Biology subjects

Beeravolu Reddy, C.

Publications and source records attributed to Beeravolu Reddy, C..

2 recordsLinked to original sources

bioRxiv2026

Nemo2.4: fast and accurate quantitative genetics forward-time simulations

We present Nemo 2.4, an advanced forward-time individual-based simulation framework designed to model the complex eco-evolutionary dynamics and genetic basis of quantitative traits. This tool addresses current challenges in evolutionary quantitative genetics by providing unprecedented flexibility and computational efficiency. Nemo 2.4s modular architecture allows researchers to design custom life cycles by combining specialized Life Cycle Event (LCE) modules, from reproduction and dispersal to selection, crossing, and phenotype expression. The software supports diverse population models, including both Wright-Fisher (WF) and non-WF dynamics, spatially explicit models, and varying demography. Nemo 2.4 handles a wide range of genetic architectures, including both multi-allelic Quantitative Trait Loci (QTL) for general trait studies, and dense di-allelic Quantitative Trait Nucleotides (QTN) implemented with highly optimized bit-wise data structures. Crucially, it allows the simulation of QTNs on comprehensive genetic maps that incorporate other genetic elements, providing genomic-scale resolution. Key biological complexities are integrated natively: the model accommodates modular pleiotropy, dominance, and pairwise epistasis across multiple traits, facilitating the study of complex genotype-phenotype mappings. Furthermore, Nemo 2.4 models phenotypic plasticity through reaction norms and incorporates underlying liability thresholds, enabling the simulation of environmental influences on trait evolution with various forms of selection (e.g., Gaussian, linear, truncation). Due to its compiled design and memory-efficient data representations for large numbers of loci, Nemo provides a robust platform for running high-throughput simulations critical for testing theoretical predictions in polygenic adaptation and understanding evolutionary responses to changing environments.

evolutionary biology↗

bioRxiv2024

Gene expression evolution is predicted by selection, genetic covariance and network topology

Changes in gene expression play a fundamental role in the process of adaptation and can provide insight into the genetic basis of adaptation. We utilized transcriptome-wide variation in gene expression as a means to uncover genes under selection for expression changes during adaptation to heat and drought stress, and to understand the nature of selection on gene expression traits of the red flour beetle Tribolium castaneum. We showed that estimates of genetic selection on transcript abundance were predictive of evolutionary changes in gene expression after 20 generations of adaptation in seven independent experimental lines. Having measured the genetic covariance between gene expression and relative fitness and among expression traits, we showed that evolutionary changes were driven more by indirect selection acting on genetically correlated partners rather than by direct selection acting on isolated genes. Consequently, genes with central positions in gene co-expression networks experienced stronger selection and exhibited larger evolutionary changes in expression. Our genomic analysis revealed that selection on expression levels is associated with parallel allele frequency changes (AFCs) in the respective genes. More pleiotropic genes and those carrying expression quantitative trait loci (eQTLs) showed a higher degree of parallel evolution. More generally, the stronger the parallelism of AFCs in a gene, the stronger its genetic selection. Contrary to previous evidence of constrained evolution at more connected genes, adaptation was driven by selection acting disproportionately on genes central to co-expression gene networks. We demonstrated that measures of selection at the transcriptome level can provide accurate evolutionary predictions and critical information on the molecular basis of rapid adaptation.

evolutionary biology↗