Faye Lawrence

Bio: Faye Lawrence is an academic researcher from North Carolina State University. The author has contributed to research in topics: Allele & Population. The author has an hindex of 6, co-authored 6 publications receiving 2432 citations.

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.

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 .

Epistasis dominates the genetic architecture of Drosophila quantitative traits.

Abstract: Epistasis—nonlinear genetic interactions between polymorphic loci—is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agriculture.

Effects of single P-element insertions on bristle number and viability in Drosophila melanogaster.

Abstract: Single P-element mutagenesis was used to construct 1094 lines with P[lArB] inserts on all three major chromosomes in an isogenic background previously free of P elements. The effects of insertions on bristle number and on viability were assessed by comparison to 392 control lines. The variance and effects of P-element inserts on bristle number and viability were larger than those inferred from spontaneous mutations. The distributions of effects on bristle number were symmetrical and highly leptokurtic, such that a few inserts with large effects caused most of the increase in variance. The distribution of effects on viability were negatively skewed and platykurtic. On average, the effects of P-element insertions on bristle number were partly recessive and on viability were completely recessive. P-element inserts with large effects on bristle number tended to have reduced viability, but the correlation between the absolute value of the effects on bristle number and on viability was not strong. Fifty P-element inserts tagging quantitative trait loci (QTLs) with large effects on bristle number were mapped cytogenetically. Two P-element-induced scabrous alleles and five extramacrochaetae alleles were generated. Single P-element mutagenesis is a powerful method for identifying QTLs at the level of genetic locus.

Genetic Architecture of Abdominal Pigmentation in Drosophila melanogaster.

Abstract: Pigmentation varies within and between species and is often adaptive. The amount of pigmentation on the abdomen of Drosophila melanogaster is a relatively simple morphological trait, which serves as a model for mapping the genetic basis of variation in complex phenotypes. Here, we assessed natural variation in female abdominal pigmentation in 175 sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel, derived from the Raleigh, NC population. We quantified the proportion of melanization on the two most posterior abdominal segments, tergites 5 and 6 (T5, T6). We found significant genetic variation in the proportion of melanization and high broad-sense heritabilities for each tergite. Genome-wide association studies identified over 150 DNA variants associated with the proportion of melanization on T5 (84), T6 (34), and the difference between T5 and T6 (35). Several of the top variants associated with variation in pigmentation are in tan, ebony, and bric-a-brac1, genes known to affect D. melanogaster abdominal pigmentation. Mutational analyses and targeted RNAi-knockdown showed that 17 out of 28 (61%) novel candidate genes implicated by the genome-wide association study affected abdominal pigmentation. Several of these genes are involved in developmental and regulatory pathways, chitin production, cuticle structure, and vesicle formation and transport. These findings show that genetic variation may affect multiple steps in pathways involved in tergite development and melanization. Variation in these novel candidates may serve as targets for adaptive evolution and sexual selection in D. melanogaster.

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.