Page 368 - Applied Probability
P. 368
Index
358
for radiation hybrid mapping,
Drosophila
237
recombination on X chromo-
gradient method for posterior
some, 273
mode, 246
Dynamic programming, 281
haplotyping, 198
for allele frequency estimation,
multiple sequence alignment, Empirical Bayes, 39
292–293 48
Needleman-Wunsch algorithm, for haplotype frequency esti-
see Needleman-Wunsch mation, 51, 56
algorithm Enhancer region, 342, 344
Smith-Waterman algorithm, Entropy inequality, 25
see Smith-Waterman al- Environmental effect, see Quan-
gorithm titative trait
Eocyte, 215
Episodic ataxia pedigree data, 130
Egg, 1
haplotyping using descent graph
Electrophoresis, see Gel electrophore-
method, 187
sis
Epistasis, 123
Elston-Stewart algorithm, 115–117 Epoch, 170
for hypergeometric polygenic Equilibrium distribution, 262
model, 157 continuous time, 210
EM algorithm, 23
discrete time, 170
ascent property, 24–26
Wright’s formula, 327
expected information, 55 Equilibrium, stable and unstable,
for estimating admixture pa- 10
rameter, 35 Erd¨os-R´enyi law, 311
for estimating allele frequen- Ergodic condition, 170, 196
cies, 26 Ergodic theorem, 171
for estimating binomial pa- Errors, genotyping, see Genotyp-
rameter, 36 ing errors
for estimating haplotype fre- Eubacteria, 215
quencies, 33, 55 Eukaryote, 215, 344
for estimating identity coef- Evolution, neutral
ficients, 110 Kimura’s model of, 211–214
for estimating inbreeding co- equilibrium distribution, 226
efficients, 34 Evolution, slow versus fast, 219
for estimating multinomial pa- Evolutionary parsimony, Lake’s method
rameters, 37 of, 227
for estimating recombination Evolutionary trees, 203–208
fractions, 34 likelihood for, 214
for estimating segregation ra- maximum parsimony, see Max-
tios, 28 imum parsimony
for finding binding domains, model assumptions, 214
31, 37 possible number of, 204, 223
for polygenic model, 159–161 postorder traversal, 208
