profile_qtl function

QTL profiling

Generates the score-based genome-wide profile conditional to the selected QTL.

profile_qtl(
  data,
  model,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.profile'
print(x, pheno.col = NULL, sint = NULL, ...)

Arguments

• data: an object of class qtlpoly.data.
• model: an object of class qtlpoly.model containing the QTL to be profiled.
• d.sint: a $d$ value to subtract from logarithm of p-value ($LOP-d$) for support interval calculation, e.g. $d=1.5$ (default) represents approximate 95% support interval.
• polygenes: if TRUE all QTL but the one being tested are treated as a single polygenic effect, if FALSE (default) all QTL effect variances have to estimated.
• n.clusters: number of parallel processes to spawn.
• plot: a suffix for the file's name containing plots of every QTL profiling round, e.g. "profile" (default); if NULL, no file is produced.
• verbose: if TRUE (default), current progress is shown; if FALSE, no output is produced.
• x: an object of class qtlpoly.profile to be printed.
• pheno.col: a numeric vector with the phenotype column numbers to be plotted; if NULL, all phenotypes from 'data' will be included.
• sint: whether "upper" or "lower" support intervals should be printed; if NULL (default), only QTL peak information will be printed.
• ...: currently ignored

Returns

An object of class qtlpoly.profile which contains a list of results for each trait with the following components:

• pheno.col: a phenotype column number.

• stat: a vector containing values from score statistics.

• pval: a vector containing p-values from score statistics.

• qtls: a data frame with information from the mapped QTL.

• lower: a data frame with information from the lower support interval of mapped QTL.

• upper: a data frame with information from the upper support interval of mapped QTL.

Examples

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)

  # Profile model
  profile.mod = profile_qtl(data = data, model = optimize.mod, d.sint = 1.5, n.clusters = 1)

References

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. tools:::Rd_expr_doi("10.1534/genetics.120.303080") .

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Author(s)

Guilherme da Silva Pereira, gdasilv@ncsu.edu