A multi-parent recombinant inbred line population of C. elegans allows identification of novel QTLs for complex life history traits

Snoek, L. Basten; Volkers, Rita J. M.; Nijveen, Harm; Petersen, Carola; Dirksen, Philipp; Sterken, M.G.; Nakad, Rania; Riksen, J.A.G.; Rosenstiel, Philip; Šťastná, Jana; Braeckman, Bart P.; Harvey, Simon C.; Schulenburg, Hinrich; Kammenga, J.E.

doi:10.1186/s12915-019-0642-8

Cited by 36 publications

(91 citation statements)

References 87 publications

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“…We compiled the list of studies using two reviews listing older studies, mostly pre-2000 (1, 35), and performed a literature search for more recent studies from 2000-onwards. For many studies we could obtain the relevant data from the supplemental information (2,3,5,6,21,34,41,47,49,53,54,57–59,76,80,81,83–86,89–91). Where such data was not provided in journal supplements, approaching the authors was successful for most studies (6-8,11,13,55,82,87,88,91).…”

Section: Resultsmentioning

confidence: 99%

“…We offer analytical access to studies widely differing in statistical power and RIL population. For example, the number of RILs used per (e)QTL dataset ranges from 36 for each of the three conditions in Viñuela & Snoek et al (76), up to 200 in Rockman et al (77) and Snoek et al (34) or even more in the CeMEE panel of Noble et al (111). Furthermore, the size of the genetic map (in centimorgans) of the N2 x CB4856 RIAIL populations is larger than the N2 x CB4856 RIL population (27,28,77).…”

Section: Discussionmentioning

confidence: 99%

“…Most quantitative genetics studies in this model animal have been conducted in recombinant inbred line (RIL) panels derived from two polymorphic wild type strains, namely N2 (isolated in Bristol, UK) and CB4856 (isolated in Hawaii, USA) (1,27–29). A substantial amount of phenotypic, genotypic, and high-throughput molecular data has been gathered across these recombinant inbred panels, as well as from introgression lines (30, 31) (31: PREPRINT NOT PEER REVIEWED), and many other wild isolates (32–34); for a more detailed overview, see reviews (1, 35). Together, the field of quantitative genetics in C. elegans has been very productive.…”

Section: Introductionmentioning

confidence: 99%

“…However, accessing all this data, and using it for follow-up studies or for comparative analysis can be challenging. Furthermore, recent in-depth studies on the ecology of C. elegans yielded even more phenotypic and genotypic information on novel wild isolates (34,36–46). Inclusion of diverse genetic backgrounds in experiments is therefore a welcome and useful addition to the work on the canonical N2 strain as it can identify novel modifiers of well-studied pathways and thereby shed light on molecular mechanisms of genetic variation (46–52).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, phenotypic QTLs are well represented with data on more than 1000 traits from 32 studies. This extends the exploratory options by integrating QTL data on classical phenotypes, gene expression levels as well as protein- and metabolite levels (2,3,13,23,30,34,41,47,49,53–59,61,76,80–90) (Table 2). In this paper, we present WormQTL2 and showcase its use by presenting short research scenarios.…”

Section: Introductionmentioning

confidence: 99%

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WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Snoek

Sterken

Hartanto

et al. 2019

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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...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

WormQTL2: an interactive platform for systems genetics inCaenorhabditis elegans

Snoek

Sterken

Hartanto

et al. 2019

Preprint

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Plasticity of maternal environment-dependent expression-QTLs of tomato seeds

et al. 2023

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Seeds are essential for plant reproduction, survival, and dispersal. Germination ability and successful establishment of young seedlings strongly depend 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 construct a linkage map and measure gene expression of seeds of a tomato recombinant inbred line (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 single-nucleotide polymorphisms (SNPs) were subsequently used to construct a 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.

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