Stephanie M. Rollmann

Bio: Stephanie M. Rollmann is an academic researcher from University of Cincinnati. The author has contributed to research in topics: Population & Olfaction. The author has an hindex of 17, co-authored 37 publications receiving 3747 citations. Previous affiliations of Stephanie M. Rollmann include University of Chicago & Oregon State University.

The Drosophila melanogaster Genetic Reference Panel

Abstract: A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.

Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines

Abstract: The Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource of 205 sequenced inbred lines, derived to improve our understanding of the effects of naturally occurring genetic variation on molecular and organismal phenotypes. We used an integrated genotyping strategy to identify 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants. Our molecular population genomic analyses show higher deletion than insertion mutation rates and stronger purifying selection on deletions. Weaker selection on insertions than deletions is consistent with our observed distribution of genome size determined by flow cytometry, which is skewed toward larger genomes. Insertion/deletion and single nucleotide polymorphisms are positively correlated with each other and with local recombination, suggesting that their nonrandom distributions are due to hitchhiking and background selection. Our cytogenetic analysis identified 16 polymorphic inversions in the DGRP. Common inverted and standard karyotypes are genetically divergent and account for most of the variation in relatedness among the DGRP lines. Intriguingly, variation in genome size and many quantitative traits are significantly associated with inversions. Approximately 50% of the DGRP lines are infected with Wolbachia, and four lines have germline insertions of Wolbachia sequences, but effects of Wolbachia infection on quantitative traits are rarely significant. The DGRP complements ongoing efforts to functionally annotate the Drosophila genome. Indeed, 15% of all D. melanogaster genes segregate for potentially damaged proteins in the DGRP, and genome-wide analyses of quantitative traits identify novel candidate genes. The DGRP lines, sequence data, genotypes, quality scores, phenotypes, and analysis and visualization tools are publicly available.

Systems genetics of complex traits in Drosophila melanogaster.

Abstract: Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits. Natural populations harbor a wide range of phenotypic variation for all aspects of morphology, physiology, behaviors and disease susceptibility. Knowledge of the genetic basis of this variation is important for understanding adaptive evolution, deriving elite domestic crop and animal strains and improving human health. However, determining the genetic architecture of natural phenotypic variation is challenging because most phenotypic variation is attributable to segregating alleles at many interacting genes with environmentally sensitive effects 1,2 .

Proteinaceous pheromone affecting female receptivity in a terrestrial salamander.

Abstract: A 22-kilodalton protein was isolated from the submandibular (mental) gland of the male terrestrial salamander, Plethodon jordani (family: Plethodontidae). This proteinaceous pheromone, termed plethodontid receptivity factor (PRF), was experimentally delivered to the female during courtship and shown to increase female receptivity. In most plethodontid salamanders, ovulation occurs weeks or months after insemination, so the pheromone-induced change in receptivity is the only known function of PRF. The messenger RNAs corresponding to isoforms of PRF were transcribed into complementary DNA, cloned, sequenced, and shown to have homology with cytokines of the interleukin-6 family. Pheromone activity would represent a previously unrecognized function for cytokines.

Quantitative genomics of aggressive behavior in Drosophila melanogaster.

Abstract: Aggressive behavior is important for animal survival and reproduction, and excessive aggression is an enormous social and economic burden for human society. Although the role of biogenic amines in modulating aggressive behavior is well characterized, other genetic mechanisms affecting this complex behavior remain elusive. Here, we developed an assay to rapidly quantify aggressive behavior in Drosophila melanogaster, and generated replicate selection lines with divergent levels of aggression. The realized heritability of aggressive behavior was approximately 0.10, and the phenotypic response to selection specifically affected aggression. We used whole-genome expression analysis to identify 1,539 probe sets with different expression levels between the selection lines when pooled across replicates, at a false discovery rate of 0.001. We quantified the aggressive behavior of 19 mutations in candidate genes that were generated in a common co-isogenic background, and identified 15 novel genes affecting aggressive behavior. Expression profiling of genetically divergent lines is an effective strategy for identifying genes affecting complex traits. Citation: Edwards AC, Rollmann SM, Morgan TJ, Mackay TFC (2006) Quantitative genomics of aggressive behavior in Drosophila melanogaster. PLoS Genet 2(9): e154. DOI: 10. 1371/journal.pgen.0020154

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Double Digest RADseq: An Inexpensive Method for De Novo SNP Discovery and Genotyping in Model and Non-Model Species

Abstract: The ability to efficiently and accurately determine genotypes is a keystone technology in modern genetics, crucial to studies ranging from clinical diagnostics, to genotype-phenotype association, to reconstruction of ancestry and the detection of selection. To date, high capacity, low cost genotyping has been largely achieved via ‘‘SNP chip’’ microarray-based platforms which require substantial prior knowledge of both genome sequence and variability, and once designed are suitable only for those targeted variable nucleotide sites. This method introduces substantial ascertainment bias and inherently precludes detection of rare or population-specific variants, a major source of information for both population history and genotypephenotype association. Recent developments in reduced-representation genome sequencing experiments on massively parallel sequencers (commonly referred to as RAD-tag or RADseq) have brought direct sequencing to the problem of population genotyping, but increased cost and procedural and analytical complexity have limited their widespread adoption. Here, we describe a complete laboratory protocol, including a custom combinatorial indexing method, and accompanying software tools to facilitate genotyping across large numbers (hundreds or more) of individuals for a range of markers (hundreds to hundreds of thousands). Our method requires no prior genomic knowledge and achieves per-site and per-individual costs below that of current SNP chip technology, while requiring similar hands-on time investment, comparable amounts of input DNA, and downstream analysis times on the order of hours. Finally, we provide empirical results from the application of this method to both genotyping in a laboratory cross and in wild populations. Because of its flexibility, this modified RADseq approach promises to be applicable to a diversity of biological questions in a wide range of organisms.