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Reconstruction of genealogical relationships with applications to Phase III of HapMap.

Kyriazopoulou-Panagiotopoulou S, Kashef Haghighi D, Aerni SJ, Sundquist A, Bercovici S, Batzoglou S - Bioinformatics (2011)

Bottom Line: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration.We demonstrate that CARROT clearly outperforms existing methods on simulated data.We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu

ABSTRACT

Motivation: Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt-niece versus niece-aunt or first cousins versus great aunt-niece).

Results: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration. We demonstrate that CARROT clearly outperforms existing methods on simulated data. We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.

Availability: Source code for CARROT is freely available at http://carrot.stanford.edu.

Contact: sofiakp@stanford.edu.

Show MeSH
Comparison between CARROT, RELPAIR and PREST-plus on a set of seven relationships: the height of the bars is the percentage of pairs that were classified incorrectly, so smaller bars are better. In each relationship, the leftmost bar corresponds to RELPAIR, the middle bar to PREST-plus and the rightmost bar to CARROT. Avg. is the average error over all relationships examined. (2, 0, 0): full siblings; (1, −1, 0): parent–child; (2, 0, 1): aunt-niece; (1, −1, 1): grandparent-grandchild; (1, 0, 0): half siblings; (2, 1, 1): first cousins, unrel: unrelated.
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Figure 3: Comparison between CARROT, RELPAIR and PREST-plus on a set of seven relationships: the height of the bars is the percentage of pairs that were classified incorrectly, so smaller bars are better. In each relationship, the leftmost bar corresponds to RELPAIR, the middle bar to PREST-plus and the rightmost bar to CARROT. Avg. is the average error over all relationships examined. (2, 0, 0): full siblings; (1, −1, 0): parent–child; (2, 0, 1): aunt-niece; (1, −1, 1): grandparent-grandchild; (1, 0, 0): half siblings; (2, 1, 1): first cousins, unrel: unrelated.

Mentions: Figure 3 shows the number of pairs that were predicted incorrectly by each method. Although all three methods achieved excellent accuracy for the first-degree relationships, CARROT clearly outperformed both RELPAIR and PREST-plus on second degree pairs, particularly on half siblings. RELPAIR and PREST-plus classified more than half (56 and 68%, respectively) of the half sibling pairs as avuncular. We note that the difference in performance between RELPAIR and PREST-plus can probably be attributed to the fact that RELPAIR was run on smaller sets of SNPs (see Section 2.8).Fig. 3.


Reconstruction of genealogical relationships with applications to Phase III of HapMap.

Kyriazopoulou-Panagiotopoulou S, Kashef Haghighi D, Aerni SJ, Sundquist A, Bercovici S, Batzoglou S - Bioinformatics (2011)

Comparison between CARROT, RELPAIR and PREST-plus on a set of seven relationships: the height of the bars is the percentage of pairs that were classified incorrectly, so smaller bars are better. In each relationship, the leftmost bar corresponds to RELPAIR, the middle bar to PREST-plus and the rightmost bar to CARROT. Avg. is the average error over all relationships examined. (2, 0, 0): full siblings; (1, −1, 0): parent–child; (2, 0, 1): aunt-niece; (1, −1, 1): grandparent-grandchild; (1, 0, 0): half siblings; (2, 1, 1): first cousins, unrel: unrelated.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117348&req=5

Figure 3: Comparison between CARROT, RELPAIR and PREST-plus on a set of seven relationships: the height of the bars is the percentage of pairs that were classified incorrectly, so smaller bars are better. In each relationship, the leftmost bar corresponds to RELPAIR, the middle bar to PREST-plus and the rightmost bar to CARROT. Avg. is the average error over all relationships examined. (2, 0, 0): full siblings; (1, −1, 0): parent–child; (2, 0, 1): aunt-niece; (1, −1, 1): grandparent-grandchild; (1, 0, 0): half siblings; (2, 1, 1): first cousins, unrel: unrelated.
Mentions: Figure 3 shows the number of pairs that were predicted incorrectly by each method. Although all three methods achieved excellent accuracy for the first-degree relationships, CARROT clearly outperformed both RELPAIR and PREST-plus on second degree pairs, particularly on half siblings. RELPAIR and PREST-plus classified more than half (56 and 68%, respectively) of the half sibling pairs as avuncular. We note that the difference in performance between RELPAIR and PREST-plus can probably be attributed to the fact that RELPAIR was run on smaller sets of SNPs (see Section 2.8).Fig. 3.

Bottom Line: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration.We demonstrate that CARROT clearly outperforms existing methods on simulated data.We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA. sofiakp@stanford.edu

ABSTRACT

Motivation: Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt-niece versus niece-aunt or first cousins versus great aunt-niece).

Results: We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration. We demonstrate that CARROT clearly outperforms existing methods on simulated data. We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.

Availability: Source code for CARROT is freely available at http://carrot.stanford.edu.

Contact: sofiakp@stanford.edu.

Show MeSH