scholarly journals Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila

2020 ◽  
Author(s):  
Adam N. Spierer ◽  
Jim A. Mossman ◽  
Samuel Pattillo Smith ◽  
Lorin Crawford ◽  
Sohini Ramachandran ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Muscle Development ◽  
Bmp Signaling ◽  
Interaction Networks ◽  
Reference Panel ◽  
Regulation Of Transcription ◽  
Flight Performance ◽  
Genetic Modifiers ◽  
Epistatic Interactions

AbstractThe winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, like dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of variation for flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted an association study using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. We functionally validated 13 of these candidate genes’ (Adgf-A/Adgf-A2/CG32181, bru1, CadN, CG11073, CG15236, CG9766, CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo) contribution to flight, two of which (fry and Snoo) also validate a whole gene analysis we introduce for the DGRP: PEGASUS_flies. Overall, our results suggest modifiers of muscle and wing morphology, and peripheral and central nervous system assembly and function are all important for flight performance. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. These results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects. It also draws connections between genetic modifiers of performance and BMP signaling and ASICs as targets for treating neurodegeneration and neurodysfunction.Author summaryInsect flight is a widely recognizable phenotype of winged insects, hence the name: flies. While fruit flies, or Drosophila melanogaster, are a genetically tractable model, flight performance is a highly integrative phenotype, making it challenging to comprehensively identify the genetic modifiers that contribute to its genetic architecture. Accordingly, we screened 197 Drosophila Genetic Reference Panel lines for their ability to react and respond to an abrupt drop. Using several computational tools, we successfully identified several additive, marginal, and epistatic variants, as well as whole genes and altered sub-networks of gene-gene and protein-protein interaction networks, demonstrating the benefits of using multiple methodologies to elucidate the genetic architecture of complex traits more generally. Many of these significant genes and variants mapped to regions of the genome that affect development of sensory and motor neurons, wing and muscle development, and regulation of transcription factors. We also introduce PEGASUS_flies, a Drosophila-adapted version of the PEGASUS platform first used in human studies, to infer gene-level significance of association based on the distribution of individual variant P-values. Our results contribute to the debate over the relative importance of individual, additive factors and epistatic, or higher order, interactions, in the mapping of genotype to phenotype.

scholarly journals Natural variation in the regulation of neurodevelopmental genes modifies flight performance in Drosophila

PLoS Genetics ◽  
2021 ◽  
Vol 17 (3) ◽  
pp. e1008887
Author(s):  
Adam N. Spierer ◽  
Jim A. Mossman ◽  
Samuel Pattillo Smith ◽  
Lorin Crawford ◽  
Sohini Ramachandran ◽  
...  
Keyword(s):  
Genome Wide Association Study ◽  
System Development ◽  
Nervous System Development ◽  
Bmp Signaling ◽  
Flight Performance ◽  
Genetic Modifiers ◽  
Epistatic Interactions ◽  
Novel Approach ◽  
A Genome ◽  
And Function

The winged insects of the order Diptera are colloquially named for their most recognizable phenotype: flight. These insects rely on flight for a number of important life history traits, such as dispersal, foraging, and courtship. Despite the importance of flight, relatively little is known about the genetic architecture of flight performance. Accordingly, we sought to uncover the genetic modifiers of flight using a measure of flies’ reaction and response to an abrupt drop in a vertical flight column. We conducted a genome wide association study (GWAS) using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, and identified a combination of additive and marginal variants, epistatic interactions, whole genes, and enrichment across interaction networks. Egfr, a highly pleiotropic developmental gene, was among the most significant additive variants identified. We functionally validated 13 of the additive candidate genes’ (Adgf-A/Adgf-A2/CG32181, bru1, CadN, flapper (CG11073), CG15236, flippy (CG9766), CREG, Dscam4, form3, fry, Lasp/CG9692, Pde6, Snoo), and introduce a novel approach to whole gene significance screens: PEGASUS_flies. Additionally, we identified ppk23, an Acid Sensing Ion Channel (ASIC) homolog, as an important hub for epistatic interactions. We propose a model that suggests genetic modifiers of wing and muscle morphology, nervous system development and function, BMP signaling, sexually dimorphic neural wiring, and gene regulation are all important for the observed differences flight performance in a natural population. Additionally, these results represent a snapshot of the genetic modifiers affecting drop-response flight performance in Drosophila, with implications for other insects.

scholarly journals Proprioceptive Genes as a Source of Genetic Variation Underlying Robustness for Flight Performance in Drosophila

2021 ◽  
Author(s):  
Adam N Spierer ◽  
David M. Rand
Keyword(s):  
Phenotypic Variation ◽  
Flight Performance ◽  
Nucleotide Polymorphisms ◽  
Genetic Modifiers ◽  
Single Nucleotide ◽  
Epistatic Interactions ◽  
Genetic Components ◽  
Trait Variance ◽  
External Stimuli ◽  
Drosophila Genetic Reference Panel

A central challenge of quantitative genetics is partitioning phenotypic variation into genetic and non-genetic components. These non-genetic components are usually interpreted as environmental effects; however, variation between genetically identical individuals in a common environment can still exhibit phenotypic variation. A trait's resistance to variation is called robustness, though the genetics underlying it are poorly understood. Accordingly, we performed an association study on a previously studied, whole organism trait: robustness for flight performance. Using 197 of the Drosophila Genetic Reference Panel (DGRP) lines, we surveyed variation across single nucleotide polymorphisms, whole genes, and epistatic interactions to find genetic modifiers robustness for flight performance. There was an abundance of genes involved in the development of sensory organs and processing of external stimuli, supporting previous work that processing proprioceptive cues is important for affecting variation in flight performance. Additionally, we tested insertional mutants for their effect on robustness using candidate genes found to modify flight performance. These results suggest several genes involved in modulating a trait mean are also important for affecting trait variance, or robustness, as well.

scholarly journals Epistatic interactions attenuate mutations affecting startle behaviour in Drosophila melanogaster

2009 ◽  
Vol 91 (6) ◽  
pp. 373-382 ◽  
Author(s):  
AKIHIKO YAMAMOTO ◽  
ROBERT R. H. ANHOLT ◽  
TRUDY F. C. MACKAY
Keyword(s):  
Drosophila Melanogaster ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Empirical Support ◽  
Genetic Network ◽  
Stabilizing Selection ◽  
Evolutionary Potential ◽  
Epistatic Interactions ◽  
Main Effects

SummaryEpistasis is an important feature of the genetic architecture of quantitative traits. Previously, we showed that startle-induced locomotor behaviour of Drosophila melanogaster, a critical survival trait, is highly polygenic and exhibits epistasis. Here, we characterize epistatic interactions among homozygous P-element mutations affecting startle-induced locomotion in the Canton-S isogenic background and in 21 wild-derived inbred genetic backgrounds. We find pervasive epistasis for pairwise combinations of homozygous P-element insertional mutations as well as for mutations in wild-derived backgrounds. In all cases, the direction of the epistatic effects is to suppress the mutant phenotypes. The magnitude of the epistatic interactions in wild-derived backgrounds is highly correlated with the magnitude of the main effects of mutations, leading to phenotypic robustness of the startle response in the face of deleterious mutations. There is variation in the magnitude of epistasis among the wild-derived genetic backgrounds, indicating evolutionary potential for enhancing or suppressing effects of single mutations. These results provide a partial glimpse of the complex genetic network underlying the genetic architecture of startle behaviour and provide empirical support for the hypothesis that suppressing epistasis is the mechanism underlying genetic canalization of traits under strong stabilizing selection. Widespread suppressing epistasis will lead to underestimates of the main effects of quantitative trait loci (QTLs) in mapping experiments when not explicitly accounted for. In addition, suppressing epistasis could lead to underestimates of mutational variation for quantitative traits and overestimates of the strength of stabilizing selection, which has implications for maintenance of variation of complex traits by mutation–selection balance.

scholarly journals Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
Author(s):  
M. E. Goddard ◽  
K. E. Kemper ◽  
I. M. MacLeod ◽  
A. J. Chamberlain ◽  
B. J. Hayes
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Quantitative Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Crop Breeding ◽  
Single Nucleotide ◽  
Genome Wide ◽  
Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

scholarly journals RIL-StEp: epistasis analysis of rice recombinant inbred lines (RILs) reveals candidate interacting genes that control seed hull color and leaf chlorophyll content

2021 ◽  
Author(s):  
Toshiyuki Sakai ◽  
Akira Abe ◽  
Motoki Shimizu ◽  
Ryohei Terauchi
Keyword(s):  
Chlorophyll Content ◽  
Recombinant Inbred ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Gene Interactions ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Seed Hull

Abstract Characterizing epistatic gene interactions is fundamental for understanding the genetic architecture of complex traits. However, due to the large number of potential gene combinations, detecting epistatic gene interactions is computationally demanding. A simple, easy-to-perform method for sensitive detection of epistasis is required. Due to their homozygous nature, use of recombinant inbred lines (RILs) excludes the dominance effect of alleles and interactions involving heterozygous genotypes, thereby allowing detection of epistasis in a simple and interpretable model. Here, we present an approach called RIL-StEp (recombinant inbred lines stepwise epistasis detection) to detect epistasis using single nucleotide polymorphisms in the genome. We applied the method to reveal epistasis affecting rice (Oryza sativa) seed hull color and leaf chlorophyll content and successfully identified pairs of genomic regions that presumably control these phenotypes. This method has the potential to improve our understanding of the genetic architecture of various traits of crops and other organisms.

BMP4 and WNT signaling interact to promote mouse tracheal mesenchyme morphogenesis

2021 ◽  
Author(s):  
Natalia Bottasso Arias ◽  
Lauren Leesman ◽  
Kaulini Burra ◽  
John Snowball ◽  
Ronak M Shah ◽  
...  
Keyword(s):  
Smooth Muscle ◽  
Wnt Signaling ◽  
Rna Sequencing ◽  
Muscle Development ◽  
Target Genes ◽  
Bmp Signaling ◽  
Embryonic Mouse ◽  
Tracheal Cartilage ◽  
Cartilage Formation

Tracheobronchomalacia and Complete Tracheal Rings are congenital malformations of the trachea associated with morbidity and mortality for which the etiology remains poorly understood. Epithelial expression of Wls (a cargo receptor mediating Wnt ligand secretion) by tracheal cells is essential for patterning the embryonic mouse trachea's cartilage and muscle. RNA sequencing indicated that Wls differentially modulated the expression of BMP signaling molecules. We tested whether BMP signaling, induced by epithelial Wnt ligands, mediates cartilage formation. Deletion of Bmp4 from respiratory tract mesenchyme impaired tracheal cartilage formation that was replaced by ectopic smooth muscle, recapitulating the phenotype observed after epithelial deletion of Wls in the embryonic trachea. Ectopic muscle was caused in part by anomalous differentiation and proliferation of smooth muscle progenitors rather than tracheal cartilage progenitors. Mesenchymal deletion of Bmp4 impaired expression of Wnt/β-catenin target genes, including targets of WNTsignaling: Notum, and Axin2. In vitro, rBMP4 rescued the expression of Notum in Bmp4 deficient tracheal mesenchymal cells and induced Notum promoter activity via SMAD1/5. RNA sequencing of Bmp4 deficient tracheas identified genes essential for chondrogenesis and muscle development co-regulated by BMP and WNT signaling. During tracheal morphogenesis, WNT signaling induces Bmp4 in mesenchymal progenitors to promote cartilage differentiation and restrict trachealis muscle. In turn, Bmp4 differentially regulates the expression of Wnt/β-catenin targets to attenuate mesenchymal WNT signaling and to further support chondrogenesis.

scholarly journals Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

2018 ◽  
Author(s):  
Doug Speed ◽  
David J Balding
Keyword(s):  
Complex Traits ◽  
Genetic Architecture ◽  
Large Scale ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Confounding Bias ◽  
Conserved Regions ◽  
Genome Wide ◽  
Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

scholarly journals On Using Local Ancestry to Characterize the Genetic Architecture of Human Phenotypes: Genetic Regulation of Gene Expression in Multiethnic or Admixed Populations as a Model

2018 ◽  
Author(s):  
Yizhen Zhong ◽  
Minoli Perera ◽  
Eric R. Gamazon
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Genetic Regulation ◽  
Regulation Of Gene Expression ◽  
Type I ◽  
Eqtl Mapping ◽  
Entire Genome ◽  
Local Ancestry ◽  
Heritability Estimation

AbstractBackgroundUnderstanding the nature of the genetic regulation of gene expression promises to advance our understanding of the genetic basis of disease. However, the methodological impact of use of local ancestry on high-dimensional omics analyses, including most prominently expression quantitative trait loci (eQTL) mapping and trait heritability estimation, in admixed populations remains critically underexplored.ResultsHere we develop a statistical framework that characterizes the relationships among the determinants of the genetic architecture of an important class of molecular traits. We estimate the trait variance explained by ancestry using local admixture relatedness between individuals. Using National Institute of General Medical Sciences (NIGMS) and Genotype-Tissue Expression (GTEx) datasets, we show that use of local ancestry can substantially improve eQTL mapping and heritability estimation and characterize the sparse versus polygenic component of gene expression in admixed and multiethnic populations respectively. Using simulations of diverse genetic architectures to estimate trait heritability and the level of confounding, we show improved accuracy given individual-level data and evaluate a summary statistics based approach. Furthermore, we provide a computationally efficient approach to local ancestry analysis in eQTL mapping while increasing control of type I and type II error over traditional approaches.ConclusionOur study has important methodological implications on genetic analysis of omics traits across a range of genomic contexts, from a single variant to a prioritized region to the entire genome. Our findings highlight the importance of using local ancestry to better characterize the heritability of complex traits and to more accurately map genetic associations.

scholarly journals TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

2018 ◽  
Author(s):  
Sini Nagpal ◽  
Xiaoran Meng ◽  
Michael P. Epstein ◽  
Lam C. Tsoi ◽  
Matthew Patrick ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Bayesian Model ◽  
Genetic Architecture ◽  
Bayesian Method ◽  
Association Studies ◽  
Gwas Data ◽  
Nonparametric Bayesian ◽  
Transcriptomic Data ◽  
Special Cases

AbstractThe transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool “TIGAR” (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

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