SNPgenmap: convert physical map positions on build 36 of the genome to...

In CrypticIBDcheck: Identifying Cryptic Relatedness in Genetic Association Studies

Description

Convert physical map positions on build 36 of the genome to genetic map positions by linear interpolation of the Rutgers combined linkage-physical map. The markers in the Rutgers map are a small subset of markers for which genetic map positions have been determined. Linear interpolation is done for points in between.

Usage

SNPgenmap(physmap, chromosomes)

Arguments

physmap	a vector of physical map positions on build 36 of the human genome
chromosomes	a vector containing the corresponding chromosome numbers

Details

Genetic map positions are inferred from physical positions by linear interpolation of the Rutgers Combined Linkage-Physical Map for build 36 of the human genome, contained in the data object RutgersMapB36. Users who want some other form of interpolation can do so themselves using RutgersMapB36, as illustrated in the Examples. NB: The order of markers in RutgersMapB36 is the same for both physical and genetic maps. In order for an interpolated genetic map to preserve the ordering of physical map positions, the interpolant must be monotone increasing. Linear interpolation is monotone increasing, but other forms, such as spline interpolation, may not be.

Value

The function returns a vector of genetic map positions.

Note

The function interpolates the Rutgers map and does not attempt to extrapolate for SNPs outside the map. Genetic map positions for SNPs outside the Rutgers map are set to NA.

Author(s)

Annick Joelle Nembot-Simo, Jinko Graham and Brad McNeney

See Also

RutgersMapB36

Examples

data(Nhlsim)
gmap <- SNPgenmap(Nhlsim$physmap,Nhlsim$chromosome)

# Example of using RugtersMapB36 to do spline rather than linear 
# interpolation of genetic map positions on chromosome 1.  
# NB: Interpolant is not necessarily monotone increasing, which can lead to a 
# genetic map on which markers are re-ordered relative to the physical map.
data("RutgersMapB36")
chrmap<-splinefun(RutgersMapB36[["chr1"]]$Build36_map_physical_position,
                  RutgersMapB36[["chr1"]]$Sex.averaged_map_position)
c1ind<-(Nhlsim$chromosome=="chr1")
gmap[c1ind]<-chrmap(Nhlsim$physmap[c1ind])

Example output

Loading required package: rJPSGCS
Loading required package: rJava
OpenJDK 64-Bit Server VM warning: Can't detect primordial thread stack location - find_vma failed
Loading required package: car
Loading required package: carData
Loading required package: ellipse

Attaching package: ‘ellipse’

The following object is masked from ‘package:car’:

    ellipse

The following object is masked from ‘package:graphics’:

    pairs

Warning messages:
1: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
2: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
3: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
4: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
5: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
6: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
7: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
8: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
9: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
10: In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
  collapsing to unique 'x' values
Warning message:
In regularize.values(x, y, ties, missing(ties)) :
  collapsing to unique 'x' values

CrypticIBDcheck documentation built on May 2, 2019, 7:30 a.m.