An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments

Duitama, Jorge; Quintero, Juan Camilo; Cruz, Daniel Felipe; Quintero, Constanza; Hubmann, Georg; Foulquié-Moreno, María R.; Verstrepen, Kevin J.; Thevelein, Johan M.; Tohmé, Joe

doi:10.1093/nar/gkt1381

Cited by 109 publications

(110 citation statements)

References 39 publications

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“…Strand bias filters are also not generally adequate for GBS data which probably explains why Samtools reported the lowest sensitivity in these experiments, taking into account that this tool was among the best ranked in our previous benchmarks with WGS data [17]. …”

Section: Discussionmentioning

confidence: 99%

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP

et al. 2016

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BackgroundTherecent development and availability of different genotype by sequencing (GBS) protocols provided a cost-effective approach to perform high-resolution genomic analysis of entire populations in different species. The central component of all these protocols is the digestion of the initial DNA with known restriction enzymes, to generate sequencing fragments at predictable and reproducible sites. This allows to genotype thousands of genetic markers on populations with hundreds of individuals. Because GBS protocols achieve parallel genotyping through high throughput sequencing (HTS), every GBS protocol must include a bioinformatics pipeline for analysis of HTS data. Our bioinformatics group recently developed the Next Generation Sequencing Eclipse Plugin (NGSEP) for accurate, efficient, and user-friendly analysis of HTS data.ResultsHere we present the latest functionalities implemented in NGSEP in the context of the analysis of GBS data. We implemented a one step wizard to perform parallel read alignment, variants identification and genotyping from HTS reads sequenced from entire populations. We added different filters for variants, samples and genotype calls as well as calculation of summary statistics overall and per sample, and diversity statistics per site. NGSEP includes a module to translate genotype calls to some of the most widely used input formats for integration with several tools to perform downstream analyses such as population structure analysis, construction of genetic maps, genetic mapping of complex traits and phenotype prediction for genomic selection. We assessed the accuracy of NGSEP on two highly heterozygous F1 cassava populations and on an inbred common bean population, and we showed that NGSEP provides similar or better accuracy compared to other widely used software packages for variants detection such as GATK, Samtools and Tassel.ConclusionsNGSEP is a powerful, accurate and efficient bioinformatics software tool for analysis of HTS data, and also one of the best bioinformatic packages to facilitate the analysis and to maximize the genomic variability information that can be obtained from GBS experiments for population genomics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2827-7) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP

et al. 2016

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“…The reads were demultiplexed and aligned to the rice reference genome (OsNipponbare-Reference-IRGSP-1.0; Kawahara et al, 2013), and variants were identified using the NGSEP pipeline (Duitama et al, 2014). Missing data were imputed with the implementation of the Fast Phase Hidden Markov Model (Scheet and Stephens, 2006).…”

Section: Snp Genotyping Datamentioning

confidence: 99%

Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

et al. 2017

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Elucidating the genetic control of rooting behavior under water-deficit stress is essential to breed climate-robust rice (Oryza sativa) cultivars. Using a diverse panel of 274 indica genotypes grown under control and water-deficit conditions during vegetative growth, we phenotyped 35 traits, mostly related to root morphology and anatomy, involving 45,000 root-scanning images and nearly 25,000 cross sections from the root-shoot junction. The phenotypic plasticity of these traits was quantified as the relative change in trait value under water-deficit compared with control conditions. We then carried out a genome-wide association analysis on these traits and their plasticity, using 45,608 high-quality single-nucleotide polymorphisms. One hundred four significant loci were detected for these traits under control conditions, 106 were detected under water-deficit stress, and 76 were detected for trait plasticity. We predicted 296 (control), 284 (water-deficit stress), and 233 (plasticity) a priori candidate genes within linkage disequilibrium blocks for these loci. We identified key a priori candidate genes regulating root growth and development and relevant alleles that, upon validation, can help improve rice adaptation to water-deficit stress.

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“…We kept only SNPs with a mapping quality > 20, resulting in 315,276 SNPs. Informative markers of a full sib cross‐pollinated design (CP = “hk × hk”, “lm × ll” and “nn × np”; Van Ooijen & Jansen, ) were extracted using NGSEP v2.1.2 (Duitama et al, ). We retained one marker per fragment (the one with the least missing data), assuming that recombination does not occur within a fragment smaller than 125 bp.…”

Section: Methodsmentioning

confidence: 99%

Insights into the genetic architecture of sexual dimorphism from an interspecific cross between two diverging Silene (Caryophyllaceae) species

Baena‐Díaz

Zemp²,

Widmer

2019

Molecular Ecology

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The evolution of sexual dimorphism in species with separate sexes is influenced by the resolution of sexual conflicts creating sex differences through genetic linkage or sex‐biased expression. Plants with different degrees of sexual dimorphism are thus ideal to study the genetic basis of sexual dimorphism. In this study we explore the genetic architecture of sexual dimorphism between Silene latifolia and Silene dioica. These species have chromosomal sex determination and differ in the extent of sexual dimorphism. To test whether QTL for sexually dimorphic traits have accumulated on the sex chromosomes and to quantify their contribution to species differences, we create a linkage map and performed QTL analysis of life history, flower and vegetative traits using an unidirectional interspecific F2 hybrid cross. We found support for an accumulation of QTL on the sex chromosomes and that sex differences explained a large proportion of the variance between species, suggesting that both natural and sexual selection contributed to species divergence. Sexually dimorphic traits that also differed between species displayed transgressive segregation. We observed a reversal in sexual dimorphism in the F2 population, where males tended to be larger than females, indicating that sexual dimorphism is constrained within populations but not in recombinant hybrids. This study contributes to the understanding of the genetic basis of sexual dimorphism and its evolution in Silene.

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An integrated framework for discovery and genotyping of genomic variants from high-throughput sequencing experiments

Cited by 109 publications

References 39 publications

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP

Bioinformatic analysis of genotype by sequencing (GBS) data with NGSEP

Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

Insights into the genetic architecture of sexual dimorphism from an interspecific cross between two diverging Silene (Caryophyllaceae) species

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