Genetic Components of Alcoholism

Levels of expressed alpha synuclein mRNA in three groups of allele lengths of NACP-REP1.

alpha

Format

A data frame with 97 observations on 2 variables.

alength

allele length, a factor with levels "short", "intermediate" and "long".

elevel

expression levels of alpha synuclein mRNA.

Details

Various studies have linked alcohol dependence phenotypes to chromosome 4. One candidate gene is NACP (non-amyloid component of plaques), coding for alpha synuclein. Bönsch et al. (2005) found longer alleles of NACP-REP1 in alcohol-dependent patients compared with healthy controls and reported that the allele lengths show some association with levels of expressed alpha synuclein mRNA.

Source

Bönsch, D., Lederer, T., Reulbach, U., Hothorn, T., Kornhuber, J. and Bleich, S. (2005). Joint analysis of the NACP-REP1 marker within the alpha synuclein gene concludes association with alcohol dependence. Human Molecular Genetics 14(7), 967–971. doi:10.1093/hmg/ddi090

References

Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2006). A Lego system for conditional inference. The American Statistician 60(3), 257–263. doi:10.1198/000313006X118430

Winell, H. and Lindbäck, J. (2018). A general score-independent test for order-restricted inference. Statistics in Medicine 37(21), 3078–3090. doi:10.1002/sim.7690

Examples

## Boxplots
boxplot(elevel ~ alength, data = alpha)


## Asymptotic Kruskal-Wallis test
kruskal_test(elevel ~ alength, data = alpha)
#> 
#> 	Asymptotic Kruskal-Wallis Test
#> 
#> data:  elevel by alength (short, intermediate, long)
#> chi-squared = 8.8302, df = 2, p-value = 0.01209
#> 

## Asymptotic Kruskal-Wallis test using midpoint scores
kruskal_test(elevel ~ alength, data = alpha,
             scores = list(alength = c(2, 7, 11)))
#> 
#> 	Asymptotic Linear-by-Linear Association Test
#> 
#> data:  elevel by alength (short < intermediate < long)
#> Z = 2.9263, p-value = 0.00343
#> alternative hypothesis: two.sided
#> 

## Asymptotic score-independent test
## Winell and Lindbaeck (2018)
(it <- independence_test(elevel ~ alength, data = alpha,
                         ytrafo = function(data)
                             trafo(data, numeric_trafo = rank_trafo), 
                         xtrafo = function(data)
                             trafo(data, factor_trafo = function(x)
                                 zheng_trafo(as.ordered(x)))))
#> 
#> 	Asymptotic General Independence Test
#> 
#> data:  elevel by alength (short, intermediate, long)
#> maxT = 2.9651, p-value = 0.008198
#> alternative hypothesis: two.sided
#> 

## Extract the "best" set of scores
ss <- statistic(it, type = "standardized")
idx <- which(abs(ss) == max(abs(ss)), arr.ind = TRUE)
ss[idx[1], idx[2], drop = FALSE]
#>                                
#> gamma = (0.0, 0.4, 1.0) 2.96508