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PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling.

Park DS, Baran Y, Hormozdiari F, Eng C, Torgerson DG, Burchard EG, Zaitlen N - BMC Bioinformatics (2015)

Bottom Line: Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics.IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci.Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult.

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ABSTRACT
Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.

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Power and error rates as a function of IBD segment counts. (a)Number of predicted segments overlapping a true IBD segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans. (b)The percentage of predicted segments that have no overlap with a true segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans.
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Figure 4: Power and error rates as a function of IBD segment counts. (a)Number of predicted segments overlapping a true IBD segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans. (b)The percentage of predicted segments that have no overlap with a true segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans.

Mentions: For a given genomic locus, the power of tests comparing the distribution of IBD in cases or between cases and controls [13,10,9], is a function of the number of true IBD segments intersected by predicted segments. We therefore performed an analysis of the total number of true IBD segments intersected by IBD calls from Refined IBD, Germline, and PIGS. The results shown in Figure 4a show that PIGS substantially outperforms Refined IBD for small IBD segments. DASH was not included in this analysis because it was not designed for this purpose and the resulting error rates were 10 fold higher than PIGS and Refined IBD even at 1 centimorgan segments. For predicted segments of size 0.5, 0.6, 0.7, 0.8, 0.9, and 1 centimorgans, there was an increase of 95%, 43%, 27%, 17%, 12%, and 9% in the number of predicted segments intersecting a true segment over Refined IBD. For predicted segments of size 1.1, 1.2, and 1.3 centimorgans, Germline was able to detect 60%, 27%, and 12% more segments than PIGS but the calls were less accurate (See Accuracy of IBD segments).


PIGS: improved estimates of identity-by-descent probabilities by probabilistic IBD graph sampling.

Park DS, Baran Y, Hormozdiari F, Eng C, Torgerson DG, Burchard EG, Zaitlen N - BMC Bioinformatics (2015)

Power and error rates as a function of IBD segment counts. (a)Number of predicted segments overlapping a true IBD segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans. (b)The percentage of predicted segments that have no overlap with a true segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4402697&req=5

Figure 4: Power and error rates as a function of IBD segment counts. (a)Number of predicted segments overlapping a true IBD segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans. (b)The percentage of predicted segments that have no overlap with a true segment is shown on the y-axis. The x-axis shows the size of the predicted segment in centimorgans.
Mentions: For a given genomic locus, the power of tests comparing the distribution of IBD in cases or between cases and controls [13,10,9], is a function of the number of true IBD segments intersected by predicted segments. We therefore performed an analysis of the total number of true IBD segments intersected by IBD calls from Refined IBD, Germline, and PIGS. The results shown in Figure 4a show that PIGS substantially outperforms Refined IBD for small IBD segments. DASH was not included in this analysis because it was not designed for this purpose and the resulting error rates were 10 fold higher than PIGS and Refined IBD even at 1 centimorgan segments. For predicted segments of size 0.5, 0.6, 0.7, 0.8, 0.9, and 1 centimorgans, there was an increase of 95%, 43%, 27%, 17%, 12%, and 9% in the number of predicted segments intersecting a true segment over Refined IBD. For predicted segments of size 1.1, 1.2, and 1.3 centimorgans, Germline was able to detect 60%, 27%, and 12% more segments than PIGS but the calls were less accurate (See Accuracy of IBD segments).

Bottom Line: Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics.IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci.Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult.

View Article: PubMed Central - HTML - PubMed

ABSTRACT
Identifying segments in the genome of different individuals that are identical-by-descent (IBD) is a fundamental element of genetics. IBD data is used for numerous applications including demographic inference, heritability estimation, and mapping disease loci. Simultaneous detection of IBD over multiple haplotypes has proven to be computationally difficult. To overcome this, many state of the art methods estimate the probability of IBD between each pair of haplotypes separately. While computationally efficient, these methods fail to leverage the clique structure of IBD resulting in less powerful IBD identification, especially for small IBD segments.

Show MeSH