PediHaplotyper: software for consistent assignment of marker haplotypes in pedigrees

Voorrips, Roeland E.; Bink, M.C.A.M.; Kruisselbrink, Johannes W.; Putten, H.J.J. Koehorst-van; Weg, Eric van de

doi:10.1007/s11032-016-0539-y

Cited by 49 publications

(45 citation statements)

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“…To define SNP HBs, 26,544 unique and robust SNPs were selected and ordered according to the Chandler genome v2.0 physical map. Subsequently, for each SNP markers and individual, phasing and identification of closely linked groups of SNPs, without recombination in most of the pedigree, was performed using the software FlexQTL TM (Bink et al 2014) and PediHaplotyper (Voorrips et al 2016) following the approach described in (Vanderzande et al 2019) and (Voorrips et al 2016). In particular, HB were defined by recombination sites detected in ancestral generation of Chandler.…”

Section: Methodsmentioning

confidence: 99%

High-quality chromosome-scale assembly of the walnut (Juglans regiaL) reference genome

Marrano

Britton

Zaini

et al. 2019

Preprint

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27The release of the first reference genome of walnut (Juglans regia L.) enabled many achievements 28 in the characterization of walnut genetic and functional variation. However, it is highly 29 fragmented, preventing the integration of genetic, transcriptomic, and proteomic information to 30 fully elucidate walnut biological processes. Here we report the new chromosome-scale assembly 31 of the walnut reference genome (Chandler v2.0) obtained by combining Oxford Nanopore long-32 read sequencing with chromosome conformation capture (Hi-C) technology. Relative to the 33 previous reference genome, the new assembly features an 84.4-fold increase in N50 size, and the 34 full sequence of all 16 chromosomal pseudomolecules, nine of which present telomere sequences 35 at both ends. Using full-length transcripts from single-molecule real-time sequencing, we predicted 36 40,491 gene models, with a mean gene length higher than the previous gene annotations. Most of 37 the new protein-coding genes (90%) are full-length, which represents a significant improvement 38 compared to Chandler v1.0 (only 48%). We then tested the potential impact of the new 39 chromosome-level genome on different areas of walnut research. By studying the proteome 40 changes occurring during catkin development, we observed that the virtual proteome obtained 41 from Chandler v2.0 presents fewer artifacts than the previous reference genome, enabling the 42 identification of a new potential pollen allergen in walnut. Also, the new chromosome-scale 43 genome facilitates in-depth studies of intraspecies genetic diversity by revealing previously 44 undetected autozygous regions in Chandler, likely resulting from inbreeding, and 195 genomic 45 regions highly differentiated between Western and Eastern walnut cultivars. Overall, Chandler 46 v2.0 is a valuable resource to understand and explore walnut biology better.47 48Persian walnut (Juglans regia L.) is among the top three most-consumed nuts in the world, and 50 over the last ten years, its global production increased by 37% (International Nut and Dried Fruit 51

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Section: Methodsmentioning

confidence: 99%

High-quality chromosome-scale assembly of the walnut (Juglans regiaL) reference genome

Marrano

Britton

Zaini

et al. 2019

Preprint

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“…If the calling error occurred in a single or few individuals, haplotypes were manually adjusted to reflect the change in SNP allele. In the rare event that a large group of individuals had their SNP genotype calls adjusted, the corresponding haplotypes were re-determined using PediHaplotyper [36]. Where Mendelian-inconsistent errors were due to missing SNP alleles, the individual was compared to its parent and offspring to determine the correct haplotype.…”

Section: Methodsmentioning

confidence: 99%

“…The PediHaplotyper package [36] was loaded into R and the working directory was set to the location of the input files created above (‘HaploBlocks.map’, ‘mhaplotypes.csv’, ‘flexqtl.par’, and ‘flexqtl.sort’). In R, the function ‘fq_haplotyping_session(sessionID=’prefix”, mapfile=“HaploBlocks.map”)’ was used to create the haplotype output files in the working directory where ‘prefix’ was user-defined text that prefixed all output file names.…”

Section: Methodsmentioning

confidence: 99%

“…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]. Despite the existence these and other methods and software, an effective way to combine these methods has not been described.…”

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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“…Pedigree-based QTL analysis (PBA) (Van de Weg et al 2003) using DNA markers, pedigree information, and inter-related populations has been proposed as an approach to identify and characterize more QTL and more alleles at identified QTLs than are possible in biparental QTL studies. Recent advances have been made in PBA by the development of dedicated software including FlexQTL™ (Bink et al 2002(Bink et al , 2008www.flexqtl.nl), Pedimap (Voorrips et al 2012), and PediHaplotyper (Voorrips et al 2016). PBA has been made a more attractive approach in apple through guidelines on the composition of the study germplasm (Peace et al 2014), high throughput genomewide genotyping capabilities through SNP arrays (Chagné et al 2012;Bianco et al 2014Bianco et al , 2016, the availability of sets of pedigreed full-sib families (Peace et al 2014), and standardized phenotyping procedures for some major traits (Evans et al 2011a).…”

Section: Communicated By D Chagnémentioning

confidence: 99%

Two QTL characterized for soft scald and soggy breakdown in apple (Malus × domestica) through pedigree-based analysis of a large population of interconnected families

Howard

Weg

Tillman

et al. 2017

Tree Genetics & Genomes

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Soft scald and soggy breakdown are important postharvest physiological disorders of apple (Malus × domestica). 'Honeycrisp' and some of its offspring are particularly susceptible to developing these disorders. The purpose of this study was to identify molecular markers associated with high incidences of soft scald and soggy breakdown for use in marker-assisted breeding. Towards this aim, we employed a pedigree-based approach using mostly germplasm related to 'Honeycrisp.' Two quantitative trait loci (QTL) were consistently identified on linkage groups (LGs) 2 and 16 across the 2014 and 2015 harvest years. The same QTL were identified for both storage disorders, indicating that they may be physiologically related. 'Honeycrisp' is homozygous for an identical by state haplotype at the LG2 QTL that was consistently associated with a deleterious effect on soft scald and soggy breakdown incidence. This haplotype was traced through SNP-confirmed pedigrees to the following cultivars: 'Grimes Golden,' 'Northern Spy,' 'Rome Beauty,' and 'Fireside' and is common in derived apple germplasm. Haplotypes at the LG16 QTL could not be adequately characterized due to variation between years combined with effects of this QTL being of relatively smaller size and being most evident in individuals that carry two copies of the deleterious haplotype at the LG2 QTL. These results suggest that limiting homozygosity of the deleterious haplotype at the LG2 QTL through marker-assisted breeding would be a valid strategy to limit soft scald and soggy breakdown incidences in apple seedling populations.

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PediHaplotyper: software for consistent assignment of marker haplotypes in pedigrees

Cited by 49 publications

References 10 publications

High-quality chromosome-scale assembly of the walnut (Juglans regiaL) reference genome

High-quality chromosome-scale assembly of the walnut (Juglans regiaL) reference genome

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

Two QTL characterized for soft scald and soggy breakdown in apple (Malus × domestica) through pedigree-based analysis of a large population of interconnected families

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