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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: chromosome number of genetic variant such as chr1,…,chr22, chrX, chrY;pos: physical position, in base pair, of genetic variant;a1: allele associated with the trait, 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 very helpful for sanity checks:
strand: strand on which the alleles are reported, a single character - or +;n: number of individuals analyzed (a.k.a. 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 about genetic variants.It is very important to make sure that [a1, betahat, se] are perfectly matched. Below is a toy example. Consider two SNPs (rs1, rs2) and four individuals (i1, i2, i3, i4):
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 given by:
(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 explicitly specify the assembly releases and versions of human genome. For example, if 1000 Genomes Project Phase 3 data are used to estimate LD, please ensure that chr and pos columns are based on UCSC hg19/GRCh37.
All RSS methods to date also require the input of an estimated LD matrix.
The 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 their effect alleles (i.e. [snp, chr, pos, a1] from the summary statistics file).
If there are some internal genotype data that can be used to estimate LD matrix, please organize the genotype data in the same VCF format as 1000 Genomes Phase 3 data. Again, 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.
Annotation data are only required if you want to use RSS for enrichment analyses. 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 or genomic regions (e.g. biological pathways). For these annotations, it is easier to provide a list of annotated regions:
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 137809809For all these annotation files, please make sure that the physical positions ([snp, chr, pos] or [chromosome_name, start_position, end_position]) are consistent with [snp, chr, pos] in the summary statistics file.
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setting value
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