Last updated: 2019-08-21

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Knit directory: Comparative_eQTL/analysis/

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Welcome to my research website.

QC

Association testing with various models

  • first iteration: description: lmm with KING-robust GRM thresholded at 0, and 3 genotype PCs
  • Check residuals after regressing out some covariates
  • second iteration: description: lm with 5 genotype PCs (PCs 4 and 5 takes into account some first hand relatedness) and more stringent genotype filtering. Also, outlier sample MD_And dropped from analysis
  • Third iteration
  • fourth iteration: description, lmm with 4 PCs and 3 Genotype PCs, used STAR RNA-seq CPM for less outliers. Fixed big bug that was permuting samples, resulting in no true hits in previous iterations. Here I used standardization and qqnorm.

Conservation and GO/GSEA analysis

  • GO analysis, FDR=0.1 overlap enrichment analysis of eGenes across humans and chumps, and gene ontology analysis of eGenes based on eGene classification defined at FDR=0.1 threshold. Also, a set of similar analyses using GSEA methodology, which is based on relative ranking of eGenes between species.
  • Conservation analysis, FDR=0.1 analysis of conservation of coding sequence (percent identity and dN/dS) based on eGene classification defined at FDR=0.1
  • Conservation analysis, HumanTop600_eGenes analysis of conservation of coding sequence (percent identity and dN/dS) based on eGene classification defined at FDR=0.1 for chimp and top600 qvalue genes for human.
  • Tissue sharing, both FDR=0.1 and Top600 analysis of number of GTEx tissues eGenes are detected in for shared and species specific eGenes. As before, I classify species specific and shared eGenes using either FDR=0.1 for both species, or using FDR-0.1 for chimp and top600 qvalue for human.
  • Conservation analysis, most high-variance genes analysis of conservation of coding sequence (percent identity and dN/dS) based on within species variance of expression. A useful comparison for the similar analysis above.
  • More thourough analyses looking at gene variance within and between species after adjusting for expression.

Power analysis for inter-species differential expression

  • PowerAnalysisFromOrinalDataset DE gene analysis based on subsampling ~39 chimp samples & 50 human samples (mostly GTEx)… Note that there are some outlier samples that I want to purge in later iterations of this analysis.

Differential contacts

  • Do differential DNA contacts between species explain differences in eGene character between species.
  • First crude analysis In this analysis I asked whether the there is a correlation between the rank difference in eGenes between species, and the difference in the sum of contacts in each species’ cis-window for each gene
  • There was a slight but significant correlation, but I worry about a potential bias introduced by the fact that eGene significance for humans was based on cis-eQTL calling with a 1MB window and chimp was a 250kB window. I should control for this.
  • Check that human lead snps in 250kB window are reasonable As expected, the p-value for lead snps within 250kb is well correlated for the eGene p-value, with the exception that there are many genes with much lower eGene pvalues probably because there is a better SNP >250kb away
  • After controlling for cis-window-size

Shared polymorphic loci

  • QQ Plot of species shared polymorphisms Of all the shared polymorphic loci (putative targets of balancing selection), do they have inflated cis-eQTL P-values compared to a random set of snp-gene tests.