Last updated 28/1/2019
This analysis attempts to collect and analyse GWAS summary statistics for proteins on SomaLogic SOMAscan assays.
- Phenotypes. Rank-inverse Normal transformation of residuals from
ln(Relative protein abundance) ~ age + sex + PCs, e.g., viainvnormalfunction,
invnormal <- function(x) qnorm((rank(x,na.last="keep")-0.5)/sum(!is.na(x)))- Genotypes. Genechip and imputed genotypes with build37 (hg19) coordinates.
- Multiple linear regression for all samples with covariates.
- Additive genetic model.
- For case-control studies, please conduct analysis for cases and controls separately.
It is preferable to use software which account for genotype uncertainty, such as SNPTEST, QUICKTEST, and BOLT-LMM.
These are summaised in the following table, with missing values coded as “NA”.
| No. | Variable name | Description |
|---|---|---|
| 1 | SNPID | CHR:POS_A1_A2 (where A1<A2) or rsid |
| 2 | CHR | Chromosome number (1-22) |
| 3 | POS | Physical position for the reference sequence (please indicate NCBI build in descriptive file) |
| 4 | STRAND | Indicator of strand direction. Please specify “+” if positive or forward strand and “-” if negative or reverse strand. |
| 5 | N | Number of non-missing observations |
| 6 | EFFECT_ALLELE | Allele for which the effect (beta coefficient) is reported. For example, in an A/G SNP in which AA = 0, AG=1, and GG=2, the coded allele is G. |
| 7 | REFERENCE_ALLELE | Second allele at the SNP (the other allele). In the example above, the non-coded allele is A. |
| 8 | EAF | Effect allele frequency – “NA” if not available |
| 9 | BETA | Effect size for the coded allele, beta estimate from the genotype-phenotype association, with at least 5 decimal places. Note: if not available, please report “NA” for this variable. |
| 10 | SE | Standard error of the beta estimate, to at least 5 decimal places - “NA” if not available. |
| 11 | PVAL | p-value of Wald test statistic – “NA” if not available |
| 12 | RSQ | Residual phenotypic variance explained by SNP. “NA” if not available |
| 13 | RSQ_IMP | Observed divided by expected variance for imputed allele dosage. |
| 14 | IMP | Please specify whether the SNP was imputed or genotyped: 1: imputed SNP, 0: directly genotyped SNP |
Cohorts may opt to report sppedy results from PLINK with the following information, then it is desirable to provide SNP-level information:
| No | Name | Description | Additional comment |
|---|---|---|---|
| 1 | BP | Position in base pairs | |
| 2 | CHR | Chromosome | |
| 3 | SNP | CHR:POS_A1_A2 or rsid | |
| 4* | HWE | Hardy-Weinberg equilibrium P-value | |
| 5* | MAF | Minor allele frequency | Please indicate if this is the effect allele frequency |
| 6 | A1 | Allele 1 | Please indicate if this is the effect/reference allele |
| 7* | A2 | Allele 2 | Please indicate if this is the effect/reference allele |
| 8 | NMISS | Sample size | |
| 9 | BETA | Regression coefficient | |
| 10 | STAT | Regression test statistic | |
| 11 | P | P value |
* may be taken from the PLINK --hardy option and .bim file, see http://zzz.bwh.harvard.edu/plink/anal.shtml#glm.
In this case, please provide for each SNP information on strand, effect allele, effect allele frequency, and the information measures for imputation -- the information measure can be on the genotype level obtained once for a cohort rather than from phenotype-genotype regression through software such as SNPTEST. SNP and sample based statistics can be greatly facilitated with software qctool, http://www.well.ox.ac.uk/~gav/qctool_v2/, e.g.
qctool -g INTERVAL.bgen -s INTERVAL.sample -snp-stats -osnp INTERVAL.snp-stats -sample-stats -osample INTERVAL.sample-statsSee Appendix for a full example using SLURM.
When a dosage format is used, PLINK can also gives an INFO measure, see http://zzz.bwh.harvard.edu/plink/dosage.shtml.
This would include cohort-level summary statistics, brief description of the protein assays, genechips, quality controls, imputation. Please provide details of imputation panels.
A Google sheet has been set up to enable assembling of the information online.
Alternatively, an Excel spreadsheet is also available for download.
It is recommended to use format
- STUDY_analyst_SomaLogic_protein_SeqID_UniProtID_date.gz for SNP-based results. See the SomaLogic Master Table for SeqID and also https://www.uniprot.org/ for additional information on UniProt IDs.
- STUDY_analyst_SomaLogic_Descriptive_Information_date.txt for cohort level information.
Please contact us for details.
Meta-analysis will be performed centrally using the inverse-N weighted analysis of regression betas and standard errors, as implemented in the software METAL (https://github.com/statgen/METAL).
Genomic control and appropriate marker filters will be applied at this stage.
- Marker exclusion filters: we will apply imputation quality filters at the meta-analysis stage, so provide unfiltered results.
- Genomic control (GC): genomic control will be applied at the meta-analysis stage (single GC), so GC-correction is not needed for each cohort.
- Significance: the Bonferroni threshold for the genome-wide analyses is 5 x 10-11. The results will be replicated in independent cohorts.
FHS, INTERVAL, KORA, Malmo, QMDiab, TwinsUK.
Cotton RJ, Graumann J (2016) readat: An R package for reading and working with SomaLogic ADAT files. BMC Bioinformatics, https://doi.org/10.1186/s12859-016-1007-8, Bioconductor and bitbucket.
Stacey D, et al. (2018), ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci. Nucleic Acids Research, https://doi.org/10.1093/nar/gky837, GitHub and bioRxiv.
Sun BB, et al. (2018). Genomic atlas of the human plasma proteome. Nature 558: 73–79, SomaLogic plasma protein GWAS summary statistics, https://app.box.com/s/u3flbp13zjydegrxjb2uepagp1vb6bj2, EGA entry.
Notes: SERPINA1.R creates similar to RCircos version by Jimmy, and Methods.md is the Markdown version of the Methods section of the paper.
SLURM script for qctool 2.0.1
This is called with sbatch qctool.sb, where qctool.sb contains the following lines:
#!/bin/bash --login
#SBATCH -J qctool
#SBATCH -o qctool.log
#SBATCH -p long
#SBATCH -t 1-0:0
#SBATCH --export ALL
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8
export DIR=/scratch/bp406/data_sets/interval_subset_olink/genotype_files/unrelated_4994_pihat_0.1875_autosomal_typed_only
export INTERVAL=$DIR/interval_olink_subset_unrelated_4994_pihat_0.1875_autosomal_typed_only
ln -sf $INTERVAL.bgen INTERVAL.bgen
ln -sf $INTERVAL.sample INTERVAL.sample
# to obtain SNP-specific statistics as in .bgen and .sample format
qctool -g INTERVAL.bgen -s INTERVAL.sample -snp-stats -osnp INTERVAL.snp-stats
# Note in particular: the # option allows for chromosome-specific analysis; the -strand option will enable results in positive strand.The following script obtain SNP-statistics for each chromosome,
#!/bin/bash --login
#SBATCH -J qctool
#SBATCH -o qctool.log
#SBATCH -p long
#SBATCH -a 1-22
#SBATCH -t 1-0:0
#SBATCH --export ALL
#SBATCH --nodes=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=8
export DIR=/scratch/bp406/data_sets/interval_subset_olink/genotype_files/unrelated_4994_pihat_0.1875_autosomal_typed_only/per_chr
export INTERVAL=$DIR/interval_olink_subset_unrelated_4994_pihat_0.1875_autosomal_typed_only_chr_
export chr=$SLURM_ARRAY_TASK_ID
qctool -g ${INTERVAL}${chr}.bgen -s ${INTERVAL}${chr}.sample -snp-stats -osnp INTERVAL-${chr}.snp-stats