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P. 373
for testing clustering of re-
Meiosis indicators, 189
striction sites, 306–308
MENDEL pedigree likelihood soft-
ware, xi, 185
fixation probabilities, 323
Messenger RNA, see RNA, mes-
senger
fixation times, 324
mean frequency, 326
Metaphase spread, 64 Neutral model, 321 Index 363
Methanogen, 215 variance of frequency, 326
Metropolis algorithm, see Hastings- Newton’s method, 39
Metropolis algorithm for a quadratic function, 52
Mixed model for polygenic trait, Nonparametric linkage analysis
154 affecteds-only method, 106
Moran’s model, 14–16 descent graph method, 192
Morgan, 132 Normal distribution, 351–354
Mosquito two-locus genotype data, multivariate, 142, 352–354
34 data compression, 161
Multilevel logistic model, 220 univariate, 351–352
Multinomial distribution Normal equations, 159
W d statistic for Nuclear family, 27
Poisson approximation to, likelihood for, 136
300 Nucleosome, 342
distribution function, 73, 74 Nucleotide, 341
hidden, see Hidden trials Nucleus, 215, 344
testing hypotheses about, 62–
73 Obligate breaks
Multivariate normal distribution, distribution
see Normal distribution, haploid case, 235, 250
multivariate polyploid case, 244, 253
Mutation, 12 minimum criterion
in hemoglobin, 66 consistency of, 233–235
Myotonic dystrophy pedigree data, haploid case, 233
128, 134 polyploid case, 244
Observed information, see Infor-
Needleman-Wunsch algorithm, 286– mation, observed
290 Oligonucleotide, 347
affine gap distance, 288 Order statistics, 310
memory reduction, 289–290 Ott’s likelihood representation, 117
parallel processing, 289
Neighborhood method, 299 Partial digest, 345
for number of triangles in ran- Partition function, see Gibbs ran-
dom graph, 312 dom fields, partition func-
for somatic cell hybrid pan- tion
els, 303 Pascal’s triangle, 285
for success runs in Bernoulli Paternity testing, 124–125
trials, 314 Pattern matching, 281–283
