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Natural populations can vary considerably in their genotypic and/or phenotypic diversity. Differences in this intraspecific diversity can have important consequences for contemporary ecological dynamics, but the direction and magnitude of these effects appear inconsistent across studies and systems. Here we proposed and tested the hypothesis that context-dependent ecological effects of altering phenotypic variance are predictable and arise from the relationship between a population's mean phenotype and the local environmental optimum. By factorially manipulating the mean and variance of a key host trait in environments with and without a lethal parasite, we demonstrate that increasing phenotypic variance can have beneficial effects for host populations (e.g., smaller disease epidemics) but only when the population's initial phenotype was poorly matched to the local environment. When phenotypes were initially well suited to environmental conditions, in contrast, greater phenotypic variance led to larger disease epidemics. Significant reductions in individual susceptibility occurred in both contexts over time, but the mechanisms leading to those reductions differed; strong selection was caused by either a suboptimal trait mean and insufficient trait variance or a near-optimal trait mean and too much trait variance. Increasing intraspecific variation is clearly not always beneficial for populations, instead producing predictable ecological and evolutionary effects that depend on environmental context and biological interactions.
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http://dx.doi.org/10.1086/703483 | DOI Listing |
Clin Breast Cancer
August 2025
Department of Pharmacy, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, School of Pharmacy, Fujian Medical University, Fuzhou, China. Electronic address:
Background: Emerging evidence suggests that the gut microbiota (GM) may influence the progression of breast cancer by modulating immune responses. Given the vast diversity of GM and immune cell phenotypes, this study aimed to utilize the most advanced and comprehensive data to explore the causal relationships among the GM, immune cell phenotypes, and survival rates in hormone receptor-positive (HR+) breast cancer patients under different treatment regimens.
Methods: We investigated the causal relationships between the GM, immune cell phenotypes, and survival rates in HR+ breast cancer patients treated with 11 distinct therapeutic strategies using Mendelian randomization.
Gene
September 2025
Agri Biotech Foundation, Rajendranagar, Hyderabad 500 030 TS, India; Present address, Department of Agricultural Education, Sunchon National University, 413 Jungangno, Suncheon, Jeonnam 57922, Republic of Korea. Electronic address:
This study aimed to identify QTL governing three traits of the resistance against the two planthoppers such as damage score (DS), nymphal survival (NS) and days to wilt (DW) using the 94 RIL population derived from the cross TN1/RP2068 utilizing 125 SSR and 1500 SNP markers. In case of the whitebacked planthopper (WBPH) five major and three minor QTL while for the brown planthopper (BPH) four major and seven minor QTL were identified to be associated with these three traits. Two major QTL, each on chromosomes 1 and 2, were responsible for DS and NS against WBPH accounted for 25% and 16% of the phenotypic variance (PVE).
View Article and Find Full Text PDFTheor Appl Genet
September 2025
Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, Australia.
Stacking desirable haplotypes across the genome to develop superior genotypes has been implemented in several crop species. A major challenge in Optimal Haplotype Selection is identifying a set of parents that collectively contain all desirable haplotypes, a complex combinatorial problem with countless possibilities. In this study, we evaluated the performance of metaheuristic search algorithms (MSAs)-genetic algorithm (GA), differential evolution (DE), particle swarm optimisation (PSO), and simulated annealing (SA) for optimising parent selection under two genotype building (GB) objectives: Optimal Haplotype Selection (OHS) and Optimal Population Value (OPV).
View Article and Find Full Text PDFHGG Adv
September 2025
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA; Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA. Electronic address:
Pleiotropy, the phenomenon where a genetic region confers risk to multiple traits, is widely observed, even among seemingly unrelated traits. Knowledge of pleiotropy can improve understanding of biological mechanisms of diseases/traits, and can potentially guide identification of molecular targets or help predict side-effects in drug development. However, statistical approaches for identifying pleiotropy genome-wide are limited, particularly for two correlated traits or case-control traits with unknown sample overlap or for disease traits from family studies.
View Article and Find Full Text PDFHere, we present a novel approach to estimate the degree to which the phenotypic effect of a DNA locus is attributable to four components: alleles in the child (direct genetic effects), alleles in the mother and the father (indirect genetic effects), or is dependent upon the parent from which it is inherited (parent-of-origin, PofO effects). Applying our model, JODIE, to 30,000 child-mother-father trios with phased DNA information from the Estonian Biobank (EstBB) and the Norwegian Mother, Father, Child Cohort (MoBa), we jointly estimate the phenotypic variance attributable to these four effects unbiased of assortative mating (AM) for height, body mass index (BMI) and childhood educational test score (EA). For all three traits, direct effects make the largest contribution to the genetic effect variance.
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