From GWAS to signal validation: An approach for estimating genetic effects while preserving genomic context

Validating associations between genotypic and phenotypic variation remains a challenge, despite advancements in association studies. Common approaches for signal validation rely on gene-level perturbations, such as loss-of-function mutations or RNAi, which test the effect of genetic modifications usually not observed in nature. CRISPR-based methods can validate associations at the SNP level, but have significant drawbacks, including resulting off-target effects and being both time-consuming and expensive. Both approaches usually modify the genome of a single genetic background, limiting the generalizability of experiments. To address these challenges, we present a simple, low-cost experimental scheme for validating genetic associations at the SNP level in outbred populations. The approach involves genotyping live outbred individuals at a focal SNP, crossing homozygous individuals with the same genotype at that locus, and contrasting phenotypes across resulting synthetic outbred populations. We tested this method in Drosophila melanogaster, measuring the longevity effects of a polymorphism at a naturally-segregating cis-eQTL for the midway gene. Our results demonstrate the utility of this method in SNP-level validation of naturally occurring genetic variation regulating complex traits. This method provides a bridge between the statistical discovery of genotype-phenotype associations and their validation in the natural context of heterogeneous genomic contexts.

[1]  Evan M. Cofer,et al.  Saturating the eQTL map in Drosophila melanogaster: genome-wide patterns of cis and trans regulation of transcriptional variation in outbred populations , 2023, bioRxiv.

[2]  Luisa F. Pallares,et al.  Dietary stress remodels the genetic architecture of lifespan variation in outbred Drosophila , 2022, Nature Genetics.

[3]  J. Lazar,et al.  The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases , 2022, BMC medical genomics.

[4]  Jonas Reeb,et al.  The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases , 2022, BMC Medical Genomics.

[5]  L. Keller,et al.  A Single Nucleotide Variant in the PPARγ-homolog Eip75B Affects Fecundity in Drosophila , 2021, bioRxiv.

[6]  A. Shevchenko,et al.  Abnormal accumulation of lipid droplets in neurons induces the conversion of alpha-Synuclein to proteolytic resistant forms in a Drosophila model of Parkinson’s disease , 2021, PLoS genetics.

[7]  T. Mackay,et al.  Systems Genetics of Single Nucleotide Polymorphisms at the Drosophila Obp56h Locus , 2021, bioRxiv.

[8]  Bassem A. Hassan,et al.  Altering the Temporal Regulation of One Transcription Factor Drives Evolutionary Trade-Offs between Head Sensory Organs. , 2019, Developmental cell.

[9]  E. Chesler,et al.  High-Diversity Mouse Populations for Complex Traits. , 2019, Trends in genetics : TIG.

[10]  Michael F. Wangler,et al.  The fruit fly at the interface of diagnosis and pathogenic mechanisms of rare and common human diseases. , 2019, Human molecular genetics.

[11]  A. M. Zimmer,et al.  Loss-of-function approaches in comparative physiology: is there a future for knockdown experiments in the era of genome editing? , 2019, Journal of Experimental Biology.

[12]  A. Chen-Plotkin,et al.  The Post-GWAS Era: From Association to Function. , 2018, American journal of human genetics.

[13]  Jean-Claude Tardif,et al.  Human genetic variation alters CRISPR-Cas9 on- and off-targeting specificity at therapeutically implicated loci , 2017, Proceedings of the National Academy of Sciences.

[14]  P. Visscher,et al.  10 Years of GWAS Discovery: Biology, Function, and Translation. , 2017, American journal of human genetics.

[15]  Didier Y. R. Stainier,et al.  Genetic compensation: A phenomenon in search of mechanisms , 2017, PLoS genetics.

[16]  A. Bassuk,et al.  Unexpected mutations after CRISPR–Cas9 editing in vivo , 2017, Nature Methods.

[17]  N. Perrimon,et al.  Loss-of-function genetic tools for animal models: cross-species and cross-platform differences , 2016, Nature Reviews Genetics.

[18]  S. Austad,et al.  Sex Differences in Lifespan. , 2016, Cell metabolism.

[19]  D. Auld,et al.  A Class of Diacylglycerol Acyltransferase 1 Inhibitors Identified by a Combination of Phenotypic High-throughput Screening, Genomics, and Genetics , 2016, EBioMedicine.

[20]  M. de Castro,et al.  A Single Nucleotide Variant in the Promoter Region of 17β-HSD Type 5 Gene Influences External Genitalia Virilization in Females with 21-Hydroxylase Deficiency , 2016, Hormone Research in Paediatrics.

[21]  A. Clark,et al.  Global Diversity Lines–A Five-Continent Reference Panel of Sequenced Drosophila melanogaster Strains , 2015, G3: Genes, Genomes, Genetics.

[22]  Ian Dworkin,et al.  Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution. , 2013, Trends in genetics : TIG.

[23]  Robert V Farese,et al.  Deficiency of the lipid synthesis enzyme, DGAT1, extends longevity in mice , 2012, Aging.

[24]  Heng Li,et al.  A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data , 2011, Bioinform..

[25]  M. Wenk,et al.  Tissue-Autonomous Function of Drosophila Seipin in Preventing Ectopic Lipid Droplet Formation , 2011, PLoS Genetics.

[26]  L. Cooley,et al.  Mutations in the midway gene disrupt a Drosophila acyl coenzyme A: diacylglycerol acyltransferase. , 2002, Genetics.

[27]  E. Wieschaus,et al.  Female sterile mutations on the second chromosome of Drosophila melanogaster. II. Mutations blocking oogenesis or altering egg morphology. , 1991, Genetics.

[28]  Skipper Seabold,et al.  Statsmodels: Econometric and Statistical Modeling with Python , 2010, SciPy.

[29]  E. Wieschaus,et al.  Female sterile mutations on the second chromosome of Drosophila melanogaster. I. Maternal effect mutations. , 1989, Genetics.

[30]  D.,et al.  Regression Models and Life-Tables , 2022 .