, cross-validated, and tested for generalizability across 3 autism cohorts: Simons Foundation Powering Autism Research (SPARK), Simons Simplex Collection, and MSSNG. Autistic participants were assessed older than 6 years of age for ID. Study data were analyzed from January 2023 to July 2024. EXPOSURES: Ages at attaining early developmental milestones, occurrence of language regression, polygenic scores for cognitive ability and autism, rare copy number variants, de novo loss-of-function and missense variants impacting constrained genes. MAIN OUTCOMES AND MEASURES: The out-of-sample performance of predictive models was assessed using the area under the receiver operating characteristic curve (AUROC), positive predictive values (PPVs), and negative predictive values (NPVs). RESULTS: A total of 5633 autistic participants (4574 male [81.2%]) were included in this analysis. On average, participants were diagnosed with autism at 4 (IQR, 3-7) years of age and assessed for ID at 11 (8-14) years of age, with 1159 participants (20.6%) being diagnosed with ID. The model integrating all predictors yielded an AUROC of 0.653 (95% CI, 0.625-0.681), and this predictive performance was cross-validated and generalized across cohorts. This modest performance reflected that only a subset of individuals carried large-effect variants, high polygenic scores, or presented delayed milestones. However, combinations of genetic variants that are typically not considered clinically relevant by diagnostic laboratories achieved PPVs of 55% and correctly identified 10% of individuals developing ID. The addition of polygenic scores to developmental milestones specifically improved NPVs rather than PPVs. Notably, the ability to stratify ID probabilities using genetic variants was up to 2-fold higher in individuals with delayed milestones compared with those with typical development. CONCLUSIONS AND RELEVANCE: Results of this prognostic study suggest that the growing number of neurodevelopmental condition-associated variants cannot, in most cases, be used alone for predicting ID. However, models combining different classes of variants with developmental milestones provide clinically relevant individual-level predictions that could be useful for targeting early interventions.

Humans↗
PubMed1975

Genes affecting productivity in natural populations of Drosophila melanogaster.

Two hundred second chromosomes were extracted from a Japanese population in October of 1972, and the viabilities and productivities of homozygotes and heterozygotes from them were examined. Viability was measured by the Cgamma method and productivity by the number of progeny produced per female. The frequency of lethal-carrying chromosomes was 0.1315. When the average heterozygote viability was standardized as 1.000, the average homozygote viability was 0.595 including the lethal lines, and 0.866 excluding them. The frequency of recessive sterile chromosomes among 131 non-lethal lines was 0.092 in females and 0.183 in males. There were two instances in which homozygosis for the second chromosome caused sterility in both sexes, which was close to the number expected (2.2) on a random basis of 0.0092 S 0.183 X 131. When the average heterozygote productivity of 200 lines was standardized as 1.000, the average homozygote productivity was 0.532 including female steriles, and 0.584 excluding them. The ratio of detrimental load to lethal load was 0.383, while the ratio of partial sterility load to complete sterility load was 7.767. The average viability of lethal heterozygotes was slightly, but not significantly, lower than that of lethal-free heterozygotes, while the average productivity of lethal heterozygotes was significantly lower than that of lethal-free heterozygotes. There was a significant association of sterility in either sex with low viability of homozygotes. However, no statistically significant differences in viability and productivity were detected between sterile heterozygotes and non-sterile heterozygotes. The heterozygous effects of viability and productivity polygenes were examined by regressions of the heterozygotes on the sum of corresponding homozygotes. The regression coefficients were slightly positive for both viability and productivity if lethal and sterile chromosomes were excluded. The correlation between viability and productivity in homozygotes was significantly positive when sterile chromosomes were included, but the significance disappeared when the sterile chromosomes were excluded. In the heterozygotes there were no detectable correlations between them.

Adaptation, Biological↗
PubMed2026

Context matters: coordinated transcriptional regulation and root plasticity under multinutrient conditions.

Plants often encounter simultaneous imbalances in multiple nutrients, but the regulatory logic coordinating their responses remains poorly understood. We aimed to uncover shared transcriptional programs and regulatory nodes underpinning multinutrient adaptation in Arabidopsis thaliana roots. We analyzed publicly available RNA-seq datasets spanning 15 nutrient and beneficial element conditions using differential expression, co-expression network (WGCNA), and gene regulatory network analysis. Selected transcription factors (TFs) were validated via root phenotyping, suberin staining, and ionomic profiling under two-nutrient stress conditions. We identified a core set of 2050 genes responsive to multiple nutrient treatments, enriched for suberin biosynthesis, and structured into modular co-expression clusters. Eight prioritized candidate TFs (ARR10, GBF3, HHO5, NAC32, NF-YA3, NF-YB2, SARD1, and WRKY33) were shown to modulate root system architecture under specific nutrient combinations. WRKY33 and NF-YB2, in particular, regulated nutrient-responsive suberin deposition and ionomic plasticity. These findings reveal suberin remodeling as a shared downstream process in multinutrient responses and suggest that plasticity is not a fixed trait but a modular, polygenic, and context-dependent outcome. Repurposed TFs with pleiotropic functions coordinate structural and physiological traits, providing regulatory entry points for improving nutrient resilience.

Plant Roots↗
PubMed2023

The genetic basis of adaptation to copper pollution in Drosophila melanogaster.

Introduction: Heavy metal pollutants can have long lasting negative impacts on ecosystem health and can shape the evolution of species. The persistent and ubiquitous nature of heavy metal pollution provides an opportunity to characterize the genetic mechanisms that contribute to metal resistance in natural populations. Methods: We examined variation in resistance to copper, a common heavy metal contaminant, using wild collections of the model organism Drosophila melanogaster. Flies were collected from multiple sites that varied in copper contamination risk. We characterized phenotypic variation in copper resistance within and among populations using bulked segregant analysis to identify regions of the genome that contribute to copper resistance. Results and Discussion: Copper resistance varied among wild populations with a clear correspondence between resistance level and historical exposure to copper. We identified 288 SNPs distributed across the genome associated with copper resistance. Many SNPs had population-specific effects, but some had consistent effects on copper resistance in all populations. Significant SNPs map to several novel candidate genes involved in refolding disrupted proteins, energy production, and mitochondrial function. We also identified one SNP with consistent effects on copper resistance in all populations near CG11825, a gene involved in copper homeostasis and copper resistance. We compared the genetic signatures of copper resistance in the wild-derived populations to genetic control of copper resistance in the Drosophila Synthetic Population Resource (DSPR) and the Drosophila Genetic Reference Panel (DGRP), two copper-naïve laboratory populations. In addition to CG11825, which was identified as a candidate gene in the wild-derived populations and previously in the DSPR, there was modest overlap of copper-associated SNPs between the wild-derived populations and laboratory populations. Thirty-one SNPs associated with copper resistance in wild-derived populations fell within regions of the genome that were associated with copper resistance in the DSPR in a prior study. Collectively, our results demonstrate that the genetic control of copper resistance is highly polygenic, and that several loci can be clearly linked to genes involved in heavy metal toxicity response. The mixture of parallel and population-specific SNPs points to a complex interplay between genetic background and the selection regime that modifies the effects of genetic variation on copper resistance.

Drosophila↗
PubMed2025

Whole Genome Sequencing Reveals How Plasticity and Genetic Differentiation Underlie Sympatric Morphs of Arctic Charr.

Salmonids have a remarkable ability to form sympatric morphs after postglacial colonisation of freshwater lakes. These morphs often differ in morphology, feeding and spawning behaviour. Here, we explored the genetic basis of morph differentiation in Arctic charr (n = 283) by first establishing a high-quality reference genome and then using this in whole genome sequencing of distinct morphs present in two Norwegian and two Icelandic lakes. The four lakes represent the spectrum of genetic differentiation between morphs from one lake with no genetic differentiation between morphs, implying phenotypic plasticity, to two lakes with locus-specific genetic differentiation, implying incomplete reproductive isolation, and one lake with strong genome-wide divergence consistent with complete reproductive isolation. As many as 12 putative inversions ranging from 0.45 to 3.25 Mbp in size segregated among the four morphs present in one lake, Thingvallavatn, and these contributed significantly to the genetic differentiation among morphs. None of the putative inversions were found in any of the other lakes, but there were cases of partial haplotype sharing in similar morph contrasts in other lakes. Our findings are consistent with a highly polygenic basis of morph differentiation with population-specific selection on alleles linked to the development of similar morph phenotypes. The results support a model where morph differentiation is first established through phenotypic plasticity, leading to niche expansion and separation. This may be followed by gradual development of reproductive isolation, locus-specific differentiation and eventually complete reproductive isolation and genome-wide divergence.

Whole Genome Sequencing↗