Properties and unbiased estimation of F- and D-statistics in samples containing related and inbred individuals

Abstract The Patterson F- and D-statistics are commonly used measures for quantifying population relationships and for testing hypotheses about demographic history. These statistics make use of allele frequency information across populations to infer different aspects of population history, such as...

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Bibliographic Details
Published in:Genetics (Austin) 2022-01, Vol.220 (1)
Main Authors: Mughal, Mehreen R, DeGiorgio, Michael
Format: Article
Language:English
Subjects:
Estimators
Gene Frequency
Genetics
Genetics, Population - methods
Human populations
Humans
Inbreeding
Investigation
Models, Genetic
Models, Statistical
Population
Population (statistical)
Population genetics
Population statistics
Population structure
Software
Statistical analysis
Statistical methods
Statistics
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Summary:Abstract The Patterson F- and D-statistics are commonly used measures for quantifying population relationships and for testing hypotheses about demographic history. These statistics make use of allele frequency information across populations to infer different aspects of population history, such as population structure and introgression events. Inclusion of related or inbred individuals can bias such statistics, which may often lead to the filtering of such individuals. Here, we derive statistical properties of the F- and D-statistics, including their biases due to the inclusion of related or inbred individuals, their variances, and their corresponding mean squared errors. Moreover, for those statistics that are biased, we develop unbiased estimators and evaluate the variances of these new quantities. Comparisons of the new unbiased statistics to the originals demonstrates that our newly derived statistics often have lower error across a wide population parameter space. Furthermore, we apply these unbiased estimators using several global human populations with the inclusion of related individuals to highlight their application on an empirical dataset. Finally, we implement these unbiased estimators in open-source software package funbiased for easy application by the scientific community.
ISSN:1943-2631
0016-6731
1943-2631
DOI:10.1093/genetics/iyab090