 The BTL Procedure

Suppose you have genotyped 20 members of an experimental backcross population at five markers spanning two chromosomes, and you have also recorded the disease resistance status of each subject as resistant or not resistant. You are interested in finding whether there are BTL in the genetic region spanned by your marker set, and if so, where those BTL are and how strongly linked they are to the disease resistance locus. The first step is to input the data, and then to use PROC BTL with the appropriate options to request all single-marker models of the data to be calculated, as in the following program:

```   data MarkerDat;
input (m1-m5) (\$) trait;
datalines;
A B B A B 1
B A B B A 1
A B A B B 1
A A B B A 1
A B A B B 1
B A A A B 1
A A B A A 1
B B A B A 1
A A B B A 1
A A A B A 1
A B A A B 0
B A B A A 0
B A A A B 0
B B A B B 0
B A A B B 0
B B A A B 0
A B B A A 0
B A B A A 0
A B A A B 0
B A B A B 0
;
```

```   proc btl data=MarkerDat;
marker m1-m5 /all=1;
model trait;
run;
```

The results from the analysis are shown in Figure 4.1.

Figure 4.1 Single-Marker Model Statistics
The BTL Procedure

Model Statistics
Marker
Effect
DF Chi-
Square
Pr > ChiSq AIC AICC BIC
M4 1 6.8470 0.0089 29.2 30.7 32.2
M1 1 4.5129 0.0336 31.5 33.0 34.5
M5 1 2.9321 0.0868 33.1 34.6 36.1
M2 1 1.2289 0.2676 34.8 36.3 37.8
M3 1 1.2289 0.2676 34.8 36.3 37.8

The BTL Procedure

Marker Class Means
Marker
Class
Marker
Genotype
N Mean Standard
Error
pi11 BB 9 0.3333 0.0247
pi12 BA 6 0.0000 0.0000
pi21 AB 8 0.2500 0.0234
pi22 AA 8 1.0000 0.0000

Parameter Estimates
Parameter Estimate 95% Confidence Limits
r1 0.0000
r2 0.0000
p11 1.3333 0.0000 2.6667
p12 0.0000 0.0000 0.0000
p21 1.0000 0.0000 2.4000
p22 4.0000 4.0000 4.0000
theta 0.5000

Similarly, all two-marker models can be calculated as follows, with the results shown in Figure 4.2.

```   proc btl data=MarkerDat;
marker m1-m5 /all=2;
model trait;
run;
```

Figure 4.2 Two-Marker Model Statistics
The BTL Procedure

Model Statistics
Marker
Effect
DF Chi-
Square
Pr > ChiSq AIC AICC BIC
M1*M4 3 11.0214 0.0116 29.0 33.3 34.0
M4*M5 3 10.0860 0.0178 30.0 34.3 35.0
M3*M4 3 8.3508 0.0393 31.7 36.0 36.7
M1*M2 3 7.6224 0.0545 32.4 36.7 37.4
M2*M4 3 7.1383 0.0676 32.9 37.2 37.9
M1*M5 3 6.3399 0.0962 33.7 38.0 38.7
M1*M3 3 5.0764 0.1663 35.0 39.3 40.0
M3*M5 3 4.4450 0.2172 35.6 39.9 40.6
M2*M5 3 3.6150 0.3061 36.4 40.7 41.4
M2*M3 3 1.3136 0.7259 38.7 43.0 43.7

Since m1 m4 appears to be the best two-marker effect, you can then estimate the recombination and penetrance parameters for this BTL model. First you have to enter the mapping information for the markers as follows:

```   data MarkerMap;
input marker \$ chromosome position location;
datalines;
m1 1 1 0
m2 1 2 4.3
m3 1 3 16
m4 2 1 0
m5 2 2 5.5
;
```

Now you can use the PARMEST statement to request the parameter estimates to be calculated, as in the following code. PROC BTL estimates penetrance values with each recombination parameter set to 0.5.

```   proc btl data=MarkerDat map=MarkerMap;
marker m1 m4 /group=chromosome;
model trait;
parmest cross=b gen=1 r=0.5;
run;
```

Figure 4.3 displays information about the model that includes the two-marker effect m1 m4. The "Parameter Estimates" table shows that penetrance values are not in the valid range (between 0 and 1) for this model with the given values of .

Figure 4.3 Model Statistics, Marker Class Means, and Parameter Estimates for m1*m4 Model
The BTL Procedure

Model Statistics
Marker
Effect
DF Chi-
Square
Pr > ChiSq AIC AICC BIC
M1*M4 3 11.0214 0.0116 29.0 33.3 34.0

Marker Class Means
Marker
Class
Marker
Genotype
N Mean Standard
Error
pi11 AA 5 0.4000 0.0480
pi12 AB 5 1.0000 0.0000
pi21 BA 6 0.1667 0.0231
pi22 BB 4 0.5000 0.0625

Parameter Estimates
Parameter Estimate
r1 0.0000
r2 0.0000
p11 1.6000
p12 4.0000
p21 0.6667
p22 2.0000
theta 0.5000

Note: This procedure is experimental. Previous Page | Next Page | Top of Page