Genetics

Mapping the genetic basis of complex traits is complicated by the presence of epistatic interactions between loci. While work in molecular genetics identifies numerous specific genetic interactions, statistical analyses of quantitative traits frequently conclude that additive (nonepistatic) models explain most heritable variation. However, these conclusions are typically limited by the narrow range of genetic relatedness(e.g. in F1 offspring of a biparental or circular cross). Here, we use a barcoded panel of Saccharomyces cerevisiae genotypes with a broad range of relatedness to quantify the effects of epistasis on the genetic architecture of seven complex traits. We find limited contributions of epistasis to the genetic basis of these traits. These results indicate that epistasis beyond that detected in standard yeast crosses may exist, yet it contributes little to phenotypic variance in these systems.

We study one of the simplest scenarios of polygenic selection that can be imagined: a subdivided population of diploid individuals expressing an additive trait under spatially homogeneous stabilizing selection. We are interested in the amounts of variation that can be maintained at mutation-selection-migration-drift equilibrium, at individual loci and at the level of the trait, within and among subpopulations. We derive analytical approximations for variance components and summary statistics such as FST and QST under the assumptions of the infinite-island model and compare these with individual-based simulations. We find that: (i) There is a critical migration threshold (which depends on effect sizes of trait loci) below which population structure strongly inflates genic variance in the subdivided population to levels well above those in a panmictic population. Variation within each subpopulation is maximized close to the critical migration rate. (ii) The genetic basis of trait variation across subpopulations is most similar close to this migration threshold and (counter-intuitively) decreases for higher migration rates. This has consequences for the portability of Genome-Wide Association Studies (GWAS) between subpopulations, i.e, the extent to which loci with large contributions to variance in one subpopulation explain variance in other subpopulations. (iii) An analytical mean-field approach based on the single-locus diffusion approximation, together with effective migration and selection parameters (to account for associations between loci), very accurately predicts various quantities.

Across many complex traits, genetic variants with larger effect sizes tend to occur at lower frequencies, which is often interpreted as a signature of stabilizing selection. In statistical genetics, the so-called -model captures this relationship by assuming that effect size variance is inversely proportional to heterozygosity raised to a power 0 [<=] [<=] 1. Although empirically useful, the -model is phenomenological rather than mechanistic and lacks a direct population-genetic interpretation. In this paper, we derive an alternative to the -model based on evolutionary theory. Our approach yields a linear mixed model in which the frequency dependence of effect size emerges naturally as a function of interpretable evolutionary quantities describing mutational variance, selection intensity, and coupling between the focal and selected traits. These quantities enter through two identifiable variance components that can be estimated by restricted maximum likelihood (REML). The resulting framework links a fitness-landscape model to standard mixed-model methodology, enabling both inference on evolutionary parameters and downstream prediction by best linear unbiased prediction (BLUP). In forward simulations, the model accurately recovers the focal-trait variance and generally improves genetic prediction relative to conventional -model baselines.

Understanding how mutations evolve on Y chromosomes is central to explaining the origin, diversity and persistence of sex chromosomes. Mutations occurring on the Y chromosome in sexual populations experience selective dynamics that differ markedly from those on autosomes, due to a reduced effective population size and the presence of large non-recombining regions containing alleles maintained in a permanently heterozygous state. These specific features alter gene transmission in the Y chromosome population compared to autosomes, even within the same pedigree. Here, we provide a two-sex diploid Wright-Fisher model that explicitly incorporates both sex chromosomes and autosomes within a unified population framework, in order to capture the influence of these specificities on the fate of mutations, not only considering fixation probabilities but also segregation times. We use diffusion approximations and provide analytical and numerical tools to compute these quantities across a wide range of parameters and selection regimes. We recover classical results on fixation probabilities in various scenarios, including purely beneficial, deleterious or overdominant mutations, and extend them in the light of mean segregation time, a key but often overlooked determinant of evolutionary outcomes over finite timescales. In particular, our analyses show that overdominant mutations are overall more likely to fix in observable time windows on the Y chromosome than on autosomes. Individual-based simulations corroborate our approximations and highlight parameter regimes where the theoretical approach is particularly useful, especially for parameter values inducing long segregation times or small fixation probabilities, for which simulations are impractical. Our results provide a comprehensive and tractable framework for clarifying how chromosome-specific features shape evolutionary dynamics beyond fixation probabilities alone.

Phenotypic variation arises from the interplay of genetic, environmental, and stochastic developmental factors. Quantitative genetics predicts that reducing genetic variation through inbreeding or clonality should reduce phenotypic variation, an assumption that underlies the widespread use of inbred and clonal model organisms in biomedical research. Here, we test this assumption in the facultatively parthenogenic fly Drosophila mercatorum, in which parthenogenesis results in complete homozygosity and clonality after a single generation. Contrary to expectation, clonal parthenogenic flies showed broad shifts in trait means, altered interindividual variability, increased fluctuating asymmetry, and reduced behavioral and developmental canalization relative to sexually reproducing controls. Inbreeding reproduced substantial parts of this phenotype, whereas outcrossing restored robustness, identifying loss of heterozygosity as a major driver of the effect. Our findings show that extreme genetic uniformity can amplify rather than constrain stochastic phenotypic divergence, suggesting that controlled heterozygosity may, in some contexts, provide a more robust and reproducible experimental substrate than highly inbred, isogenic, or clonal animals.

Genome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, "White and Asian" (WA), and "Any Other Asian" (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities and use consistent patterns of clustering in the dataset to train a support vector machine (SVM). The SVM was utilized to reassign n = 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We further leverage these samples to assess GWAS performance and PGS development. We include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnull and GWASenv. We show that PGS performance derived from both GWAS models yield comparable prediction to PGS models developed with an order of magnitude larger training, and environmentally-adjusted PGS models reduce the sex-bias in predictive performance. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.

The cohesin complex has conserved roles in sister chromatid cohesion, DNA replication, genome organization, and the DNA damage response. We heterologously expressed the human cohesin complex in yeast to probe the behaviour of human cohesin. Human cohesin was unable to complement loss of function mutations in yeast cohesin, either as single subunits or as complexes, including in the context of co-expressing up to 12 human cohesin-associated genes. Heterologous expression of human cohesin in yeast expressing wildtype yeast cohesin resulted in dominant cohesion dysregulation and DNA damage sensitivity phenotypes. We used co-immunoprecipitation to demonstrate that human SMC proteins interact with endogenous yeast cohesin rings creating dominant-negative hybrid complexes that disrupt endogenous cohesin biology.

Global networks of crop breeding programs leverage diverse germplasm, but diversity increases the complexity of maintaining stability in their elite genepools. To characterize genetic heterogeneity in breeding metapopulations and develop insights on how to manage it, we simulated the evolution of breeding populations on fitness landscapes. We revealed the geometric decrease in the average effect size of alleles segregating as standing variation that become fixed along an adaptive walk. We also demonstrated how independent adaptive walks of subpopulations are influenced by genetic drift, leading to cryptic genetic heterogeneity among elite genepools. This variation is released when elite lines derived from independent subpopulations are crossed, leading to segregation for 2-4X more major QTL in admixed families as in unadmixed families, and 2-4X more epistatic interactions. The emergent property of fitness epistasis for traits under stabilizing selection is well-understood in evolutionary genetics, but under-appreciated in crop quantitative genetics. To highlight the importance of this phenomenon, we constructed an empirical genotype-to-fitness landscape from the sorghum NAM, a global admixed prebreeding resource, demonstrating the utility of fitness landscapes for inferring genetic compatibilities within metapopulations. Our findings suggest that in breeding networks, strategies for effective germplasm exchange must account for epistasis in the oligogenic component of the genetic architecture of locally-adapted traits. Article summaryModern public sector crop improvement happens in networks of breeding programs that routinely exchange genetic information. Traditional models for understanding quantitative traits have limited predictiveness in situations with such genetic heterogeneity. This study uses breeding simulations and empirical data to show the utility of the fitness landscape framework for characterizing the genetic architecture of complex traits in breeding metapopulations. By simulating the evolution of breeding programs and integration into networks, it demonstrates how epistatic interactions between large-effect alleles are a fundamental property that must be accounted for when exchanging germplasm. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/712732v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1541326org.highwire.dtl.DTLVardef@b553a8org.highwire.dtl.DTLVardef@8758b4org.highwire.dtl.DTLVardef@1d0bdcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Accurate estimation of population demographic history is central to population genetics yet remains challenging due to the sensitivity of inference methods to the number of individuals and the demographic scenario assumed in inference. The site-frequency spectrum (SFS) of neutral variants, a widely used summary statistic of genetic variation, is particularly sensitive to demographic processes, but studies have shown that qualitative results from demographic inference, i.e., population expansion vs. contraction, can depend strongly on the number of individuals in the dataset. Here, we analyzed two simulated datasets and one empirical dataset characterized by an ancient population bottleneck followed by a recent population expansion. Fitting a two-epoch demographic model across a range of sample sizes, we found that inference shifted from signals of ancient population contraction at small sample sizes to signals of recent population expansion at large sample sizes. Other summary statistics, including Tajimas D and the proportion of singletons, also changed with sample size. We found that these changes of inferred evolutionary signals under a two-epoch model can be explained by the epoch which contributes the highest mean proportion of coalescent branch lengths. Our results highlight that demographic inference depends critically on the number of individuals analyzed and suggest that analyzing datasets at multiple sample sizes can reveal complementary aspects of population history.

Genetic evidence for fluctuating selection has begun to accumulate for different species over the past few decades, especially for the Drosophila genus where studies have reported hundreds of loci undergoing putatively adaptive oscillations across successive seasons. However, most theoretical and simulation studies of fluctuating selection have relied on abstract or weakly parameterized models, making it difficult to assess their relevance for natural populations. In this study, we simulate multilocus seasonally fluctuating selection under a recently developed model and examine its effect on the variance effective population size (Ne) at a genome-wide scale. By recapitulating genomic, demographic, and evolutionary parameters from natural Drosophila populations in our simulations, we were able to reproduce allele frequency oscillations reported in recent studies and show that these lead to [~]50% genome-wide reductions in Ne. We also demonstrate that Ne reductions are well predicted by the maximum frequency amplitude among all adaptively fluctuating loci, and that the frequency amplitudes are largely determined by the number of adaptively fluctuating loci and the strength of their epistatic interactions. Our results demonstrate that fluctuating selection can substantially reduce effective population size and underscore the importance of temporally variable selection in shaping genome-wide patterns of variation beyond classical models. Article SummaryGenetic studies of fluctuating selection in natural populations have grown steadily over the past decade, with reports suggesting that hundreds of loci undergo adaptive oscillations over seasonal timescales in cosmopolitan Drosophila populations. By simulating seasonally fluctuating selection under a recently developed model and ecological scenarios informed by published studies, the authors show that this mode of selection can reduce effective population size by [~]50%, with the magnitude of the reduction correlated with the locus exhibiting the largest allele frequency fluctuations. These findings highlight fluctuating selection as an important factor shaping genome-wide patterns of genetic variation and effective population size.

Intensifying insecticide resistance in the malaria vector Anopheles gambiae poses a serious threat to the hard-won gains in reducing malaria deaths in Africa. The genetic basis of insecticide resistance is often complex, involving multiple genes and mutations. However, we still lack a clear understanding of how each mechanism contributes to overall resistance and how highly resistant phenotypes arise. In this study we generated a suite of transgenic An. gambiae strains carrying either individual mechanisms or combinations that frequently co-occur in nature. We show that co-overexpression of different classes of detoxification enzymes (CYP6P3, CYP6M2, CYP9K1, ABCH2, GSTE2 and COEAE6G), as well as the overexpression of detoxification enzymes in the presence of target site resistance mutations, can lead to substantially greater levels of resistance. Our findings suggest that increased resistance strength is a primary driver for selection of multi-mechanism resistance and are transformative for the scientific insight required to design robust molecular diagnostics for timely and reliable resistance detection in the field. We further show that P450 based resistance can constitute an Achilles heel for highly resistant mosquitoes, making them more vulnerable to pro-insecticides; compounds that typically require P450 activation. Our results advance our understanding of the mechanistic basis of insecticide resistance and have important implications for the design and implementation of effective and evidence-based resistance management strategies.

The neuromuscular junction (NMJ) of larval Drosophila is widely used for studying synaptic transmission. Larval body wall muscles express five ionotropic glutamate receptor (iGluR) subunits that assemble into two tetrameric complexes, with subunit composition determining the strength and plasticity of synaptic transmission. Because NMJ function has been extensively characterized in larvae, it is often assumed that adult fly NMJs have similar molecular composition, despite substantial differences between life stages. Here, we systematically compare glutamate receptor expression across larval and adult Drosophila muscles. We find that adult leg and flight muscles exhibit different iGluR expression than larvae, lacking several receptors previously considered essential for viability and NMJ function. Adjacent muscles within the adult femur express distinct iGluRs, suggesting specialization of flexor and extensor muscles. Finally, the glutamate-gated chloride channel (GluCl) is expressed extrasynaptically in adult but not larval muscle fibers. Our results reveal unexpected heterogeneity in glutamate receptor expression across muscles and developmental stages, challenging assumptions about the uniformity of neuromuscular function and demonstrating the need for muscle-specific analyses in flies and other animals.

The correct assembly of ribosomes is essential for viability and faithful gene expression. In eukaryotic cells, the pre-40S and pre-60S ribosomal subunits are largely pre-assembled in the nucleolus before they are exported to the cytoplasm for final maturation. Although most ribosomal proteins of the large subunit are loaded onto pre-60S particles in the early nucleolar steps, a few, including eL24, are loaded in the cytoplasm. eL24 is thought to recruit the zinc-finger protein Rei1 (ZNF622 in humans). In yeast, Rei1 has a paralog, Reh1. While we and others have previously shown that Rei1 facilitates the removal of Arx1, Rei1 and Reh1 appear to have an additional unknown function. To identify this function, we first examined the protein composition of pre-60S subunits isolated from rei1{Delta} reh1{Delta} mutant cells and found that these subunits were specifically defective for eL24. However, the absence of eL24 did not impair Rei1 binding to pre-60S. Moreover, overexpression of eL24 suppressed the growth defect of the double mutant. As an alternative approach to understanding the function of Rei1 and Reh1, we screened for bypass suppressors of the growth defect of rei1{Delta} reh1{Delta} cells. We identified mutations in the genes coding for ribosomal protein uL3, the GTPase Lsg1 and the protein phosphatase Ppq1. Importantly, these suppressors all partially reversed the eL24 loading defect of rei1{Delta} reh1{Delta} cells. Based on these results, we propose a revised order of cytoplasmic assembly events where Rei1 and Reh1 facilitate the recruitment of eL24 to the pre-60S particle.

Additive fitness landscapes--also called Mount Fuji landscapes--are the simplest and most widely used models of sequence-function relationships. As such, they play essential roles across multiple areas of biology, including evolutionary theory, quantitative genetics, gene regulation, and protein science. One of the most basic properties of any fitness landscape is its genotypic density--the number of sequences near a given fitness value. Understanding this density is especially important near fitness peaks, as it quantifies the supply of high-fitness genotypes. Here I study the genotypic density of additive landscapes near fitness peaks. Although this density is well known to be approximately Gaussian near the middle of the fitness range, its behavior near maximal fitness has not been reported. I begin by deriving a saddle-point approximation that accurately describes the genotypic density of additive landscapes over virtually the entire fitness range. I then show that the log density follows a power law near maximal fitness, with an exponent determined by how much the best allele at each position outperforms its nearest competitor. This power-law behavior holds over a substantial fraction of fitness values, besting the Gaussian approximation on both simulated and empirical landscapes across roughly a quarter to a third of the fitness range. Under certain conditions this behavior also extends to globally epistatic landscapes (defined as nonlinear functions over one or more additive traits), though with a reduced range of validity. These findings advance our understanding of one of the most fundamental models of sequence-function relationships. In particular, they reveal that the uppermost reaches of Mount Fuji landscapes, rather than being sharply peaked, are actually quite stubby.

BackgroundMaize knobs are regions of constitutive heterochromatin that are readily identified in both meiotic and somatic chromosomes. These structures have been characterized as stable throughout the cell cycle, exhibiting late replication during the S-phase, and are composed of two specific families of highly repetitive DNA sequences: K180 and TR-1. Although widely used as cytogenetic markers due to their variability in number and chromosomal position across inbred lines, hybrids, and landraces, little is known about their chromatin structure and dynamics. In this study, we analyzed chromatin accessibility of knobs using DNS-seq data across four maize tissues representing distinct developmental stages. ResultsOur results reveal that K180 knobs exhibit tissue-specific variation in chromatin accessibility, transitioning between open and closed states during development. In contrast, the TR-1 knob of chromosome 4 remained consistently inaccessible across all tissues analyzed. A knob composed of both K180, and TR-1 further supported this observation, with only the K180 region showing dynamic accessibility. To validate these findings, we also analyzed other repetitive regions such as centromeres, which showed a uniformly closed chromatin structure similar to TR-1. These results suggest a unique developmental modulation of chromatin accessibility associated with K180 repeats. While the chromatin accessibility of knobs does not reach the levels observed at Transcription Start Sites (TSS), the comparison among different classes of repetitive DNA within maize constitutive heterochromatin provides compelling evidence for sequence-specific and tissue-specific chromatin dynamics. ConclusionsOur findings uncover a previously unrecognized property of maize knobs and establish a reference for future studies on chromatin organization and epigenetic regulation of repetitive DNA in plant genomes.

Hox genes have been broadly implicated in nervous system development, but the molecular and genetic mechanisms that act downstream of Hox factors remain to be identified. The MAB-5 antennapedia-like Hox transcription factor is both necessary and sufficient to cause posterior migration of the Q neuroblast descendants in Caenorhabditis elegans. In response to MAB-5, the left-side QL descendants QL.a and QL.ap undergo a three-stage migration process, with each stage characterized by a posterior lamellipodial protrusion followed by cell body migration. The QL.ap cell differentiates into the PQR neuron posterior to the anus. Previous studies showed that the MAB-5-regulated gene efn-4/Ephrin was required for the third and final stage of QL.ap migration, with efn-4 mutation resulting in placement of PQR immediately anterior to the anus. This subtle and previously-undescribed phenotype opens the possibility that other known neuronal development genes could be involved. In this work, we screened known signaling mutants for third-stage PQR migration defects. We found that mutations in SAX-3/Robo signaling, UNC-6/Netrin signaling, and heparan sulfate proteoglycans (HSPGs) all displayed third-stage PQR migration defects. The effects in single mutants were weak compared to efn-4, and double mutant analysis revealed lack of genetic synergy, consistent with all of these molecules converging on a common pathway. This genetic analysis is consistent with physical interaction studies in vitro from another group that suggest that these molecules form connected communities of interacting extracellular domains, raising the possibility that they are all components of a large extracellular signaling complex required for posterior QL.ap migration. In this model, we envision that MAB-5/Hox drives EFN-4/Ephrin expression in QL.ap, which then seeds the formation of an extracellular signaling complex containing SAX-3/Robo signaling, UNC-6/Netrin signaling, and HSPGs that drives posterior lamellipodial formation and posterior migration.

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis-regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis-regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

The architecture of the genotype-phenotype-fitness map (GPFM) is a key determinant of evolutionary dynamics. One salient feature of biological GPFMs is variational modularity, where each mutation affects only a small subset of functional traits. Variational modularity may constrain the dynamics of trait evolution, but these constraints are not well understood. Here, we use several extensions of the Fishers geometric model with two functional traits to investigate these constrains. We find that on GPFMs with universal pleiotropy, populations evolve along the fitness gradient, which implies that the trait under stronger selection is optimized exponentially faster than the trait under weaker selection. In contrast, on modular GPFMs, populations approach a quasi-steady state that we term a "module-selection balance" where both traits improve at the same rate and their ratio remains constant. We demonstrate that the existence of a module-selection balance is robust with respect to the details of evolutionary dynamics and GPFMs themselves, as long as they are variationally modular. Our theory predicts that variationally modular organisms should exhibit stereotypical bi-phasic dynamics of genome evolution, especially in the strong clonal interference regime, and we find support for this prediction in metagenomic data from Lenskis long-term evolution experiment in bacterium Escherichia coli. We propose that module-selection balance is an inherent feature of variationally modular GPFMs, which imposes an important constraint on long-term trait evolution.

Population genetic models excel at identifying the conditions for polymorphisms based on balancing selection but typically disregard the ecological processes that yield particular values of selection coefficients. We model a system that combines antagonistic pleiotropy, dominance reversal and heterozygote advantage: the wood tiger moth Arctia plantaginis, where alternative haplotypes at a major-effect locus determine male hindwing coloration. Yellow offers better protection against predators, while white is often associated with better mating success. The effects of mortality and reproductive success overlap in time because protandrous males can mate as long as they are alive, but they need to avoid predation for several days before the bulk of females emerge. We show that protandry aids polymorphism maintenance whenever the second-fittest genotype (after the heterozygote) is the poorly surviving but mating advantaged homozygote, while increased protandry harms polymorphism when the second-best fitness is that of the survival advantaged morph. Ecologically plausible protandry times predict that dominance reversal does not have to be strong for polymorphism to be maintained. Our study highlights the importance of timing traits in maintaining polymorphisms in Lepidoptera and showcases the benefits of deriving fitness explicitly in place of abstract selection coefficients that lack temporal components within the life cycle.

Grain mold severely constrains sorghum [Sorghum bicolor (L.) Moench] productivity and grain quality in subhumid environments. Photoperiod-sensitive flowering plays a key role in mold avoidance and yield stability along north-south rainfall gradients. In response to the high susceptibility of elite cultivars in subhumid zones of Senegal, we developed and characterized a recombinant inbred line (RIL) population derived from Nganda (grain mold-susceptible) and Grinkan (photoperiod-sensitive) varieties. The population was evaluated across three distinct agro-ecological zones over two years. Environmental indices derived from genotype-environmental interactions, together with defined growth windows, strongly influenced flag leaf appearance (FLA), a photoperiodic flowering trait. Plasticity parameters (intercept and slope) for environmental indices, FLA, grain mold severity, and yield enabled identification of loci contributing to flowering response, mold resistance, and yield stability. The maturity gene Ma1 and two QTLs for FLA, qFLA6.2 and qFLA6.3, were identified, stable across environments, and colocalized with grain mold and yield QTLs. The wild-type Ma1 allele from Grinkan delayed FLA and reduced grain mold damage but was not associated with increased yield. The Ma1 effect was confirmed using the developed breeder-friendly KASP marker, Sbv3.1_06_40312464K, in 174 F3 three-way cross families. Photoperiod-sensitive lines with intermediate-to-late FLA alleles showed strong negative associations with mold damage. Overall, the identified stable loci and candidate lines provide foundations for effective molecular breeding of climate-resilient varieties. PLAIN LANGUAGE SUMMARYGrain mold is a fungal disease that reduces sorghum grain yield and quality, particularly in subhumid climates. With the limited number of resistant elite varieties, photoperiod-sensitive flowering to day length variation can contribute to grain mold escape at the end of rainy seasons. We characterized 286 sorghum recombinant inbred lines across three contrasting environments over two years along rainfall gradients in Senegal. Using flag leaf appearance (FLA), which is a photoperiodic flowering trait, strong genotype-environment interactions for FLA and genotypic plasticity were revealed. We identified and validated the common genomic locus associated with FLA variation and its plasticity across environments, the canonical maturity gene Ma1, which was influenced by temperature variation across environments. The presence of Ma1 in the background of photoperiod-sensitive lines enhances grain mold avoidance and yield stability along rainfall gradients in Senegal. CORE IDEASO_LIWe investigated photoperiodic flowering plasticity in sorghum as a contributor to grain mold resistance and yield stability along rainfall gradients. C_LIO_LIThe Maturity locus Ma1 (qFLA6.1) is the major contributor of photoperiodic flowering and its plasticity across semi-arid and subhumid environments. C_LIO_LIHybrid genotypes carrying two stable loci qFLA6.1 and qFLA6.2 sustain high grain mold avoidance in diverse environments. C_LIO_LIPhotoperiod-sensitive lines with medium to late flowering times are effective in avoiding grain mold, while maintaining yield stability in subhumid regions. C_LI