Page 60 - Applied Probability
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3. Newton’s Method and Scoring
                              quency is (1 − f)2p 1p 2 .
                                      TABLE 3.2. Brazilian Genotypes at the Haptoglobin Locus
                                    Genotype
                                                                                     2
                                                                        fp 1 +(1 − f)p
                                                        108
                                      G 1 /G 1  Observed Number      Genotype Frequency      43
                                                                                     1
                                      G 1 /G 2          196              (1 − f)2p 1p 2
                                                        429
                                      G 1 /G 3                           (1 − f)2p 1p 3
                                                        143             fp 2 +(1 − f)p 2
                                      G 2 /G 2
                                                                                     2
                                                        513
                                      G 2 /G 3                           (1 − f)2p 2p 3
                                                        559             fp 3 +(1 − f)p 2
                                      G 3 /G 3
                                                                                     3
                                Because p 3 =1 − p 1 − p 2 , this model effectively has only the three
                                                                                       1 1
                              parameters (p 1 ,p 2 ,f). From the initial values, (p 01 ,p 02 ,f 0 )=( , ,.02),
                                                                                       3 3
                              scoring converges in five iterations to the maximum likelihood estimates
                                                      ˆ
                                               (ˆ p 1 , ˆ p 2 , f)=(.2157,.2554,.0431).
                              If we invert the expected information matrix, then the asymptotic standard
                                                 ˆ
                              errors of ˆ p 1 ,ˆ p 2 , and f are .0067, .0071, and .0166, respectively. If we invert
                              the observed information matrix, the first two standard errors remain the
                              same and the third changes to .0165. The asymptotic correlations of f ˆ
                              with ˆ p 1 and ˆ p 2 are less than .02 in absolute value regardless of how they
                              are computed.
                              3.4 Application to the Design of Linkage
                                     Experiments

                              In addition to being useful in the scoring algorithm, expected information
                              provides a criterion for the rational design of genetic experiments. In animal
                              breeding, it is possible to set up test matings for the detection of linkage
                              and estimation of recombination fractions. Consider two linked, codomi-
                              nant loci A and B with alleles A 1 and A 2 and B 1 and B 2 , respectively.
                              The simplest experimental design is the phase-known, double-backcross
                              mating A 1 B 1 /A 2 B 2 × A 1 B 1 /A 1 B 1 . This mating notation conveys, for ex-
                              ample, that the left parent has one haplotype with alleles A 1 and B 1 and
                              another haplotype with alleles A 2 and B 2 . Offspring of this mating can be
                              categorized as recombinant with probability θ and nonrecombinant with
                              probability 1 − θ. In view of equation (3.5), the expected information per
                              offspring is
                                                               1     1
                                                      J(θ)  =    +
                                                               θ   1 − θ
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