SNPs and SNVs in forensic science

Weir, Bruce S.; Zheng, Xiuwen

doi:10.1016/j.fsigss.2015.09.106

Cited by 4 publications

(12 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our framework, the WG estimator is given by Therefore, this estimator differs from our proposal [ 58 ] by replacing our with . Under the kinship model, the expectation of is Therefore, the limit of this estimator is which agrees with calculations in the original WG work [ 20 – 22 ]. Note that, assuming that kinship coefficients must be non-negative, the above estimator recovers the kinship IBD probabilities if and only if which holds if and only if for every pair of individuals j ≠ k .…”

Section: Resultssupporting

confidence: 85%

See 1 more Smart Citation

Estimating FST and kinship for arbitrary population structures

2021

View full text Add to dashboard Cite

FST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of FST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of FST to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing FST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and FST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and FST estimates.

show abstract

Section: Resultssupporting

confidence: 85%

“…Estimation of F ST in the correct scale is crucial for its interpretation as an IBD probability, for obtaining comparable estimates in different datasets and across species, as well as for DNA forensics [ 3 , 7 , 19 , 20 , 78 – 80 ]. Our framework results in a new unbiased genome-wide F ST estimator.…”

Section: Discussionmentioning

confidence: 99%

Estimating FST and kinship for arbitrary population structures

2021

View full text Add to dashboard Cite

show abstract

“…Estimates (blue) include 95% prediction intervals (too narrow to see) from 39 independently-simulated genotype matrices for our admixture model ( Supplementary Information, Section S9). [5,9,55,61,[63][64][65]. Our findings that existing genome-wide F ST estimates are downwardly biased matters in these settings.…”

Section: Discussionmentioning

confidence: 91%

“…The implementation ofÂ min used in the analyses in this work is given in the next subsection. The A jk statistic defined above is closely related to the mean "identity by state" estimator [18] and to another recently-described kinship estimator [53,61]. However, only ourφ T,new jk in Eq.…”

Section: General Approachmentioning

confidence: 99%

F_STand kinship for arbitrary population structures II: Method-of-moments estimators

Ochoa

Storey

2016

Preprint

View full text Add to dashboard Cite

F ST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently used estimators of F ST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we provide new results on the behavior of these estimators in the presence of arbitrarily complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing F ST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally assumed models of structure. We then present our new approach, which consistently estimates kinship and F ST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and F ST estimates.Note: This article is Part II of two-part manuscripts. We refer to these in the text as Part I and Part II, respectively. Part I: Alejandro Ochoa and John D. Storey. "F ST and kinship for arbitrary population structures I: Generalized definitions". bioRxiv (

show abstract

“…At the other extreme, a fully differentiated population has F ST = 1 and every subpopulation at every locus is homozygous for some allele. In addition to measuring population differentiation, F ST is also used to model DNA profile matching uncertainty in forensics [7][8][9][10][11][12][13] and to identify loci under selection [14][15][16][17][18][19][20][21]. Current F ST definitions assume a partitioned or subdivided population into discrete, non-overlapping subpopulations [5,6,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

F_STand kinship for arbitrary population structures I: Generalized definitions

Ochoa

Storey

2016

Preprint

View full text Add to dashboard Cite

F ST is a fundamental measure of genetic differentiation and population structure, currently defined for subdivided populations. F ST in practice typically assumes independent, nonoverlapping subpopulations, which all split simultaneously from their last common ancestral population so that genetic drift in each subpopulation is probabilistically independent of the other subpopulations. We introduce a generalized F ST definition for arbitrary population structures, where individuals may be related in arbitrary ways, allowing for arbitrary probabilistic dependence among individuals. Our definitions are built on identity-by-descent (IBD) probabilities that relate individuals through inbreeding and kinship coefficients. We generalize F ST as the mean inbreeding coefficient of the individuals' local populations relative to their last common ancestral population. We show that the generalized definition agrees with Wright's original and the independent subpopulation definitions as special cases. We define a novel coancestry model based on "individual-specific allele frequencies" and prove that its parameters correspond to probabilistic kinship coefficients. Lastly, we extend the Pritchard-Stephens-Donnelly admixture model in the context of our coancestry model and calculate its F ST . To motivate this work, we include a summary of analyses we have carried out in follow-up papers, where our new approach has been applied to simulations and global human data, showcasing the complexity of human population structure, demonstrating our success in estimating kinship and F ST , and the shortcomings of existing approaches. The probabilistic framework we introduce here provides a theoretical foundation that extends F ST in terms of inbreeding and kinship coefficients to arbitrary population structures, paving the way for new estimators and novel analyses.Note: This article is Part I of two-part manuscripts. We refer to these in the text as Part I and Part II, respectively. Part I: Alejandro Ochoa and John D. Storey. "F ST and kinship for arbitrary population structures I: Generalized definitions". bioRxiv (

show abstract

SNPs and SNVs in forensic science

Cited by 4 publications

References 10 publications

Estimating FST and kinship for arbitrary population structures

Estimating FST and kinship for arbitrary population structures

F_STand kinship for arbitrary population structures II: Method-of-moments estimators

F_STand kinship for arbitrary population structures I: Generalized definitions

Contact Info

Product

Resources

About

SNPs and SNVs in forensic science

Cited by 4 publications

References 10 publications

Estimating FST and kinship for arbitrary population structures

Estimating FST and kinship for arbitrary population structures

FSTand kinship for arbitrary population structures II: Method-of-moments estimators

FSTand kinship for arbitrary population structures I: Generalized definitions

Contact Info

Product

Resources

About

F_STand kinship for arbitrary population structures II: Method-of-moments estimators

F_STand kinship for arbitrary population structures I: Generalized definitions