Inferring population history from genealogical trees
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Inferring population history from genealogical trees. / Wiuf, Carsten.
In: Journal of Mathematical Biology, Vol. 46, No. 3, 01.03.2003, p. 241-264.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Inferring population history from genealogical trees
AU - Wiuf, Carsten
PY - 2003/3/1
Y1 - 2003/3/1
N2 - Inference about population history from DNA sequence data has become increasingly popular. For human populations, questions about whether a population has been expanding and when expansion began are often the focus of attention. For viral populations, questions about the epidemiological history of a virus, e.g., HIV-1 and Hepatitis C, are often of interest. In this paper I address the following question: Can population history be accurately inferred from single locus DNA data? An idealised world is considered in which the tree relating a sample of n non-recombining and selectively neutral DNA sequences is observed, rather than just the sequences themselves. This approach provides an upper limit to the information that possibly can be extracted from a sample. It is shown, based on Kingman's (1982a) coalescent process, that consistent estimation of parameters describing population history (e.g., a growth rate) cannot be achieved for increasing sample size, n. This is worse than often found for estimators of genetic parameters, e.g., the mutation rate typically converges at rate √log(n) under the assumption that all historical mutations can be observed in the sample. In addition, various results for the distribution of maximum likelihood estimators are presented.
AB - Inference about population history from DNA sequence data has become increasingly popular. For human populations, questions about whether a population has been expanding and when expansion began are often the focus of attention. For viral populations, questions about the epidemiological history of a virus, e.g., HIV-1 and Hepatitis C, are often of interest. In this paper I address the following question: Can population history be accurately inferred from single locus DNA data? An idealised world is considered in which the tree relating a sample of n non-recombining and selectively neutral DNA sequences is observed, rather than just the sequences themselves. This approach provides an upper limit to the information that possibly can be extracted from a sample. It is shown, based on Kingman's (1982a) coalescent process, that consistent estimation of parameters describing population history (e.g., a growth rate) cannot be achieved for increasing sample size, n. This is worse than often found for estimators of genetic parameters, e.g., the mutation rate typically converges at rate √log(n) under the assumption that all historical mutations can be observed in the sample. In addition, various results for the distribution of maximum likelihood estimators are presented.
KW - Coalescent process
KW - Genealogy
KW - Maximum likelihood inference
KW - Population history
UR - http://www.scopus.com/inward/record.url?scp=0642378036&partnerID=8YFLogxK
U2 - 10.1007/s00285-002-0180-8
DO - 10.1007/s00285-002-0180-8
M3 - Journal article
C2 - 12728335
AN - SCOPUS:0642378036
VL - 46
SP - 241
EP - 264
JO - Journal of Mathematical Biology
JF - Journal of Mathematical Biology
SN - 0303-6812
IS - 3
ER -
ID: 203902681