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 -