Simulate by quantitative trait


In this tutorial, we will use SeqSIMLA to simulate families with quantitative trait.

  • In this tutorial, we use Asian 500kb on chrom 1(download).
    -popfile ASN_500k.bed.gz
    -recfile ASN_500k.rec

  • In our example pedigree file(download), there are 20 families, with 1,380 people.
    -famfile SAP.txt

    If you don't have a pedigree file, you can just use the option "default 3-generation families" to generate fixed 3-generation pedigrees.
    see "Output Options: -fam number" in User Manual.

  • One replicate of simulated data.
    -batch 1

  • Correlation between two traits. Here we use the same parameters as we used in the SeqSIMLA2 paper to simulate correlation structures for the systolic and diastolic blood pressure (SBP and DBP).
    • Trait1 (SBP):
      • Sites
        -site 1,3345,6631,9836,12422,15428,18932,21737,24485,27343,30944,33847,38154,41107,45113
        15 QTL are randomly selected across different allele frequency spectrum.
      • The phenotypic variance for the first trait.
        -var 190.4
      • The proportions of variance explained by QTL, polygenic effects, and shared environmental effects on the first trait.
        -vp 0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.405,0.313
        We assumed the known QTL explain 5% of the total variance for SBP. The polygenic effects explain 40.5%, the shared environmental effects explain 31.3%, and the individual specific environmental effects explain the rest of the variance.
      • The population mean for the first trait.
        -mu 132
    • Trait2 (DBP):
      • Sites
        -site2 1,3345,6631,9836,12422,15428,18932,21737,24485,27343
        We assume 10 out of the 15 QTL for SPB are the known QTL for DBP.
      • The phenotypic variance for the second trait.
        -var2 81
      • The proportions of variance explained by QTL, polygenic effects, and shared environmental effects on the second trait.
        -vp2 0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.282,0.232
        We assumed the known QTL explain 3% of the total variance for SBP. The polygenic effects explain 28.2%, the shared environmental effects explain 23.2%, and the individual specific environmental effects explain the rest of the variance.
      • The population mean for the second trait.
        -mu2 83.1
    • 95% of polygenic sites for the second trait will be the same as the polygenic sites for the first trait. The rest (5%) of the sites for the second trait will be independently simulated.
      -pintersect 0.95
    • The correlation coefficient between spouses for the shared environmental effects on traits.
      -sp_cor 0.12
      -sp_cor2 0.15
    • The correlation coefficient between siblings for the shared environmental effects on traits.
      -sib_cor 0.219
      -sib_cor2 0.109
    • The correlation coefficient between parent and offspring for the shared environmental effects on traits.
      -po_cor 0
      -po_cor2 0.072
    • The correlation coefficient within the same person for the shared environmental effects between traits.- self_corb 0.7
    • The correlation coesfficient between spouses for the shared environmental effects between traits.
      -sp_corb 0.4
    • The correlation coefficient between parent and offspring for the shared environmental effects between traits.
      -po_corb 0.3
    • The correlation coefficient between siblings for the shared environmental effects between traits.
      -sib_corb 0.8
Collect all file into a folder and placed in the same directory you run SeqSIMA

Execute the following command,
./SeqSIMLA -popfile data/ASN_500k.bed.gz -recfile data/ASN_500k.rec -folder test1 -header test -batch 1 -famfile data/SAP.txt -site 1,3345,6631,9836,12422,15428,18932,21737,24485,27343,30944,33847,38154,41107,45113 -site2 1,3345,6631,9836,12422,15428,18932,21737,24485,27343 -pintersect 0.95 -var 190.4 -var2 81 -vp 0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.0033,0.405,0.313 -vp2 0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.282,0.232 -mu 132 -mu2 83.1 -sp_cor 0.12 -sp_cor2 0.15 -sib_cor 0.219 -sib_cor2 0.109 -po_cor 0 -po_cor2 0.072 -self_corb 0.7 -sp_corb 0.4 -po_corb 0.3 -sib_corb 0.8

With our example files, this simulation would take about 150 seconds.

Notice: If you don't want to make the command yourself, we provide a generate command user interface on our website.