Last updated: 2021-03-05
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All RSS methods to date require the input of GWAS summary statistics and ancestry-matching LD estimates. Some RSS methods further require the input of genomic annotations.
This section is modified from Box 1 of Winkler (2014).
The following columns are required for any RSS analysis:
snp: identifier of genetic variant, character string such as rs12498742;chr: autosome number of genetic variant such as chr1,…,chr22;pos: physical position, in base pair, of genetic variant;a1: effect allele, a single upper case character A, C, G or T;a2: the other (non-effect) allele, a single upper case character A, C, G or T;betahat: estimated effect size of genetic variant under the single-marker model;se: estimated standard error of betahat.The following columns are optional, but they can be helpful for sanity checks:
strand: strand on which the alleles are reported, a single character - or +;n: number of individuals analyzed (i.e., sample size) for the genetic variant;maf: minor allele frequency, numeric between 0 and 1;p: p-value of genetic variant association, numeric between 0 and 1;info: other information (e.g., imputation quality) about genetic variants.It is crucial to make sure that [a1, betahat, se] are consistently defined. Below is a toy example. Consider two SNPs (rs1, rs2) and four individuals (i1, i2, i3, i4):
IND, i1, i2, i3, i4
rs1, AT, TT, AT, AA
rs2, CG, CC, GG, GC If the effect alleles (a1) of these two SNPs are A and G respectively, then the genotype data of rs1 are X[, 1]=[1, 0, 1, 2] and the genotype data of rs2 are X[, 2]=[1, 0, 2, 1]. Further, the single-SNP summary statistics of rs1 and rs2 are generated as follows.
(betahat[1], se[1]) <- single.SNP.model(y, X[, 1])
(betahat[2], se[2]) <- single.SNP.model(y, X[, 2])Finally, when providing chr and pos columns, please confirm the assembly releases and versions of human genome. For example, if 1000 Genomes Project Phase 3 data are used to generate ancestry-matching LD estimates, then chr and pos columns should be based on UCSC hg19/GRCh37.
The ancestry-matching LD estimates are often derived from the phased haplotype data from 1000 Genomes Project Phase 3 data. Because the 1000 Genomes data are publicly available, the LD estimates only require the list of genetic variants, their physical positions and effect alleles (i.e. [snp, chr, pos, a1] from the summary statistics file).
The script import_1000g_vcf.sh illustrates how to extract phased haplotypes of select genetic variants from 1000 Genomes Phase 3 VCF format data and save them in IMPUTE reference-panel format *.impute.hap.
The scripts get_corr.m and get_corr.R illustrate how to compute LD estimates in MATLAB and R respectively.
If there are some internal genotype data that can be used to estimate LD matrix, you can first organize the genotype data in the same VCF format as 1000 Genomes Phase 3 data, and then reuse my scripts above. Again, please make sure that the physical positions and effect alleles of the internal genotype data are consistent with [chr, pos, a1] provided in the GWAS summary statistics file.
The most statistician-friendly format of genomic annotation data might look like this:
snp chr pos ann1 ann2 ann3
rs1 chr2 52877 0 0 0
rs2 chr1 50670 0 1 0
rs3 chr14 854 0 1 1
rs4 chr4 99620 1 1 1
rs5 chr16 71537 0 0 0
rs6 chr22 39741 0 0 0
rs7 chr6 89331 1 0 0where ann1, ann2 and ann3 are three types of annotations, 1 indicates that SNP is annotated and 0 otherwise.
Alternatively, a list of annotated SNPs can be saved as a separate file. For example:
> cat ann3.txt
snp chr pos
rs3 chr14 854
rs4 chr4 99620
> cat ann2.txt
snp chr pos
rs2 chr1 50670
rs3 chr14 854
rs4 chr4 99620
> cat ann1.txt
snp chr pos
rs4 chr4 99620
rs7 chr6 89331Sometimes the annotations are based on genes (e.g., biological pathways) or genomic regions (e.g., regulatory elements). For these region-based annotations, it is easier to provide a list of annotated regions as follows:
ensembl_gene_id chromosome_name start_position end_position
ENSG00000000938 1 27938575 27961788
ENSG00000008438 19 46522411 46526323
ENSG00000008516 16 3096682 3110727
ENSG00000066336 11 47376411 47400127
ENSG00000077984 20 24929866 24940564
ENSG00000085265 9 137801431 137809809Similar to LD estimates, please make sure that the physical positions ([snp, chr, pos] or [chromosome_name, start_position, end_position]) in the annotation file are consistent with [snp, chr, pos] in the summary statistics file.
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setting value
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