ASSIsT: an automatic SNP scoring tool for in- and outbreeding species

Guardo, Mario Di; Micheletti, Diego; Bianco, Luca; Putten, H.J.J. Koehorst-van; Longhi, Sara; Costa, Fabrizio; Aranzana, María José; Velasco, Riccardo; Arús, Pere; Troggio, Michela; Weg, Eric van de

doi:10.1093/bioinformatics/btv446

Cited by 48 publications

(49 citation statements)

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“…The ‘Final report’ and ‘DNA report’ input files were created as described in the ASSIsT Reference Manual [34]. Briefly, a ‘Final Report’ and ‘DNA Report’ were generated using the ‘Report Wizard’ under the ‘Reports’ option of the ‘Analysis’ section.…”

Section: Methodsmentioning

confidence: 99%

“…The presence of these highly similar sequences can lead to multi-locus segregating SNP markers that cannot be adequately called. The calling of a single segregating locus might also be hampered by the background signal of targeted but non-segregating gene copies (ASSIsT Reference Manual p17 [34]). The presence of one or more additional SNPs, insertions, or deletions in the probe-binding region can lead to reduced or loss of binding affinity for the SNP’s probe and thereby to the presence of additional clusters, both of which can lead to incorrect genotype scoring of some SNPs [33].…”

Section: Introductionmentioning

confidence: 99%

“…Methods and software exist to tackle specific types of errors. For example, the ASSIsT software was developed for use with Illumina Infinium ® arrays to identify which SNPs show robust results, which SNPs might have genotype calling errors due to alleles with reduced affinity or null alleles, and which SNPs are monomorphic or failed completely [35]. Another example is the aggregation of linked SNPs into a single genetic locus, called haploblock, which facilitates tracking the inheritance of alleles within a pedigree and subsequent identification of inheritance inconsistencies [36].…”

Section: Introductionmentioning

confidence: 99%

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High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Vanderzande

Howard

Cai

et al. 2019

Preprint

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26High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype 27 calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can 28 lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. 29High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored 30 loci is too time-consuming. However, automated SNP scoring can result in errors that should be 31 corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in 32 downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we 33 have developed a comprehensive workflow. This multiple-step workflow is based on inheritance 34 principles and on removal of markers and individuals that do not follow these principles, as 35 demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed 36 germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using 37 ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance 38 principles in the genotypic data, streamlined with FlexQTL TM software. Retained SNPs were grouped into 39 haploblocks to increase the information content of single alleles and reduce computational power 40 needed in downstream genetic analyses. Haploblock borders were defined by recombination locations 41 detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was 42 conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using 43 this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and 44 sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 45 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The 46 highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the 47 apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for 48 Rosaceae. 3 49 50 103 Manual p17 [34]). The presence of one or more additional SNPs, insertions, or deletions in the probe-104 binding region can lead to reduced or loss of binding affinity for the SNP's probe and thereby to the 105 presence of additional clusters, both of which can lead to incorrect genotype scoring of some SNPs [33]. 106 107 No systematic workflow exists to efficiently detect and resolve all types of errors from a genotypic data 108 set for pedigreed germplasm. Methods and software exist to tackle specific types of errors. For example, 109 the ASSIsT software was developed for use with Illumina Infinium® arrays to identify which SNPs show 110 robust results, which SNPs might have genotype calling errors due to alleles with reduced affinity or null 111 alleles, and which...

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Vanderzande

Howard

Cai

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…The genotyping was carried out with the 20K Infinium array (Illumina; Bianco et al , 2014; ). The SNP data, initially processed with the GenomeStudio Data Analysis software, were finally elaborated with ASSIsT (Di Guardo et al , 2015), a dedicated stand-alone pipeline to filter and re-edit SNP calls with a distorted segregation pattern due to the presence of null alleles (Pikunova et al , 2013). …”

Section: Methodsmentioning

confidence: 99%

Deciphering the genetic control of fruit texture in apple by multiple family-based analysis and genome-wide association

Guardo

Bink

Guerra

et al. 2017

Journal of Experimental Botany

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“…Among the 6849 polymorphic SNPs, 3123 were used in this study, after a careful checking of their robustness consistency (Van de Weg et al, 2013; Di Guardo et al, 2015) and recombination pattern (Allard et al, 2016). In particular, all markers which were not strictly bi-allelic in at least one of the parent were discarded.…”

Section: Methodsmentioning

confidence: 99%

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

Migault¹,

Pallas²,

Costes³

2017

Front. Plant Sci.

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In crops, optimizing target traits in breeding programs can be fostered by selecting appropriate combinations of architectural traits which determine light interception and carbon acquisition. In apple tree, architectural traits were observed to be under genetic control. However, architectural traits also result from many organogenetic and morphological processes interacting with the environment. The present study aimed at combining a FSPM built for apple tree, MAppleT, with genetic determinisms of architectural traits, previously described in a bi-parental population. We focused on parameters related to organogenesis (phyllochron and immediate branching) and morphogenesis processes (internode length and leaf area) during the first year of tree growth. Two independent datasets collected in 2004 and 2007 on 116 genotypes, issued from a ‘Starkrimson’ × ‘Granny Smith’ cross, were used. The phyllochron was estimated as a function of thermal time and sylleptic branching was modeled subsequently depending on phyllochron. From a genetic map built with SNPs, marker effects were estimated on four MAppleT parameters with rrBLUP, using 2007 data. These effects were then considered in MAppleT to simulate tree development in the two climatic conditions. The genome wide prediction model gave consistent estimations of parameter values with correlation coefficients between observed values and estimated values from SNP markers ranging from 0.79 to 0.96. However, the accuracy of the prediction model following cross validation schemas was lower. Three integrative traits (the number of leaves, trunk length, and number of sylleptic laterals) were considered for validating MAppleT simulations. In 2007 climatic conditions, simulated values were close to observations, highlighting the correct simulation of genetic variability. However, in 2004 conditions which were not used for model calibration, the simulations differed from observations. This study demonstrates the possibility of integrating genome-based information in a FSPM for a perennial fruit tree. It also showed that further improvements are required for improving the prediction ability. Especially temperature effect should be extended and other factors taken into account for modeling GxE interactions. Improvements could also be expected by considering larger populations and by testing other genome wide prediction models. Despite these limitations, this study opens new possibilities for supporting plant breeding by in silico evaluations of the impact of genotypic polymorphisms on plant integrative phenotypes.

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ASSIsT: an automatic SNP scoring tool for in- and outbreeding species

Cited by 48 publications

References 5 publications

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Deciphering the genetic control of fruit texture in apple by multiple family-based analysis and genome-wide association

Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture

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