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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

The genetic makeup of the electrocardiogram

Since its original description in 1893 by Willem van Einthoven, the electrocardiogram (ECG) has been instrumental in the recognition of a wide array of cardiac disorders1,2. Although many electrocardiographic patterns have been well described, the underlying biology is incompletely understood. Genetic associations of particular features of the ECG have been identified by genome wide studies. This snapshot approach only provides fragmented information of the underlying genetic makeup of the ECG. Here, we follow the effecs of individual genetic variants through the complete cardiac cycle the ECG represents. We found that genetic variants have unique morphological signatures not identfied by previous analyses. By exploiting identified abberations of these morphological signatures, we show that novel genetic loci can be identified for cardiac disorders. Our results demonstrate how an integrated approach to analyse high-dimensional data can further our understanding of the ECG, adding to the earlier undertaken snapshot analyses of individual ECG components. We anticipate that our comprehensive resource will fuel in silico explorations of the biological mechanisms underlying cardiac traits and disorders represented on the ECG. For example, known disease causing variants can be used to identify novel morphological ECG signatures, which in turn can be utilized to prioritize genetic variants or genes for functional validation. Furthermore, the ECG plays a major role in the development of drugs, a genetic assessment of the entire ECG can drive such developments.

genetics↗

bioRxiv2019

Genetic diversity of Collaborative Cross mice controls viral replication, clinical severity and brain pathology induced by Zika virus infection, independently of Oas1b

The explosive spread of Zika virus (ZIKV) has been associated with major variations in severe disease and congenital afflictions among infected populations, suggesting an influence of host genes. We investigated how genome-wide variants could impact susceptibility to ZIKV infection in mice. We first describe that the susceptibility of Ifnar1 knockout mice is largely influenced by their genetic background. We then show that the broad genetic diversity of Collaborative Cross mice, which receptor to type I interferon (IFNAR) was blocked by anti-IFNAR antibody, expressed phenotypes ranging from complete resistance to severe symptoms and death with large variations in the peak and rate of decrease of plasma viral load, in brain viral load, in brain histopathology and in viral replication rate in infected cells. Differences of susceptibility between CC strains were correlated between Zika, Dengue and West Nile viruses. We identified highly susceptible and resistant mouse strains as new models to investigate the mechanisms of human ZIKV disease and other flavivirus infections. Genetic analyses revealed that phenotypic variations are driven by multiple genes with small effects, reflecting the complexity of ZIKV disease susceptibility in human population. Notably, our results rule out a role of the Oas1b gene in the susceptibility to ZIKV. Altogether, this study emphasizes the role of host genes in the pathogeny of ZIKV infection and lays the foundation for further genetic and mechanistic studies.\n\nIMPORTANCEIn recent outbreaks, ZIKV has infected millions of people and induced rare but potentially severe complications, including Guillain-Barre syndrome and encephalitis in adults. While several viral sequence variants were proposed to enhance the pathogenicity of ZIKV, the influence of host genetic variants in the clinical heterogeneity remains mostly unexplored. We have addressed this question using a mouse panel which models the genetic diversity of human population and a ZIKV strain from a recent clinical isolate. Through a combination of in vitro and in vivo approaches, we demonstrate that multiple host genetic variants determine viral replication in infected cells, and clinical severity, kinetics of blood viral load and brain pathology in mice. We describe new mouse models expressing high susceptibility or resistance to ZIKV and to other flaviviruses. These models will facilitate the identification and mechanistic characterization of host genes that influence ZIKV pathogenesis.

genetics↗

bioRxiv2019

Large-scale GWAS in sorghum reveals common genetic control of grain size among cereals

O_LIGrain size is a key yield component of cereal crops and a major quality attribute. It is determined by a genotypes genetic potential and its capacity to fill the grains.\nC_LIO_LIThis study aims to dissect the genetic architecture of grain size in sorghum via an integrated genome wide association study (GWAS) using a diversity panel of 837 individuals and a BC-NAM population of 1,421 individuals.\nC_LIO_LIIn order to isolate genetic effects associated with grain size, rather than the genotypes capacity to fill grain, a field treatment of removing half of the panicle during flowering was imposed. Extensive variation in grain size with high heritability was observed in both populations across 5 field trials. Subsequent GWAS analyses uncovered 92 grain size QTL, which were significantly enriched for orthologues of known grain size genes in rice and maize. Significant overlap between the 92 QTL and grain size QTL in rice and maize was also found, supporting common genetic control of this trait among cereals. Further analysis found grain size genes with opposite effect on grain number were less likely to overlap with the grain size QTL from this study, indicating the treatment facilitated identification of genetic regions related to the genetic potential of grain size rather than the capacity to fill the grain.\nC_LIO_LIThese results enhance understanding of the genetic architecture of grain size in cereal, and pave the way for exploration of underlying molecular mechanisms in cereal crops and manipulation of this trait in breeding practices.\nC_LI

genetics↗

bioRxiv2019

The Genetic History of France

The study of the genetic structure of different countries within Europe has provided significant insights into their demographic history and their actual stratification. Although France occupies a particular location at the end of the European peninsula and at the crossroads of migration routes, few population genetic studies have been conducted so far with genome-wide data. In this study, we analyzed SNP-chip genetic data from 2 184 individuals born in France who were enrolled in two independent population cohorts. Using FineStructure, six different genetic clusters of individuals were found that were very consistent between the two cohorts. These clusters match extremely well the geography and overlap with historical and linguistic divisions of France. By modeling the relationship between genetics and geography using EEMS software, we were able to detect gene flow barriers that are similar in the two cohorts and corresponds to major French rivers or mountains. Estimations of effective population sizes using IBDNe program also revealed very similar patterns in both cohorts with a rapid increase of effective population sizes over the last 150 generations similar to what was observed in other European countries. A marked bottleneck is also consistently seen in the two datasets starting in the fourteenth century when the Black Death raged in Europe. In conclusion, by performing the first exhaustive study of the genetic structure of France, we fill a gap in the genetic studies in Europe that would be useful to medical geneticists but also historians and archeologists.

genetics↗

bioRxiv2019

Decoupling Genetics from Attainments: The Role of Social Environments

This paper examines the extent to which growing up in a socially mobile environment might decouple genetic endowments related to educational attainment with actual attainments. Many models of intergenerational transmission of advantage contain both a transmission channel through endowments (i.e. genetics) from parents to children as well as from parental investments and \"luck\". Indeed, many scholars consider the intergenerational links due to the transmission of genetically based advantage to place a lower bound on plausible levels of social mobility--genetics may be able to \"lock in\" advantage across generations. This paper explores this idea by using new genetic measurements in the Health and Retirement Study to examine potential interactions between social environments and genetics related to attainments. The results suggest evidence of gene environment interactions: children born in high mobility states have lower genetic penetrance--the interaction between state-level mobility and the polygenic score for education is negative. These results suggest a need to incorporate gene-environment interactions in models of attainment and mobility and to pursue the mechanisms behind the interactions.

genetics↗

bioRxiv2019

Simulating isolated populations to identify emerging genetic structure in the absence of selection

Conservation efforts are often informed by measures of genetic structure within or between isolated populations. We have established a simulation approach to investigate how isolated or captive populations can display misleading (i.e recently acquired) genetic structure as a result of genetic drift. We utilized a combination of softwares to generate isolated population genetic datasets that allow interrogation of emerging genetic structure under a range of conditions. We have developed a new statistic, S, to describe the extent of differentiation due to genetic drift between two isolated populations within the clustering software, STRUCTURE. O_LIA novel method to infer the effects of genetic drift on structure among isolated populations C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=186 HEIGHT=200 SRC="FIGDIR/small/853895v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@19eabc7org.highwire.dtl.DTLVardef@1fc4cforg.highwire.dtl.DTLVardef@1b1f947org.highwire.dtl.DTLVardef@164c916_HPS_FORMAT_FIGEXP M_FIG C_FIG SPECIFICATIONS TABLE O_TBL View this table: org.highwire.dtl.DTLVardef@1cdff90org.highwire.dtl.DTLVardef@1455d67org.highwire.dtl.DTLVardef@f74715org.highwire.dtl.DTLVardef@5bbc66org.highwire.dtl.DTLVardef@adfae2_HPS_FORMAT_FIGEXP M_TBL C_TBL

genetics↗

bioRxiv2026

Genetic Compensation Restores Embryonic Viability in Fatty Acid Synthase Mutants

Fatty acid synthase (FASN) is a key rate-limited, dimeric multi-enzyme complex in the de novo lipogenesis pathway. Each FASN monomer contains seven catalytic domains, which coordinate the stepwise conversion of acetyl-CoA into fatty acids. While FASN has been extensively studied in cultured cells, particularly for its oncogenic role, its functions in the germline and early embryonic development remain elusive. A major challenge is that the FASN dysfunction typically causes embryonic lethality in animal models, which complicates detailed functional analysis and the identification of compensatory genetic interactors during development. To overcome this limitation and identify novel genetic suppressors of the FASN gene, we utilized a temperature-sensitive allele, fasn-1(g43ts) (A1424T), in the genetically tractable model Caenorhabditis elegans, to conduct unbiased forward genetic screens. We isolated 22 suppressor lines that significantly restored embryonic viability in the fasn-1(g43ts) mutant at the non-permissive temperature. Using a combination of MIP-MAP genomic mapping and a customized bioinformatic pipeline, we identified six missense mutations in the ptr-6 gene, which encodes a protein containing a patched domain associated with the Hedgehog signaling pathway. To validate this genetic suppression, we recreated one of the loss-of-function mutations, ptr-6 (W701*), in the fasn-1(g43ts) background using CRISPR/Cas9 gene editing. Notably, ptr-6(W701*) robustly rescued the embryonic lethality and permeability defects caused by fasn-1 loss-of-function. Taken together, our findings expand the genetic regulatory network of fatty acid synthase during early embryogenesis and highlight ptr-6 and Hedgehog signaling pathway as potential genetic modifiers of FASN-associated developmental and metabolic disorders.

genetics↗

bioRxiv2026

From Diversity to Discovery: Genome-Wide Insights into the Genetic Landscape of Tropical Maize DH Lines

Understanding the extent and structure of genetic diversity within breeding populations is essential for sustaining long-term genetic gain in maize improvement programs. In this study, a panel of 2,555 maize doubled haploid (DH) lines representing diverse genetic backgrounds was genotyped using 3305 high-quality single nucleotide polymorphism (SNP) markers to assess genome-wide diversity, population structure, and relatedness. The SNPs were distributed across all ten chromosomes, with varying marker densities among genomic regions. Diversity indices revealed moderate polymorphism, with mean gene diversity (0.38) and polymorphic information content (0.30), while the minor allele frequency ranged from 0.04 to 0.50. The low observed heterozygosity (0.04) and high fixation index (0.89) confirmed the expected homozygosity of DH lines. Population structure analysis using sparse non-negative matrix factorization (sNMF) and principal coordinate analysis (PCoA) consistently identified two major genetic clusters corresponding to the established heterotic groups used in CIMMYTs tropical maize breeding pipelines. The Analysis of Molecular Variance (AMOVA) indicated that 36% of genetic variation occurred among populations, 58% among individuals within populations, and 6% within individuals (P = 0.001), confirming significant population differentiation and high within-group diversity. These results demonstrate that the DH panel represents a genetically diverse and well-structured population with limited relatedness among lines. The distinct clustering by heterotic group, coupled with substantial within-group variation, provides a strong foundation for genome-wide association studies, genomic selection, and allele mining for complex adaptive traits. The panels diversity and structure make it an invaluable genomic resource for dissecting trait architecture and accelerating genetic gain in tropical maize breeding programs targeting sub-Saharan Africa and similar environments.

genetics↗

bioRxiv2026

The impact of serial translocations on the genetic diversity of Anegada iguanas (Cyclura pinguis) in the British Virgin Islands

Animal translocations are becoming increasingly popular as a tool for conservationists. Demographic factors can be crucial determinants dictating translocation viability in the short term. Translocated populations pass through artificial bottlenecks and can suffer from founder effects. Reduction in genetic variation relative to their source populations is likely, limiting their adaptive potential. Founder events can increase frequencies of deleterious alleles due to elevated rates of inbreeding and inbreeding depression. Here, we describe the effects of human-driven, serial population translocations on the genetic diversity of critically endangered Anegada iguanas (Cyclura pinguis) in the British Virgin Islands. Though founding populations were extremely small (N=8, N=4), the census sizes of translocated iguana populations increased dramatically over the first twenty years. This implies that these translocations were successful from a demographic perspective despite the small number of animals used, indicating a genetic paradox. To quantify genetic signatures in these bottlenecked populations, blood samples were collected from the source population and two translocated populations and genotyped at 21 microsatellite loci. We found that allele frequencies in translocated populations differed significantly from those of the source, with the translocated populations having less genetic diversity. However, common methods for estimating presence of genetic bottlenecks were non-significant. Estimates of internal relatedness by age class suggest that inbreeding depression may be elevated after translocation, likely reflecting the small initial population sizes associated with these translocation events. Anecdotally, our work shows that translocations may result in subtle genetic erosion that has long-term population viability impacts, even when census size indicates success.

genetics↗

bioRxiv2026

Identifying genetic regulations on immune cell type proportions and their impacts on autoimmune diseases

Genetic regulation of immune cell composition plays a crucial role in the etiology of complex diseases, yet remains poorly understood. We propose a unified analytical framework that integrates genome-wide association studies (GWAS) of cell type proportions with cell-type-wide association studies (cWAS) to systematically characterize both the genetic regulation of immune cell composition and its downstream effects on disease risk. Using single-cell RNA sequencing data from the OneK1K cohort, we conducted a GWAS of immune cell-type proportions with a depth-weighted quasi-binomial model designed for bounded, overdispersed traits. We identified 47 genome-wide significant loci influencing eight fine-labeled immune cell subtypes. Leveraging these identified genetic effects, we further imputed genetically regulated proportions (GRPs) using polygenic risk score (PRS)-based imputation and assessed their associations with complex diseases through cWAS. We identified five significant cell type-disease associations, including two with type 1 diabetes, two with Crohns disease, and one with ulcerative colitis. Together, our results demonstrate that cell type proportions observed in scRNA-seq can reveal regulatory loci and offer insights into how genetic variations regulate immune cell type proportions to affect disease risk. Although we focused on immune single-cell data, our framework is applicable to other tissues or cellular compositions as scRNA-seq datasets expand. Author SummaryGenome-wide association studies (GWASs) have uncovered many disease-associated signals, yet most lie in noncoding regions and are difficult to interpret. Mapping GWAS signals to the relevant cell types is therefore important for better understanding the biological mechanisms that drive disease. A major challenge is that observed gene expression and measured cell-type proportions can be influenced by environmental factors and disease status. In contrast, genotypes are less affected by these factors, making them more reliable for interpreting factors of diseases. Moreover, the cell-type proportions are bounded and often skewed, so standard GWAS models that rely on Gaussian assumptions may lose power. To address this, we developed a quasi-binomial approach that better matches the data and improves discovery while controlling false positives. In real data, our method identified more genetic loci associated with cell-type proportions than a traditional linear model. To further investigate how genetic variation regulates immune cell composition to influence disease risk, we integrated our results with disease GWAS summary statistics to identify immune cell types that may contribute to disease susceptibility. Together, our results link disease-associated GWAS signals to specific immune cell types and provide insights into the cellular mechanisms that may underlie these diseases.

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bioRxiv2026

Sex stratified analyses enable new genetic insights into brain imaging phenotypes

Sex differences are commonly observed in neuroimaging phenotypes and in the risk of brain diseases, yet the underlying genetic mechanisms remain poorly understood. We investigated sex differences in the genetic architecture of 805 neuroimaging phenotypes in 22,950 males and 22,950 females matched for sample size and covariates, and systematically compared sex-stratified with sex-combined genetic analyses. We found eight variant-trait associations with significant sex differences, 235 fine-mapped sex-dominant causal associations, 457 sex-dominant colocalizations with sex hormones, and 96 sex-dominant colocalizations with schizophrenia. Compared with sex-combined analysis, sex-stratified analysis identified 47 new genetic associations, 170 new fine-mapped causal associations, 1,019 new colocalizations with sex hormones, and 191 new colocalizations with schizophrenia. Additionally, sex-stratified analysis improved global heritability and genetic-correlation estimates and enhanced polygenic prediction for certain phenotypes. This work highlights the need to routinely perform sex-stratified genetic association analyses to elucidate sex-specific and sex-shared genetic control of neuroimaging phenotypes and related disorders.

genetics↗

bioRxiv2026

High concordance between genetic effects on mRNA and protein abundance

Genetic influences on gene expression are an important source of variation in complex traits. Conflicting results have been reported about the concordance of genetic effects on mRNA abundance vs. protein levels, ranging from high agreement to a predominance of effects that are specific to mRNA or protein. Here, we integrated 13 published datasets of genetic variation in mRNA or protein collected in the same cross of two strains of the yeast Saccharomyces cerevisiae. These highly replicated data allowed us to gauge the overall agreement between the genetics of mRNA and protein and search for individual loci whose effects on these two gene products are reproducibly different. Overall, genetic effects were highly correlated across all datasets. mRNA and protein showed similar genetic architectures. Pairwise agreement between loci from mRNA datasets and loci from protein datasets was indistinguishable from agreement between loci from datasets of the same gene product. Trans-acting hotspots with effects on numerous genes affected mRNA and protein similarly. There were no hotspots that exclusively affected mRNA or protein across datasets. A small number of loci did show reproducibly different effects on mRNA or protein of individual genes. Collectively, these results show that, with a few notable exceptions, genetic effects on mRNA and protein are largely concordant.

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bioRxiv2026

Multispecies Mixtures: An Individual-Centered Quantitative Genetic Framework for Complex Plant Neighborhoods

Mixing crop species or varieties in the same field can raise yield and stability, but performance varies widely among combinations that breeders cannot yet predict. What makes a good neighbor is partly heritable, and quantitative genetics models this as an indirect genetic effect describing a genotype's contribution to its neighbors' phenotypes. Here, we extend existing models with a complementary approach based on continuous neighborhoods, in which diverse genotypes of two species are mixed. In a continuous neighborhood design, several genotypes of each species are grown in a single trial, with every plant georeferenced. Each plant's phenotype is decomposed into direct genetic effects, indirect genetic effects from neighbors of both species, and environmental effects. Breeding values are then assembled for each genotype by combining its three genetic effects. We derived analytical expressions for the variances and covariance of these effects, validated the model, and evaluated its statistical properties through simulations spanning a wide range of parameter settings. For the same number of plants, the continuous neighborhood design estimated indirect effects far more accurately than a pairwise design and, in addition, allowed indirect environmental variance to be estimated. Applied to a wheat-alfalfa experiment comprising 3,840 plants, from 181 wheat pure lines and 106 alfalfa families, the model attributed 8.6% and 8.2% of the variance in wheat grain number and alfalfa biomass to direct genetic effects respectively, and 4.0% and 4.6% to indirect genetic effects from alfalfa. The framework therefore offers an individual-entered basis for analyzing multispecies neighborhoods and their breeding potential.

genetics↗

bioRxiv2026

Scaling Across Environments: Temperature and nutrition independently shape the genetics of size plasticity and morphological scaling

Across animals, variation in adult body size is accompanied by coordinated variation in the size of individual morphological traits. However, the same morphological trait can scale differently with body size depending on what drives the size variation. In Drosophila melanogaster, for example, wing size scales differently with body size when size varies because of developmental nutrition versus developmental temperature. Whether the genetic basis of size plasticity and scaling is shared across different environmental regulators of size remains unclear, but is central to predicting how selection acts on the developmental mechanisms that regulate trait size, plasticity and morphological scaling. Using ~200 isogenic D. melanogaster lineages, we measured wing and leg size across nutritional and thermal treatments. For each lineage, we estimated nutritional and thermal plasticity for both traits, as well as the wing-leg individual-level scaling relationship, or ILSR, generated by each environmental source of size variation. We found extensive genetic variation in both thermal and nutritional plasticity for wings and legs, and in the slope of the ILSR between them. However, a lineages thermal plasticity was genetically uncorrelated with its nutritional plasticity for either trait, and we detected no genetic correlation between the slopes of thermal and nutritional wing-leg ILSRs. We also found no genetic correlation in the slope of nutritional wing-leg ILSRs across temperatures. Thus, the slope of a lineages nutritional ILSR at 17{degrees}C was not predictive of its slope at 25{degrees}C of 28{degrees}C. Nevertheless, the overall pattern of nutritional ILSRs was conserved across temperatures. These results suggest that the genetic architecture of size plasticity and scaling depends on the environmental source of size variation. Consequently, the evolutionary response of scaling to selection in heterogeneous environments may not be predictable from genetic variation measured in any single environment.

genetics↗

bioRxiv2026

From Wikipedia to AI: Measuring 25 years of synthesis of human genetics research in the public-facing information ecosystem

Genetics research frequently intersects with ethnicity, nationality, and race, making it uniquely vulnerable to misrepresentation. Yet, 25 years after the initial sequencing of the human genome, there is little understanding of how human genetics research exists in the public-facing information ecosystem. We analyze 3,050,422 historical revisions from 6,738 Wikipedia pages about ethnicity, nationality, and race spanning 25 years. We find genetics terminology is present in 14.8% of these pages (55.5% in the top 1,000 pages) and in 67.8% of pages about nationalities, suggesting research is synthesized to present a biological element to ethnicity and nationality. We also find that 10.1% of 56,908 discussions from these pages contain genetics terminology. We further analyze responses from three popular chatbots queried about nationalities and find that they commonly reference both genetics and Wikipedia. Lastly, we analyze 133 pages from Grokipedia, an AI-generated encyclopedia, and find it mentions genetics more frequently than Wikipedia and hallucinates or misrepresents human genetics research.

genetics↗

bioRxiv2026

Genetic background diversifies mutational paths but converges on membrane depolarization for bleomycin resistance in yeast

Drug resistance evolution is shaped by both selective agent and genetic background in which resistant mutations arise, but most experimental evolution studies rely on a single genetic background. Here we performed parallel experimental evolution across six genetically diverged Saccharomyces cerevisiae strains, propagating 48 replicate populations for [~]225 generations under the selection of DNA-damaging chemotherapeutic bleomycin. Over 70% of the evolved populations acquired resistance with the magnitude and repeatability of adaptation varying markedly across genetic backgrounds. Whole-genome sequencing showed that bleomycin elevated single-nucleotide and insertion-deletion mutation rates, and induced background-specific aneuploidies and structural variants, including Ty-element-mediated translocations and tandem duplications. Notably, mutation rates varied up to 10-fold among backgrounds, with the most commonly-used lab strain exhibiting the highest mutagenic susceptibility, underscoring that evolutionary insights derived from single-background models may not universally generalize across a species. Despite this background-dependent genomic heterogeneity, we identified recurrent loss-of-function mutations in PMA1 and PTK2 that arose independently across nearly all genetic backgrounds and conferred strong resistance. Functional and biophysical assays revealed that these mutations impair Pma1p H+-ATPase activity and depolarize the plasma membrane, thereby limiting the cellular uptake of the positively charged drug. Our findings demonstrate that while the mutational routes to drug resistance are heavily shaped by genetic background, a convergent, background-independent mechanism centered on membrane potential regulation can dominate phenotypic adaptation. This work underscores the necessity of integrating genetic diversity into experimental evolution paradigm and nominates plasma membrane potential as an important lever for modulating chemotherapeutic efficacy.

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bioRxiv2026

TigerAI: An AI-powered genetic evidence platform to support clinical development

Genetic evidence is a major determinant of clinical success in drug development, yet its aggregation has long relied on laborious human curation. Large language models (LLMs) have the potential to rapidly synthesize knowledge across biomedical resources, providing a route to scalable AI-driven genetic evidence generation. Here we develop a novel domain-grounded instruction framework to systematically evaluate GPT-5 for producing genetic evidence relevant to clinical trial success. Using 13,022 target-indication pairs from a comprehensive drug development database, we benchmark LLM-derived evidence against a recent exhaustive human expert-curated study. We find that GPT-5 yields genetic evidence that is at least as informative as expert curation for inferring clinical success, while substantially expanding coverage relative to traditional curation resources. Building on these results, we introduce TigerAI (https://tigerai.bio/), a dual-purpose platform for AI-powered genetic evidence that (i) benchmarks emerging state-of-the-art LLMs and (ii) provides an accessible service for querying reliable AI-generated genetic evidence. These contributions outline a practical, domain-grounded pathway for integrating AI-powered genetic evidence into drug development pipelines and for realizing the potential of LLMs to inform clinical success.

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bioRxiv2026

Genomic SEM reveals shared genetic architecture and expands locus discovery for locomotor activity in heterogeneous stock rats

Locomotor response to a novel environment is a heritable, addiction-relevant behavioral phenotype in rodents. We characterized its genetic basis in 9,341 male and female heterogeneous stock (HS) rats from eight studies. Locomotor activity was heritable across studies, while pairwise genetic correlations were positive but variable, indicating shared yet nonidentical genetic influences. Given nonuniform genetic architecture, we compared three complementary approaches to locus discovery that analyzed SNP effects 1) on a pooled locomotor phenotype (MEGA), 2) across studies under a fixed-effect model (METAL), or 3) on a shared genetic factor (Genomic SEM). To ensure comparability, all three locus-discovery approaches were applied to a common subset of seven studies. Together, these approaches identified 38 loci, with both convergence and method-specific discovery across analyses. Genomic SEM identified 31 loci, compared to 8 for MEGA and 13 for METAL. To interpret the loci, we integrated brain molecular QTLs, predicted coding and splice consequences, and open-field locomotor phenotypes from knockouts of orthologous mouse genes. Our results highlighted established neurobehavioral candidates spanning locomotor and reward-related biology (Negr1 and Lmo4), serotonergic signaling (Htr1b), and GABAergic signaling (Gabrg2), as well as relatively novel genes including Tcerg1l, Pcdh10, and Tmem132e. Our results illustrate how Genomic SEM can integrate genetically correlated yet heterogeneous behavioral measures and enable discovery of loci underlying their shared genetic architecture, highlighting its utility for complex-trait studies in rodents.

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