Expanded version of JAM (a single-trait fine-mapping approach) that first runs on thinned SNPs and then expands models on tag SNPs; this can run independently on multiple traits

Source: R/prep.R

Expanded version of JAM (a single-trait fine-mapping approach) that first runs on thinned SNPs and then expands models on tag SNPs; this can run independently on multiple traits

JAMexpanded.multi(
  beta1,
  Gmat,
  snpinfo,
  ybar,
  Vy,
  N,
  chr = 10,
  fstub,
  mafthr = 0.005,
  path2plink,
  r2 = 0.99,
  save.path,
  related = FALSE,
  y = NULL
)

Arguments

beta1

list where each component is a named vector
Gmat

genotype matrix (reference or from sample) where SNPs are columns, indivudals are rows
snpinfo

a data.frame with columns: snpid, BP, allele1, allele2; BP is base pair position, allele1 is reference allele
ybar

vector of trait means; if related samples, this should be based on unrelated samples; if traits are transformed to be standard Normal, could set ybar as 0-vector
Vy

vector of trait variances; if related samples, this should be based on unrelated samples; if traits are transformed to be standard Normal, could set Vy as 1-vector
N

vector of sample sizes for each trait; if related samples then give effective sample sizes
chr

chromosome number
fstub

file stub for path with file prefix to save intermediate files (plink format and tag snp list)
mafthr

MAF threshold for filtering SNPs to include in fine-mapping; default is 0, assuming pre-filterec
path2plink

file path to plink
r2

r.squared threshold for thinning SNPs before JAM
save.path

path to save JAM output files; tmp files and could delete these later
related

logical indicating if samples are related (TRUE) or not (FALSE); default is FALSE
y

if available, matrix of trait measurements or indicators of non-NA trait measurements (columns are traits); used to get joint sample counts; default is NULL and if not provided an approximation is used based on vector of trait sample sizes

Value

list with 3 components: SM a list of snpmod objects giving fine-mapping results for each trait; mbeta a list of joint effect estimates for each trait; nsnps number of SNPs