geno: Simulated genotypic dataset

In RFGLS: Rapid Feasible Generalized Least Squares

Description

A dataset of simulated genotypes on 10 arbitrary SNPs, for the same simulees in datasets pheno and pedigree.

Usage

data(geno)

Format

A data frame containing only integers, with 1 row per SNP, and 1 column per subject. The row and column names are rs numbers and individual IDs, respectively.

Details

The genotypes are coded as counts of each SNPs reference allele on the HapMap (http://hapmap.ncbi.nlm.nih.gov/) positive strand. First, 10 SNPs, one each from the first 10 human chromosomes, were selected arbitrarily. Then, genotypes were generated for founders (parents, adoptees, and "independent observations"), under Hardy-Weinberg equilibrium, using the allele frequencies from HapMap's CEU reference data (representing Caucasians of European Ancestry). After that, genes were "dropped" from parents to offspring. Subjects' genotypes on the arbitrarily chosen effect locus, rs7681769, were conditioned upon to simulate quantitative phenotype scores (Zscore in dataset pheno). The true effect size in the data-generating distribution is approximately 0.5% of phenotypic variance.

Dataset geno has both row and column names, which is acceptable for a data frame to be provided as argument genfile to gls.batch(). However, a genotype file to be read from disk should have NEITHER an extra column of row labels nor an extra row of column headers.

Details about each SNP may be found in dataset map.

Examples

data(geno)
str(data.frame(t(geno)))
round(cor(t(geno)),3)  ##<--SNPs are on different chromosomes, so no LD.
##Also see examples for functions fgls(), gls.batch(), and gls.batch.get().

Example output

Loading required package: bdsmatrix

Attaching package: 'bdsmatrix'

The following object is masked from 'package:base':

    backsolve

Loading required package: Matrix
'data.frame':	4050 obs. of  10 variables:
 $ rs3934834 : int  2 2 2 1 2 2 2 2 2 2 ...
 $ rs2204204 : int  2 2 2 2 1 1 2 0 2 2 ...
 $ rs12493026: int  2 2 2 2 2 2 2 2 1 1 ...
 $ rs7681769 : int  1 1 1 0 1 1 1 0 1 1 ...
 $ rs2304033 : int  0 0 0 1 2 2 1 2 1 1 ...
 $ rs10944161: int  1 1 2 1 2 2 2 2 0 0 ...
 $ rs6952877 : int  2 2 2 2 2 2 2 1 2 2 ...
 $ rs475181  : int  0 0 1 0 0 0 1 1 0 0 ...
 $ rs17432514: int  1 1 1 2 2 2 2 2 2 2 ...
 $ rs11591988: int  2 2 2 2 1 1 2 1 2 2 ...
           rs3934834 rs2204204 rs12493026 rs7681769 rs2304033 rs10944161
rs3934834      1.000    -0.006     -0.004    -0.029    -0.002      0.006
rs2204204     -0.006     1.000     -0.025     0.008    -0.010      0.036
rs12493026    -0.004    -0.025      1.000     0.002     0.018     -0.006
rs7681769     -0.029     0.008      0.002     1.000    -0.011      0.020
rs2304033     -0.002    -0.010      0.018    -0.011     1.000      0.017
rs10944161     0.006     0.036     -0.006     0.020     0.017      1.000
rs6952877      0.015     0.040      0.022     0.010     0.011      0.002
rs475181      -0.004     0.025      0.024     0.013    -0.018     -0.018
rs17432514    -0.011     0.008     -0.009     0.001     0.034     -0.015
rs11591988     0.002    -0.029      0.028    -0.005    -0.011     -0.005
           rs6952877 rs475181 rs17432514 rs11591988
rs3934834      0.015   -0.004     -0.011      0.002
rs2204204      0.040    0.025      0.008     -0.029
rs12493026     0.022    0.024     -0.009      0.028
rs7681769      0.010    0.013      0.001     -0.005
rs2304033      0.011   -0.018      0.034     -0.011
rs10944161     0.002   -0.018     -0.015     -0.005
rs6952877      1.000   -0.004      0.008     -0.041
rs475181      -0.004    1.000      0.007      0.005
rs17432514     0.008    0.007      1.000      0.031
rs11591988    -0.041    0.005      0.031      1.000

RFGLS documentation built on May 2, 2019, 2:51 p.m.