Complex Feline Disease Mapping Using a Dense Genotyping Array.
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ABSTRACT:
SUBMITTER: Hernandez I
PROVIDER: S-EPMC9244801 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96-98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at ?20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses.
| S-EPMC4498655 | biostudies-literature
Project description:BackgroundA whole-genome genotyping array has previously been developed for Malus using SNP data from 28 Malus genotypes. This array offers the prospect of high throughput genotyping and linkage map development for any given Malus progeny. To test the applicability of the array for mapping in diverse Malus genotypes, we applied the array to the construction of a SNP-based linkage map of an apple rootstock progeny.ResultsOf the 7,867 Malus SNP markers on the array, 1,823 (23.2%) were heterozygous in one of the two parents of the progeny, 1,007 (12.8%) were heterozygous in both parental genotypes, whilst just 2.8% of the 921 Pyrus SNPs were heterozygous. A linkage map spanning 1,282.2 cM was produced comprising 2,272 SNP markers, 306 SSR markers and the S-locus. The length of the M432 linkage map was increased by 52.7 cM with the addition of the SNP markers, whilst marker density increased from 3.8 cM/marker to 0.5 cM/marker. Just three regions in excess of 10 cM remain where no markers were mapped. We compared the positions of the mapped SNP markers on the M432 map with their predicted positions on the 'Golden Delicious' genome sequence. A total of 311 markers (13.7% of all mapped markers) mapped to positions that conflicted with their predicted positions on the 'Golden Delicious' pseudo-chromosomes, indicating the presence of paralogous genomic regions or mis-assignments of genome sequence contigs during the assembly and anchoring of the genome sequence.ConclusionsWe incorporated data for the 2,272 SNP markers onto the map of the M432 progeny and have presented the most complete and saturated map of the full 17 linkage groups of M. pumila to date. The data were generated rapidly in a high-throughput semi-automated pipeline, permitting significant savings in time and cost over linkage map construction using microsatellites. The application of the array will permit linkage maps to be developed for QTL analyses in a cost-effective manner, and the identification of SNPs that have been assigned erroneous positions on the 'Golden Delicious' reference sequence will assist in the continued improvement of the genome sequence assembly for that variety.
| S-EPMC3410780 | biostudies-literature
Project description:Background and aimGlobally, toxoplasmosis is an important zoonotic parasite infection of many warm-blooded animals (including humans). Toxoplasma gondii oocysts are widespread, and their contamination can be primarily attributed to the members of the Felidae family. This study aimed to estimate the prevalence and determine the dense granule antigen 6 (GRA6) genotype of T. gondii among domestic cats in the Phayao Province, Thailand.Materials and methodsA total of 124 fecal samples were collected from owned cats in the Muang district, Phayao, Thailand, from January to December 2020. Fecal samples were tested for the presence of T. gondii DNA using targeted B1 gene polymerase chain reaction (PCR) amplification, and positive samples were subsequently analyzed for their T. gondii genotype through PCR-restriction fragment length polymorphism (RFLP) analysis and sequencing of the GRA6 gene.ResultsAmong the 124 samples, 46 (37.1%) were tested positive for T. gondii. Only 10 positive DNA samples were successfully amplified for the GRA6 marker. Subsequent PCR-RFLP and sequence analyses indicated that all T. gondii isolates from cats in Phayao belonged to GRA6 genotype I.ConclusionData revealed that toxoplasmosis is remarkably distributed among (studied) domestic cats in Phayao, Thailand. Moreover, the virulent GRA6 allele was found to be circulated among domestic cats in this area. However, no significant correlation was observed between infection rates and different risk factors, which indicated that pet cats of any age, gender, or breed have similar risks of being infected with T. gondii. Our results further suggested that infective oocysts of T. gondii are widely distributed and that environmental contamination with these oocysts will introduce more risks of disease transmission to humans and other animals.
| S-EPMC9631367 | biostudies-literature
Project description:Chromosomal inversion polymorphisms have special importance in the Anopheles gambiae complex of malaria vector mosquitoes, due to their role in local adaptation and range expansion. The study of inversions in natural populations is reliant on polytene chromosome analysis by expert cytogeneticists, a process that is limited by the rarity of trained specialists, low throughput, and restrictive sampling requirements. To overcome this barrier, we ascertained tag single nucleotide polymorphisms (SNPs) that are highly correlated with inversion status (inverted or standard orientation). We compared the performance of the tag SNPs using two alternative high throughput molecular genotyping approaches vs. traditional cytogenetic karyotyping of the same 960 individual An. gambiae and An. coluzzii mosquitoes sampled from Burkina Faso, West Africa. We show that both molecular approaches yield comparable results, and that either one performs as well or better than cytogenetics in terms of genotyping accuracy. Given the ability of molecular genotyping approaches to be conducted at scale and at relatively low cost without restriction on mosquito sex or developmental stage, molecular genotyping via tag SNPs has the potential to revitalize research into the role of chromosomal inversions in the behavior and ongoing adaptation of An. gambiae and An. coluzzii to environmental heterogeneities.
| S-EPMC7467005 | biostudies-literature
Project description:BackgroundPooling genomic DNA samples within clinical classes of disease followed by genotyping on whole-genome SNP microarrays, allows for rapid and inexpensive genome-wide association studies. Key to the success of these studies is the accuracy of the allelic frequency calculations, the ability to identify false-positives arising from assay variability and the ability to better resolve association signals through analysis of neighbouring SNPs.ResultsWe report the accuracy of allelic frequency measurements on pooled genomic DNA samples by comparing these measurements to the known allelic frequencies as determined by individual genotyping. We describe modifications to the calculation of k-correction factors from relative allele signal (RAS) values that remove biases and result in more accurate allelic frequency predictions. Our results show that the least accurate SNPs, those most likely to give false-positives in an association study, are identifiable by comparing their frequencies to both those from a known database of individual genotypes and those of the pooled replicates. In a disease with a previously identified genetic mutation, we demonstrate that one can identify the disease locus through the comparison of the predicted allelic frequencies in case and control pools. Furthermore, we demonstrate improved resolution of association signals using the mean of individual test-statistics for consecutive SNPs windowed across the genome. A database of k-correction factors for predicting allelic frequencies for each SNP, derived from several thousand individually genotyped samples, is provided. Lastly, a Perl script for calculating RAS values for the Affymetrix platform is provided.ConclusionOur results illustrate that pooling of DNA samples is an effective initial strategy to identify a genetic locus. However, it is important to eliminate inaccurate SNPs prior to analysis by comparing them to a database of individually genotyped samples as well as by comparing them to replicates of the pool. Lastly, detection of association signals can be improved by incorporating data from neighbouring SNPs.
| S-EPMC1262713 | biostudies-literature
Project description:ObjectiveThe admixture of domestic pig into wild boar populations is controlled until now, by cytogenetic analysis. Even if a first-generation hybrid animal is discernable because of its 37-chromosome karyotype, the cytogenetic method is not applicable in the case of advanced intercrosses. The aim of this study is therefore to evaluate the use of SNP (Single Nucleotide Polymorphism) markers as an alternative technology to characterize recent or past hybridization between the two sub-species. The final goal would be to develop a molecular diagnostic tool.Data descriptionThe Geneseek Genomic Profiler High-Density porcine beadchip (GGP70KHD, Illumina, USA), comprising 68,516 porcine SNPs, was used on a set of 362 wild boars with diverse chromosomal statuses collected from different areas and breeding environments in France. We generated approximately 62,192-64,046 genotypes per wild boar. The present dataset might be useful for the community (i) for developing molecular tools to evaluate the admixture of domestic pig into wild boar populations, and (ii) for genetic diversity studies including wild boar species or phylogeny analyses of Suidae populations. Raw data files and a processed matrix data file were deposited in the ArrayExpress at European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) data portal under accession number E-MTAB-10591.
| S-EPMC9102940 | biostudies-literature
Project description:MotivationMicroarrays provide an accurate and cost-effective method for genotyping large numbers of individuals at high resolution. The resulting data permit the identification of loci at which genetic variation is associated with quantitative traits, or fine mapping of meiotic recombination, which is a key determinant of genetic diversity among individuals. Several issues inherent to short oligonucleotide arrays -- cross-hybridization, or variability in probe response to target -- have the potential to produce genotyping errors. There is a need for improved statistical methods for array-based genotyping.ResultsWe developed ssGenotyping (ssG), a multivariate, semi-supervised approach for using microarrays to genotype haploid individuals at thousands of polymorphic sites. Using a meiotic recombination dataset, we show that ssG is more accurate than existing supervised classification methods, and that it produces denser marker coverage. The ssG algorithm is able to fit probe-specific affinity differences and to detect and filter spurious signal, permitting high-confidence genotyping at nucleotide resolution. We also demonstrate that oligonucleotide probe response depends significantly on genomic background, even when the probe's specific target sequence is unchanged. As a result, supervised classifiers trained on reference strains may not generalize well to diverged strains; ssG's semi-supervised approach, on the other hand, adapts automatically.
| S-EPMC2666814 | biostudies-literature
Project description:BACKGROUND: Chromosomal imbalances are a major cause of developmental defects as well as cancer and often constitute the key in identification of novel disease related genes. Classical cytogenetic methods are limited in resolution and dependent on highly skilled labour, while methods with higher resolution, based on molecular cytogenetics approaches such as matrix CGH, are not widely available. METHODS: We have developed and evaluated a method we term "molecular karyotyping", using readily available and easy to handle oligonucleotide arrays originally designed for parallel genomewide analysis of over 10,000 SNPs. We show that we can easily and reliably detect unbalanced chromosomal aberrations of various sizes from as little as 250 ng of DNA on a single microarray, based on fluorescence intensity information from clusters of SNPs. RESULTS: We determined the resolution of this method through analysis of 20 trios with 21 previously confirmed subtle aberrations sizing between 0.2 and 13 Mb. Duplications and deletions of at least 5 Mb in size were reliably detectable, but detection of smaller aberrations was dependent on the number of SNPs they contained, thus seven of 10 different deletions analysed, with sizes ranging from 0.2 to 3.7 Mb, were not detectable due to insufficient SNP densitiy in the respective region. CONCLUSIONS: Deduction of reliable cut off levels for array peaks in our series of well characterised patients allows the use of the GeneChip Mapping 10K SNP array for performing rapid molecular karyotyping from small amounts of DNA for the detection of even subtle deletions and duplications with high sensitivity and specificity.
| S-EPMC1735639 | biostudies-other
Project description:Single nucleotide polymorphisms (SNPs) of genes that affect cytokine production and function are known to influence the susceptibility and progression of immune-related conditions such as infection, autoimmune diseases, transplantation, and cancer. We established a multiplex genotyping method to analyze the SNPs of cytokine genes by combining the multiplex PCR and bead array platform. Thirteen cytokine gene regions, including 20 SNPs, were amplified, and allele-specific primer extension was performed in a single tube. High-quality allele-specific primers were selected for signals greater than 1000 median fluorescence intensity (MFI) for positive alleles, and less than 500 MFI for negative alleles. To select and improve the extension primers, modifications for the reverse direction, length or refractory were performed. 24 primers in the forward or reverse direction step and 12 primers in length or refractory modifications were selected and showed high concordance with results by nucleotide sequencing. Among the 13 candidate cytokine genes, the SNPs of 12 cytokine genes, including IL-1?, IL-1R, IL-1RA, IL-1?, IL-2, IL-4, IL-4R?, IL-6, IL-10, IL-12, TGF-?1, and TNF-?, were successfully defined with the selected allele-specific primers in healthy Korean subjects. Our genotyping system provides a fast and accurate detection for SNPs of multiple cytokine genes to investigate their association with immune-related diseases and transplantation outcomes.
| S-EPMC4331502 | biostudies-literature
Project description:BACKGROUND:Insertions/deletions (InDels) and more specifically presence/absence variations (PAVs) are pervasive in several species and have strong functional and phenotypic effect by removing or drastically modifying genes. Genotyping of such variants on large panels remains poorly addressed, while necessary for approaches such as association mapping or genomic selection. RESULTS:We have developed, as a proof of concept, a new high-throughput and affordable approach to genotype InDels. We first identified 141,000 InDels by aligning reads from the B73 line against the genome of three temperate maize inbred lines (F2, PH207, and C103) and reciprocally. Next, we designed an Affymetrix® Axiom® array to target these InDels, with a combination of probes selected at breakpoint sites (13%) or within the InDel sequence, either at polymorphic (25%) or non-polymorphic sites (63%) sites. The final array design is composed of 662,772 probes and targets 105,927 InDels, including PAVs ranging from 35?bp to 129kbp. After Affymetrix® quality control, we successfully genotyped 86,648 polymorphic InDels (82% of all InDels interrogated by the array) on 445 maize DNA samples with 422,369 probes. Genotyping InDels using this approach produced a highly reliable dataset, with low genotyping error (~?3%), high call rate (~?98%), and high reproducibility (>?95%). This reliability can be further increased by combining genotyping of several probes calling the same InDels (<?0.1% error rate and?>?99.9% of call rate for 5 probes). This "proof of concept" tool was used to estimate the kinship matrix between 362 maize lines with 57,824 polymorphic InDels. This InDels kinship matrix was highly correlated with kinship estimated using SNPs from Illumina 50?K SNP arrays. CONCLUSIONS:We efficiently genotyped thousands of small to large InDels on a sizeable number of individuals using a new Affymetrix® Axiom® array. This powerful approach opens the way to studying the contribution of InDels to trait variation and heterosis in maize. The approach is easily extendable to other species and should contribute to decipher the biological impact of InDels at a larger scale.
| S-EPMC6854671 | biostudies-literature