Output

Here is a truncated example of the output of Zmapqtl for a backcross.

#      890840384   -filetype Zmapqtl.out
#
#       QTL Cartographer V. 1.13b, March 1998
#       This output file (qtlcart.z) was created by Zmapqtl...
#
#       It is 10:39:44 on Wednesday, 25 March 1998
#
#
#The position is from the left telomere on the chromosome
-window      10.00      Window size for models 5 and 6
-background      5      Background parameters in model 6
-Model           6      Model number
-trait           1      Analyzed trait [Trait_1]
-cross          B2      Cross
#  Test Site   * Like. Ratio Test Statistics   *     Additive        
 c  m position     H0:H1      R2(0:1)    TR2(0:1)    H1:a        S1       
-s
 1  1  0.0001      0.411      0.002      0.473      0.027      1.531     
 1  2  0.0133      0.016      0.000      0.472      0.005      1.542     
 1  2  0.0333      0.023      0.000      0.472      0.006      1.547     
 1  2  0.0533      0.031      0.000      0.472      0.008      1.554     
 1  2  0.0733      0.041      0.000      0.472      0.009      1.563      
 1  2  0.0933      0.052      0.000      0.472      0.010      1.572      
 1  2  0.1133      0.063      0.000      0.472      0.011      1.582      
 1  2  0.1333      0.073      0.000      0.472      0.012      1.593    
.
.
.  
-e

For a backcross, let $a$ be the additive effect. We have two hypotheses:

• $H_0$: no QTL effect at the test position, i.e. $a = 0$
• $H_1$: There is a QTL effect at the test position, i.e. $a \neq 0$

The first eight columns correspond to

1. Chromosome of test position
2. Left flanking marker of test position
3. Absolute position from left telomere, in Morgans.
4. Likelihood ratio test statistic for $\frac{H_1}{H_0}.$ It is a $\chi^2$ random variable with one degree of freedom for any position, meaning that a value of 3.84 or higher is evidence for a QTL. The significance level over more positions will be higher due to multiple testing.
5. $r^2$
6. $r_t^2$
7. Estimate of $a$ (the additive effect) under $H_1$
8. Test statistic $S$ for the normality of the residuals under $H_1$

The last 13 columns are not shown because they are only valid for $F_2$ design experiments. They would all be zeros if shown.

The output for an $F_2$ design (or any design in which dominance effects can be estimated) is similar, but has more information. For an $F_2$, you can estimate additive ($a$) and dominance ($d$) parameters at each position. Thus, there are four hypotheses.

• $H_0$: $a = 0$ , $d = 0$
• $H_1$: $a \neq 0$ , $d = 0$
• $H_2$: $a = 0$, $d \neq 0$
• $H_3$: $a \neq 0$, $d \neq 0$

and twelve full columns of output, corresponding to all possible hypothesis tests and parameter estimates. The 21 columns correspond to

1. Chromosome of test position.
2. Left flanking marker of test position.
3. Absolute position from left telomere, in Morgans.
4. Likelihood ratio test statistic for $\frac{H_3}{H_0}.$
5. Likelihood ratio test statistic for $\frac{H_3}{H_1}.$
6. Likelihood ratio test statistic for $\frac{H_3}{H_2}.$
7. Estimate of $a$ (the additive effect) under $H_1.$
8. Estimate of $a$ (the additive effect) under $H_3.$
9. Estimate of $d$ (the dominance effect) under $H_2.$
10. Estimate of $d$ (the dominance effect) under $H_3.$
11. Likelihood ratio test statistic for $\frac{H_1}{H_0}.$
12. Likelihood ratio test statistic for $\frac{H_2}{H_0}.$
13. $r^2$ for $\frac{H_1}{H_0}.$
14. $r^2$ for $\frac{H_2}{H_0}.$
15. $r^2$ for $\frac{H_3}{H_0}.$
16. $r_t^2$ for $\frac{H_1}{H_0}.$
17. $r_t^2$ for $\frac{H_2}{H_0}.$
18. $r_t^2$ for $\frac{H_3}{H_0}.$
19. $S$ for $H_1.$
20. $S$ for $H_2.$
21. $S$ for $H_3.$