Project description:Renal tumors with complex morphology require extensive workup for accurate classification. Chromosomal aberrations that define subtypes of renal epithelial neoplasms have been reported. We explored if whole-genome chromosome copy number and loss-of-heterozygosity analysis with single nucleotide polymorphism (SNP) arrays can be used to identify these aberrations in cases where morphology was unable to definitively classify these tumors. Keywords: Chromosome copy number and LOH analysis (virtual karyotyping) with SNP Genotyping Arrays Keywords: Genome variation profiling by SNP array
Project description:Renal tumors with complex morphology require extensive workup for accurate classification. Chromosomal aberrations that define subtypes of renal epithelial neoplasms have been reported. We explored if whole-genome chromosome copy number and loss-of-heterozygosity analysis with single nucleotide polymorphism (SNP) arrays can be used to identify these aberrations. Keywords: Chromosome copy number and LOH analysis with SNP Genotyping Arrays
Project description:Genome-wide SNP genotyping array can genotyped SNP highthroughly. It can be used in many aspects, such as phylogeny relationships, genome-wide association studies, copy number identification.
Project description:Chromosomal abnormalities have been identified in some individuals with Autism Spectrum Disorder (ASD), but their full etiologic role is unknown. Submicroscopic copy number variation (CNV) represents a considerable source of genetic variation in the human genome that contributes to phenotypic differences and disease susceptibility. To explore the contribution CNV imbalances in ASD, we genotyped unrelated ASD index cases using the Affymetrix GeneChip® 500K single nucleotide polymorphism (SNP) mapping array. Keywords: Whole Genome Mapping SNP Genotyping Array
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.
Project description:To accelerate genetic studies in sugarcane, an Axiom Sugarcane100K single nucleotide polymorphism (SNP) array was designed and customized in this study. Target enrichment sequencing 300 sugarcane accessions selected from the world collection of sugarcane and related grass species yielded more than four million SNPs, from which a total of 31,449 single dose (SD) SNPs and 68,648 low dosage (33,277 SD and 35,371 double dose) SNPs from two datasets respectively were selected and tiled on Affymetrix Axiom SNP array. Most of selected SNPs (91.77%) were located within genic regions (12,935 genes), with an average of 7.1 SNPs/gene according to sorghum gene models. This newly developed array was used to genotype 469 sugarcane clones, including one F1 population derived from cross between Green German and IND81-146, one selfing population derived from CP80-1827, and 11 diverse sugarcane accessions as controls. Results of genotyping revealed a high polymorphic SNP rate (77.04%) among the 469 samples. Three linkage maps were constructed by using SD SNP markers, including a genetic map for Green German with 3,482 SD SNP markers spanning 3,336 cM, a map for IND81-146 with 1,513 SD SNP markers spanning 2,615 cM, and a map for CP80-1827 with 536 SD SNP markers spanning 3,651 cM. Quantitative trait loci (QTL) analysis identified a total of 18 QTLs controlling Sugarcane yellow leaf virus resistance segregating in the two mapping populations, harboring 27 disease resistant genes. This study demonstrated the successful development and utilization of a SNP array as an efficient genetic tool for high throughput genotyping in highly polyploid sugarcane.
Project description:We examined six pairs of monozygotic twins discordant (MZD) for schizophrenia and identified copy number variation (CNV) and single nucleotide polymorphism (SNP) differences between affected and unaffected co-twins using the Affymetrix Genome Wide SNP 6.0. Affymetrix SNP arrays were performed according to the manufacurer's protocol on DNA extracted from whole blood CNV analysis was done using Affymetrix Genotyping Console 4.0 and Partek Genotyping Suite
Project description:Comparison of Genotyping using pooled DNA samples (Allelotyping) and Individual Genotyping using the Affymetrix Genome-Wide Human SNP Array 6.0 In this study, data from 100 DNA samples individually genotyped with the Affymetrix Genome-Wide Human SNP Array 6.0 were used to estimate the error of the pooling approach by comparing the results with those obtained using the same array type but DNA pools each composed of 50 of the same samples. Newly developed and established methods for signal intensity correction were applied. Furthermore, the relative allele intensity signals (RAS) obtained by allelotyping were compared to the corresponding values derived from individual genotyping. Similarly, differences in RAS values between pools were determined and compared.
Project description:Sorghum (Sorghum bicolor L. Moench) is a C4 species sensitive to the cold spring conditions that occur at northern latitudes, usually coupled with excessive light, and that greatly affects the photosynthetic rate. The objective of this study was to discover genes/genomic regions that control the capacity to cope with excessive energy under low temperature conditions during the vegetative growth period. A genome-wide association study (GWAS) was conducted for eight photosynthesis and chlorophyll fluorescence traits under three consecutive temperature treatments: control (28°C/24°C), cold (15°C/15°C) and recovery (28°C/24°C). Cold stress significantly reduced the photosynthetic capacity of sorghum plants and a total of 204 genomic regions were discovered associated with at least one trait in a particular treatment or in the time integrated response to cold. If no GBS markers were available for the targeted candidate genes, new SNPs were developed and genotyped using a SNPtype™ Assay (Fluidigm) on the Fluidigm BioMarkHD system and GT 96.96 Dynamic Array Integrated Fluidic Circuits of Fluidigm.