Phenotype and genotype data from the Finland United States Investigation of NIDDM (type 2) Diabetes (FUSION) study.
Format
Data frames with the following variables.
- id
subject ID number for matching between data sets
- t2d
a factor with levels
casecontrol- bmi
body mass index
- sex
a factor with levels
FM- age
age of subject at time phenotypes were colelcted
- smoker
a factor with levels
formerneveroccasionalregular- chol
total cholesterol
- waist
waist circumference (cm)
- weight
weight (kg)
- height
height (cm)
- whr
waist hip ratio
- sbp
systolic blood pressure
- dbp
diastolic blood pressure
- marker
RS name of SNP
- markerID
numeric ID for SNP
- allele1
first allele coded as 1 = A, 2 = C, 3 = G, 4 = T
- allele2
second allele coded as 1 = A, 2 = C, 3 = G, 4 = T
- genotype
both alleles coded as a factor
- Adose
number of A alleles
- Cdose
number of C alleles
- Gdose
number of G alleles
- Tdose
number of T alleles
Source
Similar to the data presented in
Laura J. Scott, Karen L. Mohlke, Lori L. Bonnycastle, Cristen J. Willer, Yun Li, William L. Duren, Michael R. Erdos, Heather M. Stringham, Pe- ter S. Chines, Anne U. Jackson, Ludmila Prokunina-Olsson, Chia-Jen J. Ding, Amy J. Swift, Narisu Narisu, Tianle Hu, Randall Pruim, Rui Xiao, Xiao- Yi Y. Li, Karen N. Conneely, Nancy L. Riebow, Andrew G. Sprau, Maurine Tong, Peggy P. White, Kurt N. Hetrick, Michael W. Barnhart, Craig W. Bark, Janet L. Goldstein, Lee Watkins, Fang Xiang, Jouko Saramies, Thomas A. Buchanan, Richard M. Watanabe, Timo T. Valle, Leena Kinnunen, Goncalo R. Abecasis, Elizabeth W. Pugh, Kimberly F. Doheny, Richard N. Bergman, Jaakko Tuomilehto, Francis S. Collins, and Michael Boehnke, A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility vari- ants, Science (2007).
Examples
data(Pheno); data(FUSION1); data(FUSION2)
#> Warning: data set ‘Pheno’ not found
#> Warning: data set ‘FUSION1’ not found
#> Warning: data set ‘FUSION2’ not found
FUSION1m <- merge(FUSION1, Pheno, by = "id", all.x = FALSE, all.y = FALSE)
xtabs( ~ t2d + genotype, data = FUSION1m)
#> genotype
#> t2d GG GT TT
#> case 737 375 48
#> control 835 309 27
xtabs( ~ t2d + Gdose, data = FUSION1m)
#> Gdose
#> t2d 0 1 2
#> case 48 375 737
#> control 27 309 835
chisq.test( xtabs( ~ t2d + genotype, data = FUSION1m ) )
#>
#> Pearson's Chi-squared test
#>
#> data: xtabs(~t2d + genotype, data = FUSION1m)
#> X-squared = 18.306, df = 2, p-value = 0.0001059
#>
f1.glm <- glm( factor(t2d) ~ Gdose, data = FUSION1m, family = binomial)
summary(f1.glm)
#>
#> Call:
#> glm(formula = factor(t2d) ~ Gdose, family = binomial, data = FUSION1m)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> -1.232 -1.232 1.124 1.124 1.410
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.53183 0.13402 -3.968 7.24e-05 ***
#> Gdose 0.32930 0.07737 4.256 2.08e-05 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 3231.4 on 2330 degrees of freedom
#> Residual deviance: 3213.0 on 2329 degrees of freedom
#> AIC: 3217
#>
#> Number of Fisher Scoring iterations: 3
#>