10 Mixed models - Solutions

Run a sliding window test using the mixed model and make a QQ plot.

seqResetFilter(seqData, verbose=FALSE)
iterator <- SeqVarWindowIterator(seqData, windowSize=5000, windowShift=2000, verbose=FALSE)
assoc <- assocTestAggregate(iterator, nullmod, test="Burden", AF.max=0.1, weight.beta=c(1,1))

## # of selected samples: 100

head(assoc$results)

##   chr   start     end n.site n.alt n.sample.alt       Score   Score.SE
## 1   1  966001  971000      1     3            3 -0.08540038 0.14196057
## 2   1  982001  987000      1     9            9 -0.22242172 0.23151080
## 3   1 1022001 1027000      0     0            0          NA         NA
## 4   1 1262001 1267000      0     0            0          NA         NA
## 5   1 1468001 1473000      1     1            1 -0.08038064 0.08682388
## 6   1 1732001 1737000      0     0            0          NA         NA
##   Score.Stat Score.pval
## 1 -0.6015782  0.5474550
## 2 -0.9607401  0.3366828
## 3         NA         NA
## 4         NA         NA
## 5 -0.9257895  0.3545554
## 6         NA         NA

head(assoc$variantInfo)

## [[1]]
##   variant.id chr    pos allele.index n.obs  freq weight
## 1          1   1 970546            1   100 0.015      1
## 
## [[2]]
##   variant.id chr    pos allele.index n.obs  freq weight
## 1          2   1 985900            1   100 0.045      1
## 
## [[3]]
## [1] variant.id   chr          pos          allele.index n.obs       
## [6] freq         weight      
## <0 rows> (or 0-length row.names)
## 
## [[4]]
## [1] variant.id   chr          pos          allele.index n.obs       
## [6] freq         weight      
## <0 rows> (or 0-length row.names)
## 
## [[5]]
##   variant.id chr     pos allele.index n.obs  freq weight
## 1          5   1 1472676            1   100 0.005      1
## 
## [[6]]
## [1] variant.id   chr          pos          allele.index n.obs       
## [6] freq         weight      
## <0 rows> (or 0-length row.names)

qqPlot(assoc$results$Score.pval)

seqClose(gds)