scholarly journals Structural variants and selective sweep foci contribute to insecticide resistance in the Drosophila melanogaster genetic reference panel

2018 ◽  
Author(s):  
Paul Battlay ◽  
Llewellyn Green ◽  
Pontus B. Leblanc ◽  
Joshua M. Schmidt ◽  
Alexandre Fournier-Level ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Transposable Element ◽  
Selective Sweep ◽  
Association Studies ◽  
Transcript Abundance ◽  
Reference Panel ◽  
Drosophila Genome ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Number Variation

AbstractPatterns of nucleotide polymorphism within populations of Drosophila melanogaster suggest that insecticides have been the selective agents driving the strongest recent bouts of positive selection. However, there is a need to explicitly link selective sweep loci to the particular insecticide phenotypes that could plausibly account for the drastic selective responses that are observed in these non-target insects. Here, we screen the Drosophila Genetic Reference Panel with two common insecticides; malathion (an organophosphate) and permethrin (a pyrethroid). Genome wide association studies map ‘survival-on-malathion’ to two of the largest sweeps in the D. melanogaster genome; Ace and Cyp6g1. Malathion survivorship also correlates with lines which have high levels of Cyp12d1 and Jheh1 and Jheh2 transcript abundance. Permethrin phenotypes map to the largest cluster of P450 genes in the Drosophila genome, however in contrast to a selective sweep driven by insecticide use, the derived state seems to be associated with susceptibility. These results underscore previous findings that highlight the importance of structural variation to insecticide phenotypes: Cyp6g1 exhibits copy number variation and transposable element insertions, Cyp12d1 is tandemly duplicated, the Jheh loci are associated with a Bari1 transposable element insertion, and a Cyp6a17 deletion is associated with susceptibility.

scholarly journals Role of Cytokines in Systemic Lupus Erythematosus: Recent Progress from GWAS and Sequencing

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
John J. Connolly ◽  
Hakon Hakonarson
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Association Studies ◽  
Monozygotic Twins ◽  
Autoimmune Disorder ◽  
Genome Wide Association Studies ◽  
Systemic Lupus ◽  
Genome Wide ◽  
Number Variation

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder, known to have a strong genetic component. Concordance between monozygotic twins is approximately 30–40%, which is 8–20 times higher than that of dizygotic twins. In the last decade, genome-wide approaches to understanding SLE have yielded many candidate genes, which are important to understanding the pathophysiology of the disease and potential targets for pharmaceutical intervention. In this paper, we focus on the role of cytokines and examine how genome-wide association studies, copy number variation studies, and next-generation sequencing are being employed to understand the etiology of SLE. Prominent genes identified by these approaches includeBLK, FCγR3B,andTREX1. Our goal is to present a brief overview of genomic approaches to SLE and to introduce some of the key discussion points pertinent to the field.

scholarly journals Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

2017 ◽  
Author(s):  
Dominic Holland ◽  
Oleksandr Frei ◽  
Rahul Desikan ◽  
Chun-Chieh Fan ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Association Studies ◽  
Causal Snps ◽  
Reference Panel ◽  
Causal Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Common Variants ◽  
Genome Wide ◽  
Causal Variants

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

scholarly journals The power of a multivariate approach to genome-wide association studies: an example with Drosophila melanogaster wing shape

2017 ◽  
Author(s):  
William Pitchers ◽  
Jessica Nye ◽  
Eladio J. Márquez ◽  
Alycia Kowalski ◽  
Ian Dworkin ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Multivariate Analyses ◽  
Association Studies ◽  
Significant Snps ◽  
Wing Shape ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Phenotypic Effect ◽  
Genome Wide ◽  
Univariate Analyses

AbstractDue to the complexity of genotype-phenotype relationships, simultaneous analyses of genomic associations with multiple traits will be more powerful and more informative than a series of univariate analyses. In most cases, however, studies of genotype-phenotype relationships have analyzed only one trait at a time, even as the rapid advances in molecular tools have expanded our view of the genotype to include whole genomes. Here, we report the results of a fully integrated multivariate genome-wide association analysis of the shape of the Drosophila melanogaster wing in the Drosophila Genetic Reference Panel. Genotypic effects on wing shape were highly correlated between two different labs. We found 2,396 significant SNPs using a 5% FDR cutoff in the multivariate analyses, but just 4 significant SNPs in univariate analyses of scores on the first 20 principal component axes. A key advantage of multivariate analysis is that the direction of the estimated phenotypic effect is much more informative than a univariate one. Exploiting this feature, we show that the directions of effects were on average replicable in an unrelated panel of inbred lines. Effects of knockdowns of genes implicated in the initial screen were on average more similar than expected under a null model. Association studies that take a phenomic approach in considering many traits simultaneously are an important complement to the power of genomics. Multivariate analyses of such data are more powerful, more informative, and allow the unbiased study of pleiotropy.

scholarly journals The complex genomic basis of rapid convergent adaptation to pesticides across continents in a fungal plant pathogen

2020 ◽  
Author(s):  
Fanny E. Hartmann ◽  
Tiziana Vonlanthen ◽  
Nikhil Kumar Singh ◽  
Megan McDonald ◽  
Andrew Milgate ◽  
...  
Keyword(s):  
Selective Sweep ◽  
De Novo ◽  
Association Studies ◽  
Strong Support ◽  
Phenotypic Trait ◽  
Phenotypic Traits ◽  
Genome Wide Association Studies ◽  
Zymoseptoria Tritici ◽  
Standing Variation ◽  
Genome Wide

AbstractConvergent evolution leads to identical phenotypic traits in different species or populations. Convergence can be driven by standing variation allowing selection to favor identical alleles in parallel or the same mutations can arise independently. However, the molecular basis of such convergent adaptation remains often poorly resolved. Pesticide resistance in agricultural ecosystems is a hallmark of convergence in phenotypic traits. Here, we analyze the major fungal pathogen Zymoseptoria tritici causing serious losses on wheat and with parallel fungicide resistance emergence across continents. We sampled three population pairs each from a different continent spanning periods early and late in the application of fungicides. To identify causal loci for resistance, we combined knowledge from molecular genetics work and performed genome-wide association studies (GWAS) on a global set of isolates. We discovered yet unknown factors in azole resistance including membrane stability functions. We found strong support for the ‘hotspot’ model of resistance evolution with parallel changes in a small set of loci but additional loci showed more population-specific allele frequency changes. Genome-wide scans of selection showed that half of all known resistance loci were overlapping a selective sweep region. Hence, the application of fungicides was one of the major selective agents acting on the pathogen over the past decades. Furthermore, loci identified through GWAS showed the highest overlap with selective sweep regions underlining the importance to map phenotypic trait variation in evolving populations. Our population genomic analyses showed that both de novo mutations and gene flow likely contributed to the parallel emergence of resistance.

scholarly journals SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

2019 ◽  
Author(s):  
M. Pérez-Enciso ◽  
L. C. Ramírez-Ayala ◽  
L.M. Zingaretti
Keyword(s):  
Genomic Prediction ◽  
Predictive Accuracy ◽  
Sequence Data ◽  
Association Studies ◽  
Single Step ◽  
Genome Wide Association ◽  
Drosophila Genome ◽  
Genome Wide Association Studies ◽  
Complex Phenotypes ◽  
Genome Wide

AbstractBackgroundGenomic Prediction (GP) is the procedure whereby molecular information is used to predict complex phenotypes. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help in designing optimum experiments, including genome wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible python3 forward simulator.ResultsSeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes determined by any number of causal loci. SeqBreed implements several GP methods, including single step GBLUP. We demonstrate its functionality with Drosophila Genome Reference Panel (DGRP) sequence data and with tetraploid potato genotypes.ConclusionsSeqBreed is a flexible and easy to use tool appropriate for optimizing GP or genome wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation and examples are available at https://github.com/miguelperezenciso/SeqBreed.

scholarly journals Japonica Array NEO with Increased Genome-Wide Coverage and Abundant Disease Risk SNPs

2020 ◽  
Author(s):  
Mika Sakurai-Yageta ◽  
Kazuki Kumada ◽  
Chinatsu Gocho ◽  
Satoshi Makino ◽  
Akira Uruno ◽  
...  
Keyword(s):  
Japanese Population ◽  
Disease Risk ◽  
Association Studies ◽  
Snp Array ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Snp Arrays ◽  
Tag Snps ◽  
Genome Wide ◽  
Risk Snps

Abstract Background: Increasing the power of genome-wide association studies in diverse populations is important for understanding the genetic determinants of disease risks, and large-scale genotype data are collected by genome cohort and biobank projects all over the world. In particular, ethnic-specific SNP arrays are becoming more important because the use of universal SNP arrays has some limitations in terms of cost-effectiveness and throughput. As part of the Tohoku Medical Megabank Project, which integrates prospective genome cohorts into a biobank, we have been developing a series of Japonica Arrays for genotyping participants based on reference panels constructed from whole-genome sequence data of the Japanese population.Results: We designed a novel version of the SNP Array for the Japanese population, called Japonica Array NEO, comprising a total of 666,883 SNPs, including tag SNPs of autosomes and X chromosome with pseudoautosomal regions, SNPs of Y chromosome and mitochondria, and known disease risk SNPs. Among them, 654,246 tag SNPs were selected from an expanded reference panel of 3,552 Japanese using pairwise r2 of linkage disequilibrium measures. Moreover, 28,298 SNPs were included for the evaluation of previously identified disease risk SNPs from the literature and databases, and those present in the Japanese population were extracted using the reference panel. The imputation performance of Japonica Array NEO was assessed by genotyping 286 Japanese samples. We found that the imputation quality r2 and INFO score in the minor allele frequency bin >2.5%–5% were >0.9 and >0.8, respectively, and >12 million markers were imputed with an INFO score >0.8. After verification, Japonica Arrays were used to efficiently genotype cohort participants from the sample selection to perform a quality assessment of the raw data; approximately 130,000 genotyping data of >150,000 participants has already been obtained. Conclusions: Japonica Array NEO is a promising tool for genotyping the Japanese population with genome-wide coverage, contributing to the development of genetic risk scores for this population and further identifying disease risk alleles among individuals of East Asian ancestry.

scholarly journals The genetics of neuropsychiatric disorders

2018 ◽  
Vol 2 ◽  
pp. 239821281879927 ◽  
Author(s):  
Nicholas J. Bray ◽  
Michael C. O’Donovan
Keyword(s):  
Copy Number ◽  
Genetic Architecture ◽  
Recent Progress ◽  
Association Studies ◽  
Neuropsychiatric Disorders ◽  
Review Article ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Number Variation

Neuropsychiatric disorders are complex conditions with poorly defined neurobiological bases. In recent years, there have been significant advances in our understanding of the genetic architecture of these conditions and the genetic loci involved. This review article describes historical attempts to identify susceptibility genes for neuropsychiatric disorders, recent progress through genome-wide association studies, copy number variation analyses and exome sequencing, and how these insights can inform the neuroscientific investigation of these conditions.

scholarly journals Insights from GWAS into the quantitative genetics of transcription in humans

2010 ◽  
Vol 92 (5-6) ◽  
pp. 361-369 ◽  
Author(s):  
JINHEE KIM ◽  
GREG GIBSON
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Association Studies ◽  
Gene Expression Profiles ◽  
Transcript Abundance ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Traits ◽  
Genome Wide ◽  
Ready Availability

SummaryHuman gene expression profiles have emerged as an effective model system for the dissection of quantitative genetic traits. Peripheral blood and transformed lymphoblasts are particularly attractive for their ready availability and repeatability, respectively, and the advent of relatively inexpensive genotyping and microarray analysis technologies has facilitated genome-wide association for transcript abundance in numerous settings. Thousands of genes have been shown to harbour regulatory polymorphisms that have large local effects on transcription, explaining 20% or more of the variance in many cases, but the focus on such results obscures the reality that the vast majority of the genetic component of transcriptional variance remains to be ascertained. This mini-review surveys the inferences derived from genome-wide association studies (GWAS) for gene expression to date, and discusses some of the issues we face in finding the remainder of the heritability and understanding how environmental and genetic regulatory factors orchestrate the highly structured architecture of transcriptional variation.

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