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

Diaporthe spp. are critical plant pathogens that cause wood cankers, wilt, dieback, and fruit rot in a wide variety of economic plant hosts and are regarded as one of the most acute threats faced by kiwifruit industry worldwide. Diaporthe phragmitis strain NJD1 is a highly pathogenic isolate of soft rot of kiwifruit. Here, we present a high-quality genome-wide sequence of D. phragmitis NJD1 that was assembled into 28 contigs containing a total size of 58.33 Mb and N50 length of 3.55 Mb. These results lay a solid foundation for understanding host–pathogen interaction and improving disease management strategies.

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Long Fragment Read (LFR) Technology: Cost-Effective, High-Quality Genome-Wide Molecular Haplotyping

Methods in Molecular Biology - Haplotyping ◽

10.1007/978-1-4939-6750-6_11 ◽

2017 ◽

pp. 191-205 ◽

Cited By ~ 3

Author(s):

Mark A. McElwain ◽

Rebecca Yu Zhang ◽

Radoje Drmanac ◽

Brock A. Peters

Keyword(s):

Cost Effective ◽

High Quality ◽

Genome Wide ◽

High Quality Genome ◽

Long Fragment Read

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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

10.1101/514281 ◽

2019 ◽

Cited By ~ 5

Author(s):

Stijn Vanderzande ◽

Nicholas P Howard ◽

Lichun Cai ◽

Cassia Da Silva Linge ◽

Laima Antanaviciute ◽

...

Keyword(s):

Sweet Cherry ◽

Marker Locus ◽

Data Sets ◽

High Quality ◽

Data Set ◽

Genome Database ◽

Genetic Analyses ◽

Ploidy Levels ◽

Genome Wide ◽

Haplotype Data

AbstractHigh-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored loci is too time-consuming. However, automated SNP scoring can result in errors that should be corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we have developed a comprehensive workflow. This multiple-step workflow is based on inheritance principles and on removal of markers and individuals that do not follow these principles, as demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance principles in the genotypic data, streamlined with FlexQTL™ software. Retained SNPs were grouped into haploblocks to increase the information content of single alleles and reduce computational power needed in downstream genetic analyses. Haploblock borders were defined by recombination locations detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for Rosaceae.

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High Quality Genome-Wide Genotyping from Archived Dried Blood Spots without DNA Amplification

PLoS ONE ◽

10.1371/journal.pone.0064710 ◽

2013 ◽

Vol 8 (5) ◽

pp. e64710 ◽

Cited By ~ 17

Author(s):

Krystal R. St. Julien ◽

Laura L. Jelliffe-Pawlowski ◽

Gary M. Shaw ◽

David K. Stevenson ◽

Hugh M. O’Brodovich ◽

...

Keyword(s):

Dna Amplification ◽

Dried Blood Spots ◽

High Quality ◽

Blood Spots ◽

Genome Wide ◽

High Quality Genome

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RADpainter and fineRADstructure: population inference from RADseq data

10.1101/057711 ◽

2016 ◽

Cited By ~ 4

Author(s):

Milan Malinsky ◽

Emiliano Trucchi ◽

Daniel John Lawson ◽

Daniel Falush

Keyword(s):

Clustering Algorithm ◽

Restriction Site ◽

Model Organism ◽

Rad Sequencing ◽

Nearest Neighbour ◽

High Quality ◽

Population Inference ◽

Genome Wide ◽

Haplotype Data ◽

High Quality Genome

AbstractPowerful approaches to inferring recent or current population structure based on nearest neighbour haplotype ‘coancestry’ have so far been inaccessible to users without high quality genome-wide haplotype data. With a boom in non-model organism genomics, there is a pressing need to bring these methods to communities without access to such data. Here we present RADpainter, a new program designed to infer the coancestry matrix from restriction-site-associated DNA sequencing (RADseq) data. We combine this program together with a previously published MCMC clustering algorithm into fineRADstructure - a complete, easy to use, and fast population inference package for RADseq data (https://github.com/millanek/fineRADstructure). Finally, with two example datasets, we illustrate its use, benefits, and robustness to missing RAD alleles in double digest RAD sequencing.

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