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The BTL Procedure

Example

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
NOTE: The r and theta parameters are fixed.

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;
   

Since m1m4 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 m1m4. 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 .

Copyright © 2008 by SAS Institute Inc., Cary, NC, USA. All rights reserved.