Calculating "Additive effect" of Qtls

[zahrap...@gmail.com]

Dear prof.Karl Broman 

I want to calculate "Additive effect "of each QTL  in my work . 

On the other hand, I got Multiple QTL mapping ,i used make

M_qtl <- makeqtl(M_QTL_LR4, chr=c( "3","6"), pos=c(60, 93), what="prob")

Estimated effects:

-----------------

                     est               SE                t

Intercept    56.773       3.364      16.875

3@60          -9.908        3.639        -2.723

6@93.0      13.777         3.581        3.847

I want to  know that if   "  est " column does the same meaning of "Additive effect" of Qtls? I mean for example the addetive effects of  6th  chromosome  is 13.777?

Or I am in the wrong way and I misunderstand ?

Thank you in advance , 

best regards 

Zahra Pakbaz

[Samuel Wang]

Hi Zahra Pakbaz,

You should use “fitqtl” function. The usage of fitqtl showed below:

fitqtl(cross, pheno.col=1, qtl, covar=NULL, formula, method=c("imp", "hk"),

       model=c("normal", "binary"), dropone=TRUE, get.ests=FALSE,

       run.checks=TRUE, tol=1e-4, maxit=1000, forceXcovar=FALSE)

If you want to estimate the effect from the qtr object, you should add the “get.ests= TRUE”.

Hope it would work!

Samuel Wang

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[zahra pakbaz]

Dear Samuel Wang

I appreciate your response but I want to know which parts of the result show the additive effects? 

Because   I used 

fitqtl(QTL_root_length_TREATMENT,  qtl=M_qtl,  pheno.col=2,   method="hk",   get.ests=TRUE, dropone=FALSE))

Estimated effects:

-----------------

                     est               SE                t

Intercept    56.773       3.364      16.875

3@60          -9.908        3.639        -2.723

6@93.0      13.777         3.581        3.847

My question is if the " est  "  column shows the additive effects of QTLs  or not ? for example, the additive effects of chromosome 6 is 13.777 ?

Best regards, 

Zahra Pakbaz 

To view this discussion on the web visit https://groups.google.com/d/msgid/rqtl-disc/E9908C2E-256D-4A78-BE74-6A269C57685E%40gmail.com.

[Karl Broman]

If there's a single effect, as here, it would be the estimated additive effect. See the help file for fitqtl():

The estimated QTL effects for a backcross are derived by the coding scheme +/- 1/2 for AA and AB, so that the additive effect corresponds to the difference between phenotype averages for the two genotypes. For doubled haploids and RIL, the coding scheme is +/- 1 for AA and BB, so that the additive effect corresponds to half the difference between the phenotype averages for the two homozygotes.

For an intercross, the additive effect is derived from the coding scheme -1/0/+1 for genotypes AA/AB/BB, and so is half the difference between the phenotype averages for the two homozygotes. The dominance deviation is derived from the coding scheme 0/+1/0 for genotypes AA/AB/BB, and so is the difference between the phenotype average for the heterozygotes and the midpoint between the phenotype averages for the two homozygotes.

karl

[zahra pakbaz]

Dear Dr.Broman 

 thank you for your response.

My population is RIL .  which script can I use to estimate additive effects? 

Previously I used : 

M_QTL_LR4 <- calc.genoprob(QTL_root_length_TREATMENT, step=1)

M_qtl <- makeqtl(M_QTL_LR4, chr=c("3","6"), pos=c(60.0, 93.0), what="prob")

summary(M_qtl)

out.fq <- fitqtl(QTL_root_length_TREATMENT, qtl=M_qtl,pheno.col=2, method="hk",formula=y~Q1+Q2)
summary(out.fq)

summary(fitqtl(QTL_root_length_TREATMENT, qtl=M_qtl,pheno.col=2, method="hk", get.ests=TRUE, dropone=FALSE))

and the results is : 

Full model result

---------------------------------- 

Model formula: y ~ Q1 + Q2

 

       df        SS        MS     LOD     %var Pvalue(Chi2)    Pvalue(F)

Model   2  50376.02 25188.009 4.94745 11.35668 1.128626e-05 1.352325e-05

Error 186 393204.65  2114.003                                          

Total 188 443580.67                                                    

 

 

Estimated effects:

-----------------

           

               est         SE      t

Intercept 56.773    3.364   16.875

3@60.0     -9.908   3.639   -2.723

6@93.0     13.777   3.581    3.847

 

I want to  know that if   "  est " column does the same meaning of "Additive effect" of Qtls? or I should use another script to estimate additive effects of QTL? 

Best regards,

zahra pakbaz

To view this discussion on the web visit https://groups.google.com/d/msgid/rqtl-disc/3dbed0fa-d7fd-4684-856a-a9c20ef1e784n%40googlegroups.com.

[Karl Broman]

With recombinant inbred lines, fitqtl() is still giving the additive effect, as half the difference between the two phenotype averages.

karl