results
Last updated: 2021-04-14
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Knit directory: funcFinemapping/
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Fine-mapping with functional annotations as priors has shown improved results in identifying causal variants. This project is to evaluate the utility of novel annotation features and adopt ones that can improve fine mapping results.
Evaluation
GWAS summary statistics
Schizopherenia - Pardinas et al., 2018
- 40675 cases and 64643 controls
- CLOZUK sample + PGC sample (independent)
- 179 independent GWAS significant SNPs mapped to 145 independent loci
- SNPs were imputed using a combination of the 1KGPp3 and UK 10K datasets.
- SNPs were filtered by NFO > 0.6 and MAF > 0.01
- LD-score regression analysis: An LD reference was generated from 1KGPp3 after restricting this dataset to strictly unrelated individuals and retaining only markers with MAF > 0.01.
GWAS QC Procedures
- Current procedures was based on Alan's finemappeR pipeline
- Criteria for filtering gwas SNPs
- Remove all non-biallelic SNPs
- Remove all SNPs with strand-ambiguous alleles (SNPs with A/T, C/G alleles)
- Removed SNPs without rs IDs, duplicated rs IDs or base pair position.
- Removed SNPs not in the reference panels
- Removed SNPs whose base pair positions or alleles doesn’t match the reference panels
- Removed all SNPs on chromosome X, Y, and MT
After filtering, there are around 6 million variants remained.
Plots for GWAS summary statistics
Features
- Sequence constraints:
- context-dependent tolerance scores(CDTS) in percentiles
- A score was computed for each 10bp bin in the genome.
- The lower the score is, the more intolerant to variation is the bin.
Procedures
- GWAS summary statistics was pre-processed to remove sex chromosomes, indels, ambiguous and duplicated SNPs.
- Currently, genotypes from 1kg European samples are used to compute LD between SNPs.
- SNPs in GWAS summary statistics were matched with the reference panel and assigned to in total 1687 independent LD blocks.
- Run TORUS to perform genome-wide enrichment analyses.
Results
All variants were catogrized into whether or not they occur in genomic bins with CDTS up to 1 percentile or 5 percentile.
Examine the CDTS feature
check the proportion of variants with high sequencing constraints that also have functional annotations in brain
| iN_Dopa | iN_GABA | iN_Glut | iPSC | NPC | Any_OCR | |
|---|---|---|---|---|---|---|
| CDTS_1% | 53.7% | 62.6% | 53.2% | 70.8% | 51.9% | 76.4% |
| CDTS_5% | 24.4% | 27.8% | 21.6% | 33.9% | 20.6% | 40.4% |
Check the percent of constrained sequences that overlaps with open chromatin regions from neurons
| iN_Dopa | iN_GABA | iN_Glut | iPSC | NPC | |
|---|---|---|---|---|---|
| CDTS_1% | 46.9% | 55.6% | 45.4% | 62.2% | 44.2% |
| CDTS_5% | 18% | 21.3% | 17.4% | 23.8% | 16.9% |
Enrichment analysis for sequence constraints 
The enrichment estimate has a confience level above zero for CDTS and positive controls. This shows SNPs associated with SCZ are on average ~ 9 fold enriched in genomic bins with up to 5 percentile of CDTS.
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
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.3 knitr_1.31 data.table_1.14.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.6 pillar_1.5.0 compiler_4.0.4 bslib_0.2.4
[5] later_1.1.0.1 jquerylib_0.1.3 git2r_0.28.0 highr_0.8
[9] tools_4.0.4 digest_0.6.27 gtable_0.3.0 jsonlite_1.7.2
[13] evaluate_0.14 lifecycle_1.0.0 tibble_3.0.6 pkgconfig_2.0.3
[17] rlang_0.4.10 DBI_1.1.1 yaml_2.2.1 xfun_0.21
[21] withr_2.4.1 dplyr_1.0.4 stringr_1.4.0 generics_0.1.0
[25] fs_1.5.0 vctrs_0.3.6 sass_0.3.1 tidyselect_1.1.0
[29] rprojroot_2.0.2 grid_4.0.4 glue_1.4.2 R6_2.5.0
[33] fansi_0.4.2 rmarkdown_2.7 farver_2.0.3 purrr_0.3.4
[37] magrittr_2.0.1 whisker_0.4 scales_1.1.1 promises_1.2.0.1
[41] ellipsis_0.3.1 htmltools_0.5.1.1 assertthat_0.2.1 colorspace_2.0-0
[45] httpuv_1.5.5 labeling_0.4.2 utf8_1.1.4 stringi_1.5.3
[49] munsell_0.5.0 crayon_1.4.1