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Dataset results
9 results for “Genotype Imputation”
Development of a machine learning model to predict non- durable response to anti-TNF therapy in Crohn's disease using transcriptome imputed from genotypes
<p>This is the expression value predicted using PrediXcan version 7 to find a gene feature that can distinguish between patients with and without effect on infliximab.</p> <p>Among the various tissue models provided by PrediXcan v7, three models were selected and used: whole blood, Colon transverse, and terminal ileum of small intestine, and the predicted gene counts of each model were 6,294, 5,612 and 3,107.</p> <p>For each of the three models, predicted gene expression values and phenotype information per sample were submitted.</p>
Comparison and Assessment of Family- and Population-based Genotype Imputation Methods in Large Pedigrees Dataset
<p>Here is the data corresponding to the paper "Comparison and Assessment of Family- and Population-based Genotype Imputation Methods in Large Pedigrees" submitted to Genome Research. The data includes the following:<br> <br> Simulated data for 1200 African and European subjects in pedigrees.<br> Lists of subjects selected by each of the 4 subject selection methods examined; Primus, GIGI-Pick, Exome-Picks, and Random selection. <br> <br> The positions of sparse markers for gl_auto for African and European data.<br> <br> The lists of GWAS SNPs for AFR and EUR. </p> <p>Please see the paper for further details of the data generation, this metadata will be updated following publication. </p>
Illumina HD genotypes for 3,092 cattle from Burkina Faso, Ghana, Nigeria and Tanzania for: "Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle"
<p>Raw HD data for Riggio et al. 2022: Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle</p> <p>This repository contains the raw Illumina HD genotypes (i.e., 777,962 SNPs) mapped to the bovine UMD3.1 genome assembly for 3,092 animals from four African countries (namely Burkina Faso, Ghana, Nigeria and Tanzania). </p>
Data from: Imputation of canine genotype array data using 365 whole-genome sequences improves power of genome-wide association studies
Genomic resources for the domestic dog have improved with the widespread adoption of a 173k SNP array platform and updated reference genome. SNP arrays of this density are sufficient for detecting genetic associations within breeds but are underpowered for finding associations across multiple breeds or in mixed-breed dogs, where linkage disequilibrium rapidly decays between markers, even though such studies would hold particular promise for mapping complex diseases and traits. Here we introduce an imputation reference panel, consisting of 365 diverse, whole-genome sequenced dogs and wolves, which increases the number of markers that can be queried in genome-wide association studies approximately 130-fold. Using previously genotyped dogs, we show the utility of this reference panel in identifying potentially novel associations, including a locus on CFA20 significantly associated with cranial cruciate ligament disease, and fine-mapping for canine body size and blood phenotypes, even when causal loci are not in strong linkage disequilibrium with any single array marker. This reference panel resource will improve future genome-wide association studies for canine complex diseases and other phenotypes.
Data from: Imputation of canine genotype array data using 365 whole-genome sequences improves power of genome-wide association studies
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Data from: Estimations of linkage disequilibrium, effective population size and ROH-based inbreeding coefficients in Spanish Churra sheep using imputed high-density SNP genotypes
In this study, the availability of the Ovine HD SNP BeadChip (HD-chip) and the development of an imputation strategy provided an opportunity to further investigate the extent of linkage disequilibrium (LD) at short distances in the genome of the Spanish Churra dairy sheep breed. A population of 1686 animals, including 16 rams and their half-sib daughters, previously genotyped for the 50K-chip, was imputed to the HD-chip density based on a reference population of 335 individuals. After assessing the imputation accuracy for beagle v4.0 (0.922) and fimpute v2.2 (0.921) using a cross-validation approach, the imputed HD-chip genotypes obtained with beagle were used to update the estimates of LD and effective population size for the studied population. The imputed genotypes were also used to assess the degree of homozygosity by calculating runs of homozygosity and to obtain genomic-based inbreeding coefficients. The updated LD estimations provided evidence that the extent of LD in Churra sheep is even shorter than that reported based on the 50K-chip and is one of the shortest extents compared with other sheep breeds. Through different comparisons we have also assessed the impact of imputation on LD and effective population size estimates. The inbreeding coefficient, considering the total length of the run of homozygosity, showed an average estimate (0.0404) lower than the critical level. Overall, the improved accuracy of the updated LD estimates suggests that the HD-chip, combined with an imputation strategy, offers a powerful tool that will increase the opportunities to identify genuine marker-phenotype associations and to successfully implement genomic selection in Churra sheep.
Data from: Estimations of linkage disequilibrium, effective population size and ROH-based inbreeding coefficients in Spanish Churra sheep using imputed high-density SNP genotypes
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Data from: Improving the resolution of canine genome-wide association studies using genotype imputation: a study of two breeds
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Summary Statistics - Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations
<p>Summary Statistics - Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations</p> <p>Files contain imputation quality and allele frequency for each imputed variant. A file exists for each method and add-on tag selection setting (Setting 1 or Setting 2)</p>
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