SNP genotyping of various breeds
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ABSTRACT:
PROVIDER: PRJNA508513 | ENA |
REPOSITORIES: ENA
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Project description:Proteomic genotyping is the use of genetically variant peptides (GVPs), detected in a forensic protein sample, to infer the genotype of corresponding non-synonymous SNP alleles in the donor’s genome. This process does not depend on the presence of accessible or useable DNA in a sample. This makes proteomic genotyping an attractive alternative for analysis of problematic forensic samples, such as hair shafts, degraded bones or teeth, fingermarks, or sexual assault evidence. To demonstrate the concept in hair shafts, we developed an optimized sample processing protocol that could be used with high effectiveness on single hairs. This allows us to determine if the detected profiles of genetically variant peptides are robust and result in a consistent profile of inferred SNP alleles regardless of the chemical or biological history of the sample. Several real world scenarios have been evaluated. Here we include a study of four European subjects that had both pigmented and non-pigmented (or gray and non-gray) hair shafts. We tested whether (a) protein profiles change as a result of the loss of pigmentation and (b) these changes were reflected in the inferred genotype derived from detection of genetically variant peptides. Using this information, we can determine whether the resulting GVP profiles are more dependent on the biological context of pigmentation status or the underlying genotype.
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Project description:SNP genotyping was used to determine if the free living Highland Wild dogs of Papua, Indonesia are the ansestors of captive New Guinea Singing Dogs.
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Project description:SNP genotyping of dogs Patagonian Sheepdogs
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Project description:Purpose: The purpose of this study was to evaluate SNP genotyping methodology as a means to detect chromosomal abnormalities previously diagnosed by G-band karyotype or fluorescence in situ hybridization (FISH) analysis and to determine the frequency of sub-microscopic (cryptic) chromosomal alterations in these subjects. Methods: We used the Illumina HumanHap Beadchip platform to genotype 40 individuals having previously detected chromosomal anomalies (by G-banded and/or FISH analysis). The resulting data were analyzed for signal intensity (log R ratio) and allelic composition (B allele frequency). Results: SNP array analysis detected 100% of previously identified cytogenetic abnormalities. Changes or clarifications of the ISCN karyotype designation assigned by conventional cytogenetic and/or FISH analysis were made in 82 % of the cases (32 of 39). Nine of the 39 cases (23%) involved a reassignment of an abnormal band while an additional 9 of the 39 (23%) resulted in a clarification of a sub-band assignment. In 8 more of the 39 cases (21%) the previously reported alterations were confirmed, however the SNP analysis also identified related cryptic alterations. SNP analysis not only confirmed FISH-detected abnormalities but also more precisely mapped the breakpoints of 6/6 patients. Investigations into the origin of de novo abnormalities in 15 trio families established that 12 /15 occurred on the paternal chromosome. Conclusions: SNP genotyping array analysis, confirmed all previously detected structural chromosomal abnormalities and provided additional, clinically-relevant genomic information in 82% of these alterations.
2011-02-22 | GSE20623 | GEO
Project description:Estimating the relationships between individuals is one of the fundamental challenges in many fields. In particular, relationship estimation could provide valuable information for missing persons cases. The recently developed investigative genetic genealogy approach uses high-density single nucleotide polymorphisms (SNPs) to determine close and more distant relationships, in which hundreds of thousands to tens of millions of SNPs are generated either by microarray genotyping or whole-genome sequencing. The current studies usually assume the SNP profiles were generated with minimum errors. However, in the missing person cases, the DNA samples can be highly degraded, and the SNP profiles generated from these samples usually contain lots of errors. In this study, a robust machine learning approach was developed for estimating the relationships with high error SNP profiles. In this approach, a hierarchical classification strategy was employed first to classify the relationships by degree and then the relationship types within each degree separately. As for each classification, feature selection was implemented to gain better performance. Both simulated and real data sets with various genotyping error rates were utilized in evaluating this approach, and the accuracies of this approach were higher than individual measures; namely, this approach was more accurate and robust than the individual measures for SNP profiles with genotyping errors. In addition, the highest accuracy could be obtained by providing the same genotyping error rates in train and test sets, and thus estimating genotyping errors of the SNP profiles is critical to obtaining high accuracy of relationship estimation.
2022-07-27 | GSE209804 | GEO
Project description:This study used the CanineHD genotyping array to investigate copy number variants in the dog genome in a total of 351 samples from 30 different breeds.
2014-03-28 | GSE55134 | GEO