Use MR Base to find the SNPs linked to lactate


Move the files to a folder in my home directory using WinSCP and use gunzip to unzip ch22 and ch02

Pathway to folder: /panfs/panasas01/sscm/ca16591/ProtecT_genetic_Vanessa


Import the results for ch22 to R and select rs762523_A


Merge the genetic data for rs762523_A with the ProtecT metabolite data


Recode the rs762523_A such that the G is the risk allele


Tabulation of cases and controls by rs762523_A

0 1 2
Case 132 406 348
Control 77 283 263

Run a simple linear regression of genotype (rs762523) on log-transformed lactate


Results for rs762523

    Lac
    B CI std. Error p
(Intercept)   0.55 0.50 – 0.60 0.02 <.001
rs762523_A
1   0.04 -0.02 – 0.09 0.03 .170
2   0.02 -0.03 – 0.08 0.03 .414
Observations   1509
R2 / adj. R2   .001 / .000


Read in rs1260326_T data and merge on subjectid


Recode the rs1260326_T such that the C is the risk allele


Tabulation of cases and controls by rs1260326_T

0 1 2
Case 122 423 341
Control 115 291 217

Run a simple linear regression of genotype (rs1260326) on log-transformed lactate


Results for rs1260326

    Lac
    B CI std. Error p
(Intercept)   0.56 0.52 – 0.61 0.02 <.001
rs1260326_T
1   0.03 -0.02 – 0.08 0.03 .286
2   -0.00 -0.06 – 0.05 0.03 .915
Observations   1509
R2 / adj. R2   .002 / .001


Sensitivity analysis (adding age as a covariate)


    Lac
    B CI std. Error p
(Intercept)   0.59 0.38 – 0.81 0.11 <.001
rs762523_A
1   0.04 -0.02 – 0.09 0.03 .173
2   0.02 -0.03 – 0.08 0.03 .417
age   -0.00 -0.00 – 0.00 0.00 .681
Observations   1509
R2 / adj. R2   .001 / -.001


    Lac
    B CI std. Error p
(Intercept)   0.61 0.39 – 0.82 0.11 <.001
rs1260326_T
1   0.03 -0.02 – 0.08 0.03 .287
2   -0.00 -0.06 – 0.05 0.03 .919
age   -0.00 -0.00 – 0.00 0.00 .685
Observations   1509
R2 / adj. R2   .002 / -.000