Dissecting the eQTL micro-architecture in Caenorhabditis elegans

Hartanto

et al. 2019

Preprint

Self Cite

18Quantitative genetics provides the tools for linking polymorphic loci (QTLs) to trait 19 variation. Linkage analysis of gene expression is an established and widely applied 20 method, leading to the identification of expression quantitative trait loci (eQTLs). (e)QTL 21 detection facilitates the identification and understanding of the underlying molecular 22 components and pathways, yet (e)QTL data access and mining often is a bottleneck. Here 23 we present WormQTL2 (www.bioinformatics.nl/WormQTL2/), a database and platform 24 for comparative investigations and meta-analyses of published (e)QTL datasets in the 25 model nematode worm C. elegans. WormQTL2 integrates six eQTL studies spanning 11 26 conditions as-well-as over 1000 traits from 32 studies and allows experimental results to 27 be compared, reused, and extended upon to guide further experiments and conduct 28 systems-genetic analyses. For example, one can easily screen a locus for specific cis-eQTLs 29 that could be linked to variation in other traits, detect gene-by-environment interactions 30 by comparing eQTLs under different conditions, or find correlations between QTL 31 profiles of classical traits and gene expression.32 100 (2,3,13,23,30,34,41,47,49,(53)(54)(55)(56)(57)(58)(59)61,76,(80)(81)(82)(83)(84)(85)(86)(87)(88)(89)(90) (Table 2). In this paper, we present WormQTL2 101 and showcase its use by presenting short research scenarios. 102 Results 103 104 eQTL studies in WormQTL2 105WormQTL2 is a browser-based interactive platform and database for investigating expression 106 and other Quantitative Trait Locus (QTL) studies conducted in C. elegans (Figure 1). It enables 107 access to the mapping data of six previously published eQTL studies (Table 1) (28,50,(76)(77)(78)(79). 108Together, these studies cover over 700 samples, including expression measurements of 109 approximately 20,000 different genes across different life stages and environmental conditions. 110The effect of genetic variation on gene expression is presented in 11 genome-wide sets of 111 eQTLs from three different RIL populations. The three populations consist of two different 112 CB4856 x N2 populations, recombinant inbred lines (RILs) (28) and recombinant inbred 113 advanced intercross lines (RIAILs) (91,92) and a mutation introgressed RIL population 114 resulting from a cross between a let-60 gain-of-function mutant in an N2 background, MT2124, 115 with CB4856 (11,50). For the Li et al. 2006, Viñuela & Snoek et al. 2010 and Li et al. 2010 116 Figure 1: WormQTL2 Homepage. On the top of the page the navigation bar can be found. This includes the WormQTL2 logo, which functions as a home button. It also includes a fast link to the Correlation and Locus overviews as well as links for help, data download, and visual examples. The search box is located in the centre, in which genes, phenotypes and GO terms can be entered. Shown in the blue middle square are the buttons for the investigations of single traits, correlating QTL profiles, QTLs at a specific locus, all eQTLs of an expe...

Section: Introductionmentioning

confidence: 99%

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Snoek

Hartanto

et al. 2019

Preprint

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“…In addition, comparison of eQTL profiles from multiple environments may aid our understanding of how genetic variation shapes the effects the environment has on the appearance of phenotypes [57,63,64]. In plant (Arabidopsis) and worm (Caenorhabditis elegans) model systems it has been shown that especially trans-eQTLs are dynamic and can be highly specific for a certain environment [57,[63][64][65][66][67][68].…”

Section: Introductionmentioning

confidence: 99%

Plasticity of maternal environment dependent expression-QTLs of tomato seeds

Nijveen

Zanten

et al. 2021

Preprint

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Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depends on seed quality and on environmental factors such as nutrient availability. In tomato (Solanum lycopersicum) and many other species, seed quality and seedling establishment characteristics are determined by genetic variation, as well as the maternal environment in which the seeds develop and mature. The genetic contribution to variation in seed and seedling quality traits and environmental responsiveness can be estimated at transcriptome level in the dry seed by mapping genomic loci that affect gene expression (expression QTLs) in contrasting maternal environments. In this study, we applied RNA-sequencing to measure gene expression of seeds of a tomato RIL population derived from a cross between S. lycopersicum (cv. Moneymaker) and S. pimpinellifolium (G1.1554). The seeds matured on plants cultivated under different nutritional environments; i.e. on high phosphorus or low nitrogen. The obtained SNPs were subsequently used to construct a high-density genetic map. We show how the genetic landscape of plasticity in gene regulation in dry seeds is affected by the maternal nutrient environment. The combined information on natural genetic variation mediating (variation in) responsiveness to the environment may contribute to knowledge-based breeding programs aiming to develop crop cultivars that are resilient to stressful environments.

“…Many functional polymorphisms in plants and animals, including many model species such as model nematode C. elegans, have been discovered using QTL mapping . Over the last decade molecular phenotypes such as transcript levels, protein levels and metabolites have also been used in QTL mapping [26][27][28][29][30][31][32]. Heritable variation in these molecular phenotypes often plays a role in heritable phenotypic variation [10,30,33].…”

Section: Introductionmentioning

confidence: 99%

“…Heritable variation in these molecular phenotypes often plays a role in heritable phenotypic variation [10,30,33]. Mapping expression QTLs (eQTLs) can provide insight into the transcriptional architecture of complex traits and have been conducted in model species such as Arabidopsis thaliana and C. elegans as well as several other taxa [26,[28][29][30][31][34][35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

“…Most eQTL studies have been done on populations of recombinant inbred lines (RILs) originating from a cross between two different parental genotypes [26,[28][29][30][31][34][35][36][37][38][39][40]. Inclusion of more than two parents can capture more genetic variation, increasing the number of detected QTLs, potentially allowing more precise mapping and therefore reducing the number of potential candidate causal genes to be verified [42].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The genetics of gene expression in aC. elegansmulti parental recombinant inbred line population

Snoek

Nijveen

et al. 2021

Preprint

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Studying genetic variation of gene expression provides a powerful way to unravel the molecular components underlying complex traits. Expression QTL studies have been performed in several different model species, yet most of these linkage studies have been based on genetic segregation of two parental alleles. Recently we developed a multi-parental segregating population of 200 recombinant inbred lines (mpRILs) derived from four wild isolates (JU1511, JU1926, JU1931 and JU1941) in the nematode Caenorhabditis elegans. We used RNA-seq to investigate how multiple alleles affect gene expression in these mpRILs. We found 1,789 genes differentially expressed between the parental lines. Transgression, expression beyond any of the parental lines in the mpRILs, was found for 7,896 genes. For expression QTL mapping almost 9,000 SNPs were available. By combining these SNPs and the RNA-seq profiles of the mpRILs, we detected almost 6,800 eQTLs. Most trans-eQTLs (63%) co-locate in six newly identified trans-bands. The trans-eQTLs found in previous 2-parental allele eQTL experiments and this study showed some overlap (17.5%-46.8%), highlighting on the one hand that a large group of genes is affected by polymorphic regulators across populations and conditions, on the other hand it shows that the mpRIL population allows identification of novel gene expression regulatory loci. Taken together, the analysis of our mpRIL population provides a more refined insight into C. elegans complex trait genetics and eQTLs in general, as well as a starting point to further test and develop advanced statistical models for detection of multi-allelic eQTLs and systems genetics studying the genotype-phenotype relationship.