Daniel I Bolnick | Search PubMed

PubMed2025

Rapid genome-wide introgression reveals fitness advantage of immigrant genotypes.

Evolutionary biology has long recognized the tendency for populations to be locally adapted to their ancestral habitat, resulting in higher resident fitness. However, immigrants can also introduce beneficial alleles. The resulting adaptive introgression is usually inferred retrospectively, rather than as a contemporary process. Here, we document exceptionally rapid ongoing adaptive introgression in a lake population of threespine stickleback (Gasterosteus aculeatus). In the first generations after a discrete immigration event, all chromosomes exhibited large increases in immigrant ancestry due to linkage disequilibrium. After a decade, the extent of introgression varied across the genome. The fastest-evolving genes included Spi1b, which enables an increased fibrosis defense against a previously common tapeworm, whose prevalence then declined dramatically. This case study highlights the capacity for immigration to supply beneficial alleles that drive rapid genome-wide evolution.

Journal Article↗

PubMed2025

A spectral framework to map QTLs affecting joint differential networks of gene co-expression.

Studying the mechanisms underlying the genotype-phenotype association is crucial in genetics. Gene expression studies have deepened our understanding of the genotype  →  expression  →  phenotype mechanisms. However, traditional expression quantitative trait loci (eQTL) methods often overlook the critical role of gene co-expression networks in translating genotype into phenotype. This gap highlights the need for more powerful statistical methods to analyze genotype  →  network  →  phenotype mechanism. Here, we develop a network-based method, called spectral network quantitative trait loci analysis (snQTL), to map quantitative trait loci affecting gene co-expression networks. Our approach tests the association between genotypes and joint differential networks of gene co-expression via a tensor-based spectral statistics, thereby overcoming the ubiquitous multiple testing challenges in existing methods. We demonstrate the effectiveness of snQTL in the analysis of three-spined stickleback (Gasterosteus aculeatus) data. Compared to conventional methods, our method snQTL uncovers chromosomal regions affecting gene co-expression networks, including one strong candidate gene that would have been missed by traditional eQTL analyses. Our framework suggests the limitation of current approaches and offers a powerful network-based tool for functional loci discoveries.

Quantitative Trait Loci↗