dat30. solarius 0.3.1

Format

(Phenotypes) A data frame dat30 with 174 rows and 10 variables:

famid: Family ID (29 unique ids).
id: Individual ID.
fa: Father ID.
mo: Mother ID.
sex: Individual gender (1 - male, 2 - female).
affect: Affected status (1 - unaffected, 2 - affected).
class: Class label.
trait1: Simulated phenotype 1.
trait2: Simulated phenotype 2.
age: Age.

(Genotypes as covariates) A matrix genocovdat30 with 174 rows and 100 columns. Row names are IDs of individuals, column names are names of SNPs.

(Annotation) A data frame mapdat30 with 100 rows and 4 variables:

SNP: SNP name.
chr: Chromosome.
pos: Position in bp.
gene: Gene.

Source

https://cran.r-project.org/package=multic

Description

29 first families were selected from the complete data set of 12000 individuals. For a resulted subset of 174 individuals, a hundred of synthetic SNPs were randomly generated. Annotation information also was generated, mainly in order to plot the association results with Manhattan plot.

Details

Two simulated phenotypes possess a high genetic correlation.

Examples

data(dat30) str(dat30)

'data.frame': 174 obs. of 10 variables: $ famid : int 1 1 1 1 1 1 2 2 2 2 ... $ id : int 11 12 13 14 15 16 21 22 23 24 ... $ fa : int 0 0 11 11 11 11 0 0 21 21 ... $ mo : int 0 0 12 12 12 12 0 0 22 22 ... $ sex : int 1 2 1 2 1 1 1 2 2 1 ... $ affect: int 2 2 2 2 2 2 2 2 2 2 ... $ class : logi NA NA NA NA NA NA ... $ trait1: num 11.96 7.1 10.32 9.76 9.46 ... $ trait2: num 13.58 5.37 6.4 8.98 9.21 ... $ age : int 50 25 35 49 51 45 37 29 39 41 ...

plotPed(dat30, 2) # plot the pedigree tree for family #2

Loading required package: kinship2 Loading required package: Matrix Loading required package: quadprog

## Not run: # kin2 <- solarKinship2(dat30) # plotKinship2(kin2) # plotKinship2(kin2[1:30, 1:30]) # # ## End(Not run) str(genocovdat30)

num [1:174, 1:100] 1.978 0.795 0.231 0.139 0.487 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:174] "11" "12" "13" "14" ... ..$ : chr [1:100] "snp_1" "snp_2" "snp_3" "snp_4" ...

genocovdat30[1:5, 1:5]

snp_1 snp_2 snp_3 snp_4 snp_5 11 1.9778186 0.76066504 1.4619557 0.1212071 1.0959075 12 0.7954909 1.42966254 1.3746092 0.5010122 0.1454984 13 0.2313956 0.07411651 1.2020817 1.9940435 1.4228365 14 0.1394974 0.99711827 0.7149236 0.6051847 0.4434309 15 0.4874988 1.44298075 1.5332879 1.7050956 1.3768445

str(mapdat30)

'data.frame': 100 obs. of 4 variables: $ SNP : Factor w/ 100 levels "snp_1","snp_10",..: 1 12 23 34 45 47 48 49 50 2 ... $ chr : int 1 1 1 1 1 1 1 1 1 1 ... $ pos : num 2105324 2105467 2106094 2108138 2109262 ... $ gene: Factor w/ 12 levels "gene1","gene2",..: 1 1 1 1 1 1 1 1 1 2 ...

head(mapdat30)

SNP chr pos gene 1 snp_1 1 2105324 gene1 2 snp_2 1 2105467 gene1 3 snp_3 1 2106094 gene1 4 snp_4 1 2108138 gene1 5 snp_5 1 2109262 gene1 6 snp_6 1 2110294 gene1

dat30 data set adapted from multic R package

Format

Source

Description

Details

Examples