Identity by descent

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A

nucleotide sequences in this segment. An IBS segment is identical by descent (IBD) in two or more individuals if they have inherited it from a common ancestor without recombination, that is, the segment has the same ancestral origin in these individuals. DNA segments that are IBD are IBS per definition, but segments that are not IBD can still be IBS due to the same mutations in different individuals or recombinations that do not alter the segment.[citation needed
]

The origin of IBD segments is depicted via a pedigree.
The origin of IBD segments is depicted via a pedigree.
The origin of IBD segments is depicted via a pedigree.
A colorblind-friendly version of this image.

Theory

All individuals in a finite population are related if traced back long enough and will, therefore, share segments of their

genomes IBD. During meiosis segments of IBD are broken up by recombination. Therefore, the expected length of an IBD segment depends on the number of generations since the most recent common ancestor at the locus of the segment. The length of IBD segments that result from a common ancestor n generations in the past (therefore involving 2n meiosis) is exponentially distributed with mean 1/(2n) Morgans (M).[1] The expected number of IBD segments decreases with the number of generations since the common ancestor at this locus. For a specific DNA segment, the probability of being IBD decreases as 2−2n since in each meiosis the probability of transmitting this segment is 1/2.[2]

Applications

Identified IBD segments can be used for a wide range of purposes. As noted above the amount (length and number) of IBD sharing depends on the familial relationships between the tested individuals. Therefore, one application of IBD segment detection is to quantify relatedness.

forensic genetics,[7] but can also increase information in genetic linkage mapping[3][8] and help to decrease bias by undocumented relationships in standard association studies.[6][9]
Another application of IBD is
phase inference.[10][11][12] Long shared segments of IBD, which are broken up by short regions may be indicative for phasing errors.[5][13]
: SI 

IBD mapping

IBD mapping

power to map genes or genomic regions containing multiple rare disease susceptibility variants.[6][14]

Using simulated data, Browning and

An IBD segment identified by HapFABIA in the 1000 Genomes
An IBD segment identified by HapFABIA in Asian genomes. Rare single nucleotide variants (SNVs) that tag the IBD segment are coloured purple. Below the turquoise bar, the IBD segment in ancient genomes is displayed.

IBD in population genetics

Detection of natural selection in the human genome is also possible via detected IBD segments. Selection will usually tend to increase the number of IBD segments among individuals in a population. By scanning for regions with excess IBD sharing, regions in the human genome that have been under strong, very recent selection can be identified.[21][22]

In addition to that, IBD segments can be useful for measuring and identifying other influences on population structure.

Denisova.[13]

Methods and software

Programs for the detection of IBD segments in unrelated individuals:

  • RAPID: Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform [32]
  • Parente: identifies IBD segments between pairs of individuals in unphased genotype data[33]
  • BEAGLE/fastIBD: finds segments of IBD between pairs of individuals in genome-wide SNP data[34]
  • BEAGLE/RefinedIBD: finds IBD segments in pairs of individuals using a hashing method and evaluates their significance via a likelihood ratio[35]
  • IBDseq: detects pairwise IBD segments in sequencing data[36]
  • GERMLINE: discovers in linear-time IBD segments in pairs of individuals[5]
  • DASH: builds upon pairwise IBD segments to infer clusters of individuals likely to be sharing a single haplotype[15]
  • PLINK: is a tool set for
    whole genome association and population-based linkage analyses including a method for pairwise IBD segment detection[6]
  • Relate: estimates the probability of IBD between pairs of individuals at a specific locus using SNPs[3]
  • MCMC_IBDfinder: is based on Markov chain Monte Carlo (MCMC) for finding IBD segments in multiple individuals[37]
  • IBD-Groupon: detects group-wise IBD segments based on pairwise IBD relationships[38]
  • HapFABIA: identifies very short IBD segments characterized by rare variants in large sequencing data simultaneously in multiple individuals[13]

See also

References

  1. PMID 18430938
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  30. PMID 24385924.{{cite journal}}: CS1 maint: numeric names: authors list (link
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  32. ^ Naseri A, Liu X, Zhang S, Zhi D. Ultra-fast Identity by Descent Detection in Biobank-Scale Cohorts using Positional Burrows–Wheeler Transform BioRxiv 2017.
  33. ^ Rodriguez JM, Batzoglou S, Bercovici S. An accurate method for inferring relatedness in large datasets of unphased genotypes via an embedded likelihood-ratio test. RECOMB 2013, LNBI 7821:212-229.
  34. PMID 21310274
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