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A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome

A high-density SNP genotyping array for Brassica napus and its ancestral diploid species based on optimised selection of single-locus markers in the allotetraploid genome

The availability of genome sequences and access to rela- tively economical next-generation sequencing technologies has provided the impetus to identify extensive nucleotide var- iation among different plant species. The abundance of sin- gle nucleotide polymorphisms (SNPs) across plant genomes has made them highly desirable for marker development (Ganal et al. 2009 , 2012 ). High-throughput (tens of thou- sands or higher) SNP screening can be achieved effectively by either genotyping-by-sequencing (GBS) or high-density SNP arrays. GBS requires no former knowledge of available SNPs within a species, but is heavily reliant on bioinformat- ics capacity, and although common SNP will be found across experiments, the SNP profile identified is dependent on the genotypes queried (Deschamps et al. 2012 ). In comparison, high-density SNP arrays provide a common platform that can be continuously used and replicated across multiple labs with minimal computational requirement (Ganal et al. 2012 ). However, such SNP genotyping arrays involve significant development costs to identify sufficient numbers of robust, informative loci that fulfill assay design criteria. Identify- ing high-quality SNP loci for array design requires sequence data from sufficient numbers of genotypes to be able to assess polymorphism levels and associated allele ratios across the diversity of a species to minimise ascertainment bias. In addition, genome duplication in polyploid genomes, such as B. napus, confounds the design of SNP assays, since nucleotide variation among closely related orthologous or paralogous sequences is often misinterpreted as allelic vari- ation (Parkin et al. 2010 ). Furthermore, since the SNPs are evaluated through hybridisation, multiple homologous and homoeologous loci may hybridise to a single SNP oligonu- cleotide probe leading to highly compressed and often irre- solvable SNP patterns.
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Deciphering the genetic diversity of landraces with high-throughput SNP genotyping of DNA bulks: methodology and application to the maize 50k array Mariangela Arca

Deciphering the genetic diversity of landraces with high-throughput SNP genotyping of DNA bulks: methodology and application to the maize 50k array Mariangela Arca

32 Table 2: Mean absolute error (MAE) in frequency estimation for 1,000 SNPs used to calibrate logistic regression equations. MAE is estimated by a cross-validation procedure in which a number of pools comprised between 1 and 15 among 18 is removed at random from the calibration set. This procedure was repeated 1,000 times for each SNP.

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Genome-wide SNP genotyping of DNA pools identifies untapped landraces and genomic regions that could enrich the maize breeding pool

Genome-wide SNP genotyping of DNA pools identifies untapped landraces and genomic regions that could enrich the maize breeding pool

FST estimated by Bayescan for markers under selection between geographic groups. Distance from gene (“Dist. from gene”) was based on the closest start or stop codon of the gene, 0 indicates that the SNP is within the gene. Functional annotation was retrieved from Gramene (https://www.gramene.org/). . CC-BY-ND 4.0 International license

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Development and validation of the Axiom (R) Apple480K SNP genotyping array

Development and validation of the Axiom (R) Apple480K SNP genotyping array

The resequencing panel comprised 65 apple accessions which included the 13 apple major founders and two doubled haploids (X9273 and X9336) used for the Illumina 20K SNP chip (Bianco et al., 2014). This panel (see Table 1, Figure 1) was chosen to cover a very large genetic diversity encompassing European and Russian germplasms and including some Iranian, Tunisian and US cultivars. Briefly, SSR genotypic data (16 SSRs) avail- able from the European project FruitBreedomics (Laurens et al., 2012) were used to select 52 new accessions exhibiting large genetic distances both among them and with the previously resequenced 13 accessions + 2 doubled haploids. Notoriety of the cultivars from various European countries was used as a second selection criterion. Leaf material was obtained from vari- ous institutions as described in Table S6. The neighbor-joining tree was built with the software D ARWIN (Perrier and Jacque- moud-Collet, 2006).
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Inferring sex-specific demographic history from SNP data

Inferring sex-specific demographic history from SNP data

For illustration purposes, we analyzed both cattle and human SNP genotyping data, provid- ing new insights into the sex-specific demographic history of these two species. We chose three cattle breeds (HOL, ANG and NDA) with contrasting breeding schemes (from a widespread use of artificial insemination in the HOL dairy cattle to mostly uncontrolled mating in the NDA cattle from West-Africa). These breeds are also representative of the post-domestication history, with HOL, ANG and NDA presumably originating from the same domestication cen- ter in the Middle East, ca. 10,000 YBP [ 56 ]. As expected, we found a strongly female-biased ESR in the commercial breeds (HOL and ANG), with less than two effective males for 100 effective females in both breeds. These ESR estimates integrate over the time of divergence between ANG and HOL, which has occurred ca. 2,000 YBP [ 57 ]. Since modern genetic improvement programs have been generalized only recently (in the past 70 years), the impact of increased selective pressure for beef (in ANG) or milk (in HOL) production on the ESR might thus be even higher than our estimate suggests. Before that, indeed, the ESR for commer- cial cattle breeds might have been only moderately female-biased, as we observe for the tradi- tionally raised NDA with about 36 effective males for 100 effective females. More interestingly, we found a strongly male-biased ESR (four effective females for 100 effective males) in the internal branch of the tree, which is ancestral to the ANG and HOL breeds. This result supports the hypothesis that around the period of cattle domestication, females were plausibly more eas- ily managed than males. Keeping and rearing preferentially female offspring would indeed tend to decrease the effective size for females. At the same time, preventing tamed females from breeding randomly with wild males would be a difficult task, which would result in turn in an increased effective size for males (see [ 58 ], p. 2218), and therefore in a male-biased ESR. Alternatively, introgression of wild auroch males into domesticated cattle [ 59 , 60 ] may have increased the male effective population size. Deciphering between these two non-mutually exclusive hypotheses would require further investigations.
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Inferring sex-specific demographic history from SNP data

Inferring sex-specific demographic history from SNP data

For illustration purposes, we analyzed both cattle and human SNP genotyping data, provid- ing new insights into the sex-specific demographic history of these two species. We chose three cattle breeds (HOL, ANG and NDA) with contrasting breeding schemes (from a widespread use of artificial insemination in the HOL dairy cattle to mostly uncontrolled mating in the NDA cattle from West-Africa). These breeds are also representative of the post-domestication history, with HOL, ANG and NDA presumably originating from the same domestication cen- ter in the Middle East, ca. 10,000 YBP [ 56 ]. As expected, we found a strongly female-biased ESR in the commercial breeds (HOL and ANG), with less than two effective males for 100 effective females in both breeds. These ESR estimates integrate over the time of divergence between ANG and HOL, which has occurred ca. 2,000 YBP [ 57 ]. Since modern genetic improvement programs have been generalized only recently (in the past 70 years), the impact of increased selective pressure for beef (in ANG) or milk (in HOL) production on the ESR might thus be even higher than our estimate suggests. Before that, indeed, the ESR for commer- cial cattle breeds might have been only moderately female-biased, as we observe for the tradi- tionally raised NDA with about 36 effective males for 100 effective females. More interestingly, we found a strongly male-biased ESR (four effective females for 100 effective males) in the internal branch of the tree, which is ancestral to the ANG and HOL breeds. This result supports the hypothesis that around the period of cattle domestication, females were plausibly more eas- ily managed than males. Keeping and rearing preferentially female offspring would indeed tend to decrease the effective size for females. At the same time, preventing tamed females from breeding randomly with wild males would be a difficult task, which would result in turn in an increased effective size for males (see [ 58 ], p. 2218), and therefore in a male-biased ESR. Alternatively, introgression of wild auroch males into domesticated cattle [ 59 , 60 ] may have increased the male effective population size. Deciphering between these two non-mutually exclusive hypotheses would require further investigations.
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Design and characterization of a 52K SNP chip for goats

Design and characterization of a 52K SNP chip for goats

Abstract The success of Genome Wide Association Studies in the discovery of sequence variation linked to complex traits in humans has increased interest in high throughput SNP genotyping assays in livestock species. Primary goals are QTL detection and genomic selection. The purpose here was design of a 50–60,000 SNP chip for goats. The success of a moderate density SNP assay depends on reliable bioinformatic SNP detection procedures, the technological success rate of the SNP design, even spacing of SNPs on the genome and selection of Minor Allele Frequencies (MAF) suitable to use in diverse breeds. Through the federation of three SNP discovery projects consolidated as the International Goat Genome Consortium, we have identified approximately twelve million high quality SNP variants in the goat genome stored in a database together with their biological and technical characteristics. These SNPs were identified within and between six breeds (meat, milk and mixed): Alpine, Boer, Creole, Katjang, Saanen and Savanna, comprising a total of 97 animals. Whole genome and Reduced Representation Library sequences were aligned on .10 kb scaffolds of the de novo goat genome assembly. The 60,000 selected SNPs, evenly spaced on the goat genome, were submitted for oligo manufacturing (Illumina, Inc) and published in dbSNP along with flanking sequences and map position on goat assemblies (i.e. scaffolds and pseudo-chromosomes), sheep genome V2 and cattle UMD3.1 assembly. Ten breeds were then used to validate the SNP content and 52,295 loci could be successfully genotyped and used to generate a final cluster file. The combined strategy of using mainly whole genome Next Generation Sequencing and mapping on a contig genome assembly, complemented with Illumina design tools proved to be efficient in producing this GoatSNP50 chip. Advances in use of molecular markers are expected to accelerate goat genomic studies in coming years.
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Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes

Allelic decomposition and exact genotyping of highly polymorphic and structurally variant genes

High-throughput sequencing provides the means to determine the allelic decomposition for any gene of interest—the number of copies and the exact sequence content of each copy of a gene. Although many clinically and functionally important genes are highly polymorphic and have undergone structural alterations, no high-throughput sequencing data analysis tool has yet been designed to effectively solve the full allelic decomposition problem. Here we introduce a combinatorial optimization framework that successfully resolves this challenging problem, including for genes with structural alterations. We provide an associated compu- tational tool Aldy that performs allelic decomposition of highly polymorphic, multi-copy genes through using whole or targeted genome sequencing data. For a large diverse sequencing data set, Aldy identi fies multiple rare and novel alleles for several important pharmacogenes, signi ficantly improving upon the accuracy and utility of current genotyping assays. As more data sets become available, we expect Aldy to become an essential com- ponent of genotyping toolkits.
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Improving the reliability of genotyping of multigene families in non-model organisms

Improving the reliability of genotyping of multigene families in non-model organisms

Download the review (PDF file) Reviewed by Thomas Bigot , 2019-07-10 17:14 This article presents a workflow to improve multi-locus genotyping. They propose an experimental set-up and a pipeline named Acacia to perform the genotyping itself. They chose chicken as a model organism, and try to characterize sequences of MHC B Complex with their tool.

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The Potential of HTS Approaches for Accurate Genotyping in Grapevine (Vitis vinifera L.)

The Potential of HTS Approaches for Accurate Genotyping in Grapevine (Vitis vinifera L.)

Figure 5. Example of the intense amplification of stutter bands at locus VVMD7 for two cultivars, Furmint and Mourverde. 3.4. Analyses of 96 V. vinifera Samples The sequencing analyses (i.e., the number of reads for the sequenced amplicons) for 96 different V. vinifera cultivars over 12 loci are presented in the Supplementary Material, Table S2. In the analysed data set, we included five counterparts from French and Slovenian collections (Chardonnay, Merlot, Pinot Noir, Cabernet Sauvignon, and Sultanine), and the comparison over 12 loci yielded 55 exact matches and 5 discrepancies (Supplementary data, Table S2); three out of five were different for only two bp for the compared alleles and two were within the locus VVMD27, which was previously confirmed as one of the loci with triallelic profiles (chimerism) that showed a high intra-clonal variability [ 51 , 52 ]. Discrepancies were found in the Merlot and Pinot Noir cultivars, with previously reported intra-clonal genetic variation [ 46 , 51 , 52 ]. Studies have previously reported polymorphisms identified by microsatellite markers, which indicate the presence of trialellic loci, referred to in grapevines as chimeras [ 46 , 49 ], caused by mutations in the cells of the meristem layers L1 and L2 [ 53 ]. 3.5. HTS Genotyping Economy
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High throughput SNP discovery and genotyping in hexaploid wheat

High throughput SNP discovery and genotyping in hexaploid wheat

Introduction Because they are the most abundant type of polymorphism in plant and animal genomes and because they are amenable to high-throughput, cost effective genotyping technologies, Single Nucleotide Polymorphisms (SNPs) have been adopted as the markers of choice in genetics. In the past years, they have been widely used for various applications, including genome-wide association studies, characterization of genetic resources, marker-assisted breeding and geno- mic selection [ 1 , 2 ]. The power of these different approaches relies heavily on marker density and on the ability to assay thousands of SNPs in parallel. Compared to other types of markers such SSRs or Diversity Array Technology (DArT) markers, SNP discovery relies on the com- parison of homologous sequences between genotypes to identify variations at the sequence level [ 3 ]. If the advent of next generation sequencing systems opened the way to whole genome resequencing of several small to medium genome plant species for SNP discovery [ 4 – 8 ], the size of the wheat genome has long hampered such approaches. As a result, most of the SNP dis- covery initiatives that have been conducted to date relied on complexity reduction approaches. For example, Winfield et al. [ 9 ] used an exome capture array to target ~57 Mb of coding sequences in 43 bread wheat accessions and wheat relatives and discovered 921,705 putative varietal SNPs. Similarly, a wheat exome capture targeting 107 Mb of non-redundant genic regions was used by Jordan et al. [ 10 ] to mine for SNPs in a panel of 62 wheat lines. Eventually, ~1.57 million SNPs were identified. In 2014, RNA-seq on a set of 19 bread wheat accessions led to the discovery of 67,686 variants [ 11 ]. Also, genotyping-by-sequencing has been applied to wheat [ 12 ]. Despite the fact that this technique has the potential to sample a higher fraction of the genome and especially intergenic regions that are not targeted by exome capture, the large amount of missing data limits SNP discovery. Recently, this approach was used by Jordan
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SNP discovery in pea: a powerful tool for academic research and breeding

SNP discovery in pea: a powerful tool for academic research and breeding

This work gives a comprehensive knowledge for the selection of choice subsets of SNP markers useful for polymorphism, mapping and hierarchical information purposes. These new resources publicly delivered by the PEAPOL project will thus help as tools in cumulating alleles at QTLs for traits of interest, directing the creation of new pea ideotypes adapted to various climates and cropping systems, with stabilized and high yields.

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SESAME (SEquence Sorter & AMplicon Explorer): genotyping based on high-throughput multiplex amplicon sequencing

SESAME (SEquence Sorter & AMplicon Explorer): genotyping based on high-throughput multiplex amplicon sequencing

Associate Editor: Martin Bishop ABSTRACT Summary: Characterizing genetic diversity through genotyping short amplicons is central to evolutionary biology. Next-generation sequencing (NGS) technologies changed the scale at which these type of data are acquired. SESAME is a web application package that assists genotyping of multiplexed individuals for several markers based on NGS amplicon sequencing. It automatically assigns reads to loci and individuals, corrects reads if standard samples are available and provides an intuitive graphical user interface (GUI) for allele validation based on the sequences and associated decision- making tools. The aim of SESAME is to help allele identification among a large number of sequences.
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Développement d'un système de traçabilité génétique chez le porc basé sur le séquençage de régions riches en SNP

Développement d'un système de traçabilité génétique chez le porc basé sur le séquençage de régions riches en SNP

Development of a genetic traceability system in pig based on the sequencing of SNP-rich regions Traceability is a major concern in food industry. Genetic markers such as single nucleotide polymorphisms (SNPs) can be used in the genetic traceability context. Two loci rich in SNPs were sequenced in 96 different individuals. Thirty-two significant SNPs were observed. All tested individuals presented a different genotype. In order to simplify the procedu- re, a hybrid amplicon joining the two regions of interest was generated. A traceability experiment based on this strategy was successfully performed allowing to identify the right couple « ear-meat ».
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Combining combinatorial optimization and statistics to mine high-throughput genotyping data

Combining combinatorial optimization and statistics to mine high-throughput genotyping data

4 Approche propos ´ee : Optimisation Combinatoire et Statistique L’objectif de ce travail est de d´efinir un mod`ele de pr´ediction des traits des animaux `a partir des mar- queurs g´en´etiques, utilisant `a la fois la puissance exploratoire des algorithmes d’optimisation combinatoire et la sp´ecificit´e des mod`eles statistiques de r´egression [ 1 ]. Nous choisissons comme premi`ere approche d’ef- fectuer une s´election d’attributs en combinant une m´ethode d’optimisation de type recherche locale avec une r´egression RIDGE. A chaque ´etape de la recherche locale, nous ´evaluons la s´election d’attributs `a l’aide d’un crit`ere de type CVE (Cross Validation Error) calcul´e sur les pr´edictions par un mod`ele de r´egression, pour au final converger vers une s´election d’un nombre r´eduit de SNP, et vers un mod`ele de r´egression sur ces SNP.
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Estimating and accounting for genotyping errors in RAD-seq experiments

Estimating and accounting for genotyping errors in RAD-seq experiments

l = 1, . . . , L. Let us further denote by g pi = {g pi1 , . . . , g piL } and by g p = {g p1 , . . . , g pn p }. The likelihood of the full data given per allele genotyping error rates  = { 1 , . . . ,  S } for different sets s = 1, . . . , S of genotype calls is then given by P(g|) =

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Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association

Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association

There are numerous plausible explanations for differential error processes in rare variant data. As is the case for single-marker tests of association, without good study designs which ensures random assignment of samples to sequencing centers, to individuals handling the samples, to sequencing machines, etc., genotyping errors can easily occur at different rates in the cases and controls. One particular area of concern is the increasing trend to use publicly available databases of controls. When using publicly available databases, there is no random assignment of cases and controls to sequencing pipelines, thus there are numerous ways that differential genotyping errors can be introduced. Further- more, even if publicly available datasets are simply being used to impute rare variants, the potential for differential genotyping imputation error exists. Similarly, when using a Bayesian prior based on the known MAF at the variant site to call rare genotypes there is a potential for differential genotyping errors when the Bayesian prior favors variants observed more frequently in the cases or controls.
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Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques

Réseaux bayésiens hiérarchiques avec variables latentes pour la modélisation des dépendances entre SNP: une approche pour les études d'association pangénomiques

Réseaux bayésiens hiérarchiques avec variables latentes pour les études d’association pangénomiques. distinguer l’influence d’un SNP de celle des autres SNP présents dans le FHCLMs. Outre ces deux applications des FHLCMs, ces modèles pourraient être aussi employés comme outils de visualisation du LD à l’aide de leur graphe, ou pour la modélisation des dépendances entre marqueurs génétiques liées à la structure de la population. Récemment, nous avons développé un premier algorithme d’apprentissage de FHLCMs pour la première application proposée, la réduction de dimension de données de GWAS (soumis).
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Routine Fetal Rhd Genotyping with Maternal Plasma: A Four-Year Experience in Belgium

Routine Fetal Rhd Genotyping with Maternal Plasma: A Four-Year Experience in Belgium

amplification of exons 4, 5, 10, and SRY, indicating that the fetus was a D+ male. Ct values of the amplifications were identical to those observed for the other D+ male fetuses (mean Ct of exons 4, 5, 10, and SRY were 40.4, 37.1, 38.7, and 38.8, respectively). The real-time PCR of all exons and SRY were negative in the amniotic cells. This discrepancy between invasive and noninvasive results invalidated fetal RHD genotyping from the maternal plasma. The patient's history told us that the patient had received a kidney transplant from a D+ male donor. In October 2005, a D- girl was born. It should be noted that the RHD and SRY were still found in the maternal plasma 3 days after birth.
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Transfusion support of autoimmune hemolytic anemia: how could the blood group genotyping help?

Transfusion support of autoimmune hemolytic anemia: how could the blood group genotyping help?

In the study of Shirey et al 12 who realized an extended typing of patients with AIHA, the typing could not be achieved (not possible) in 40% of the cases and autoad- sorption tests have been performed to find a potential al- loantibody. In contrast to genotyping, extended typing with serology often cannot be conducted for all patients because of the limited availability of certain antisera and prior transfusion history. Although RBC genotyp- ing cannot be performed in all immunohematology lab- oratories, as it is the case for adsorption techniques, these analyses can be accommodated by reference lab- oratories, which have a sufficiently high genotyping ac- tivity to treat emergency samples without delay.
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