Post-transcriptional regulation
Last updated: 2022-06-15
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Knit directory: funcFinemapping/
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Assessing the utility of novel annotations
In this analysis, we aim to assess how much disease heritability can be explained by post-transcritional regulation processes. GWAS SNPs were first annotated by either functional data or computational predictions. For each annotation category, S-LDSC computed partitioned LD scores using referencel panel and then regressed \(\chi^2\) statistics against them to estimate the coefficient \(\hat\tau\) per category. This estimate would be further used to compute heritability.
Several post-transcriptonal features will be explored:
- alternative splicing
- RNA modification: m6A
- RNA binding
- Polyadenylation
Method(S-LDSC)
Technical details:
HapMap3 SNPs (~1.2M): used as a proxy for well imputed SNPs and the regression SNPs in LDSC. They were generated by the international HapMap project on a collection of 1301 samples from a variaty of human populations.[https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html]
Reference panel SNPs (~9M): the set of 1000G SNPs with MAF > 5% in ~500 Euproean samples
LDSC inputs/outputs:
.M/.l2.M_5_50: The .M file contains the total number of SNPs; the .l2.M_5_50 file contains the number of SNPs with minor allele frequency above 5%. By default, ldsc uses common SNPs (MAF > 5%) to estimate per SNP heritability, which is different for rare variants.
freqfile/w-ld-chr: computed on European of Phase 3 of 1kg Genomes
Regression weights: used to correct for non-independence and heteroskedasticity among the \(\chi^2\) statistics.
Regression coefficients: Quoted from the website that "They measure the additional contribution of one annotation to the model and are interpretable for both binary and continuous annotations"
Outputs
The third column corresponds to the proportion of heritability explained.
Category Prop._SNPs Prop._h2 Prop._h2_std_error Enrichment
1 m6a 0.006208692 0.052966389 0.010838776 8.5310061
2 neuOCR 0.093030734 0.275791186 0.037290677 2.9645169
3 DMR 0.049493563 0.324376410 0.035765978 6.5539110
4 spliceai_binary0.01 0.004887305 -0.002269065 0.009825267 -0.4642773
5 spliceai_binary0.03 0.001713928 -0.005219552 0.007272860 -3.0453734
6 spliceai_binary0.05 0.000992760 -0.005691005 0.006544050 -5.7325086Results
test annotations one at a time
Schizophrenia
baseline model: baselineLD-v1.1 annotations: spidex, spliceai, fetal brain m6a, neuronal OCR, differentially methylated regions in brains (DMR)
Legend:
- x-axis - annotations
- y-axis - fold of enrichment
- label on the plot - percent of narrow-sense heritability
- dashed line - no enrichment
Examine baseline annotations
A list of annotations with >1.5 fold of enrichment were listed. GERP.NS is suspicious as both proportion of SNPs in category and heritability exceeds one. Besides that, we see heritability is mainly explained by conservation and transcriptional regulation.
test annotation:spliceai score >=0.03
f<-read.table("output/splicing/scz_spliceai_binary0.03.results", header = T)
plot_ldsc_enrichment(f[f$Enrichment>=1.5 & f$Enrichment<10 , ], tolabel = TRUE) 
Across multiple traits
baseline model: baselineLD-v1.1
spidex annotations were not included.
test annotations jointly
splicing predictions diagnosis
SCZ
A left-skewed p-value distribution for SNPs with spliceAI >=0.03
p-value distributions for SNPs with spliceAI>=0.03 p-values against spliceAI scores
Note: Very few SNPs with high spliceAI scores also have significant p-values.
QQ plot
QQ plots for SCZ GWAS SNPs with spliceAI>=0.03
Note: There is not much signal in SNPs that have spliceAI scores above threshold.
AFib
QQ plot
QQ plots for aFib GWAS SNPs with spliceAI>=0.03
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
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attached base packages:
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