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Reconciling the analysis of IBD and IBS in complex trait studies

Abstract

Identity by descent (IBD) is a fundamental concept in genetics and refers to alleles that are descended from a common ancestor in a base population. Identity by state (IBS) simply refers to alleles that are the same, irrespective of whether they are inherited from a recent ancestor. In modern applications, IBD relationships are estimated from genetic markers in individuals without any known relationship. This can lead to erroneous inference because a consistent base population is not used. We argue that the purpose of most IBD calculations is to predict IBS at unobserved loci. Recognizing this aim leads to better methods to estimating IBD with benefits in mapping genes, estimating genetic variance and predicting inbreeding depression.

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References

  1. 1

    Malécot, G. Les Mathématiques de l'Hérédité. (Masson, Paris, 1948).

    Google Scholar 

  2. 2

    Wright, S. Coefficients of inbreeding and relationship. Am. Nat. 51, 636–639 (1917).

    Article  Google Scholar 

  3. 3

    Wright, S. Systems of mating. I. The biometric relations between parent and offspring. Genetics 6, 111–123 (1921).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

    Kong, A. et al. Detection of sharing by descent, long-range phasing and haplotype imputation. Nature Genet. 40, 1068–1075 (2008).

    CAS  Article  Google Scholar 

  5. 5

    Albrechtsen, A. et al. Relatedness mapping and tracts of relatedness for genome-wide data in the presence of linkage disequilibrium. Genet. Epidemiol. 33, 266–274 (2009).

    Article  Google Scholar 

  6. 6

    Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analysis. Am. J. Hum. Genet. 81, 559–575 (2007).

    CAS  Article  Google Scholar 

  7. 7

    Visscher, P. M. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, 316–325 (2006).

    CAS  Article  Google Scholar 

  8. 8

    Charlesworth, D. & Willis, J. H. The genetics of inbreeding depression. Nature Rev. Genet. 10, 783–796 (2009).

    CAS  Article  Google Scholar 

  9. 9

    The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–862 (2007).

  10. 10

    Hartl, D. L. & Clark, A. G. Principles of Population Genetics (Sinauer Associates, Massachusetts, 1997).

    Google Scholar 

  11. 11

    Rosenberg, N. A. & Nordborg, M. Genealogical trees, coalescent theory and the analysis of genetic polymorphisms. Nature Rev. Genet. 3, 380–390 (2002).

    CAS  Article  Google Scholar 

  12. 12

    Nordborg, M. in Handbook of Statistical Genetics (eds Balding, D. J., Bishop, M. & Cannings, C.) 179–212 (Wiley, Chichester, 2001).

    Google Scholar 

  13. 13

    Browning, S. R. Estimation of pairwise identity by descent from dense genetic marker data in a population sample of haplotypes. Genetics 178, 2123–2132 (2008).

    Article  Google Scholar 

  14. 14

    Manolio, T. A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Maher, B. The case of the missing heritability. Nature 456, 18–21 (2008).

    CAS  Article  Google Scholar 

  16. 16

    Lynch, M. & Walsh, B. Genetics and Analysis of Quantitative Traits (Sinauer Associates, Massachusetts, 1998).

    Google Scholar 

  17. 17

    Jacquard, A. The Genetic Structure of Populations (Springer, New York, 1974).

    Google Scholar 

  18. 18

    Hayes, B. J., Visscher, P. M., McPartlan, H. C. & Goddard, M. E. Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Res. 13, 635–643 (2003).

    CAS  Article  Google Scholar 

  19. 19

    Slatkin, M. Inbreeding coefficients and coalescence times. Genet. Res. 58, 167–175 (1991).

    CAS  Article  Google Scholar 

  20. 20

    Milligan, B. G. Maximum-likelihood estimation of relatedness. Genetics 163, 1153–1167 (2003).

    PubMed  PubMed Central  Google Scholar 

  21. 21

    Astle, W. & Balding, D. J. Population structure and cryptic relatedness in genetic association studies. Stat. Sci. 24, 451–471 (2009).

    Article  Google Scholar 

  22. 22

    Yang, J. et al. Common SNPs explain a large proportion of the heritability for human. Nature Genet. 42, 565–571 (2010).

    CAS  Article  Google Scholar 

  23. 23

    Chadeau-Hyam, M. et al. Fregene: simulation of realistic sequence-level data in populations and ascertained samples. BMC Bioinformatics 9, 364–375 (2008).

    Article  Google Scholar 

  24. 24

    Hoggart, C. J. et al. Sequence-level population simulations over large genomic regions. Genetics 177, 1725–1731 (2007).

    CAS  Article  Google Scholar 

  25. 25

    Visscher, P. M., Hill, W. G. & Wray, N. R. Heritability in the genomics era — concepts and misconceptions. Nature Rev. Genet. 9, 255–266 (2008).

    CAS  Article  Google Scholar 

  26. 26

    Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Goddard, M. E. & Hayes, B. J. Genomic selection. J. Anim. Breed. Genet. 124, 323–330 (2007).

    CAS  Article  Google Scholar 

  28. 28

    Hayes, B. J., Visscher, P. M. & Goddard, M. E. Increased accuracy of artificial selection by using the realized relationship matrix. Genet. Res. 91, 47–60 (2009).

    CAS  Article  Google Scholar 

  29. 29

    Goddard, M. E. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica 136, 245–257 (2009).

    Article  Google Scholar 

  30. 30

    Habier, D., Fernando, R. L. & Dekkers, J. C. The impact of genetic relationship information on genome-assisted breeding values. Genetics 177, 2389–2397 (2008).

    Article  Google Scholar 

  31. 31

    VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).

    CAS  Article  Google Scholar 

  32. 32

    Goddard, M. E. & Meuwissen, T. H. E. The use of linkage disequilibrium to map quantitative trait loci. Aust. J. Exp. Agric. 45, 837–845 (2005).

    CAS  Article  Google Scholar 

  33. 33

    Meuwissen, T. H. E. & Goddard, M. E. Multipoint identity-by-descent prediction using dense markers to map quantitative trait loci and estimate effective population size. Genetics 176, 2551–2560 (2007).

    CAS  Article  Google Scholar 

  34. 34

    Lander, E. S. & Botstein D. Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science 236, 1567–1570 (1987).

    CAS  Article  Google Scholar 

  35. 35

    Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Carothers, A. D. et al. Estimating human inbreeding coefficients: comparison of genealogical and marker heterozygosity approaches. Ann. Hum. Genet. 70, 666–676 (2006).

    CAS  Article  Google Scholar 

  37. 37

    McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).

    CAS  Article  Google Scholar 

  38. 38

    Charlesworth, B. & Charlesworth, D. The genetic basis of inbreeding depression. Genet. Res. 74, 571–576 (1999).

    Article  Google Scholar 

  39. 39

    Broman, K. W. & Weber, J. L. Long homozygous chromosome segments in reference families from the centre d'étude du polymorphisme humain. Am. J. Hum. Genet. 65, 1493–1500 (1999).

    CAS  Article  Google Scholar 

  40. 40

    Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K. & Goddard, M. E. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genet. Sel. Evol. 41, 51 (2009).

    Article  Google Scholar 

  41. 41

    Patterson, N., Price, A. L. & Reich, D. Population structure and eigenanalysis. PLoS Genet. 2, e190 (2006).

    Article  Google Scholar 

  42. 42

    Goddard, M. E., Wray, N. R., Verbyla, K. & Visscher, P. M. Estimating effects and making predictions from genome-wide marker data. Stat. Sci. 24, 517–529 (2009).

    Article  Google Scholar 

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Acknowledgements

We acknowledge funding from the Australian National Health and Medical Research Council (grants 389892, 613672 and 613601) and the Australian Research Council (grants DP0770096 and DP1093900). We thank M. Keller for discussions and advice on simulation.

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Correspondence to Joseph E. Powell or Peter M. Visscher.

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The authors declare no competing financial interests.

Supplementary information

Supplementary information S1 (box)

Simulation of genotypic data and estimation of relatedness coefficients (PDF 297 kb)

Supplementary information S2 (box)

Probability that a hidden SNP within a ROH is homozygous (PDF 295 kb)

Supplementary information S3 (box)

Estimating heritability using relatedness coefficients (PDF 288 kb)

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FURTHER INFORMATION

Genetic Epidemiology, Molecular Epidemiology and Queensland Statistical Genetics Laboratories Brisbane, Australia

Nature Reviews Genetics series on Study Designs

Glossary

Coalescence theory

A population genetics model of inheritance relationships among alleles at a given locus. The coalescence of two alleles is the most recent point (going back in time) at which they shared a common ancestor.

Cryptic relatedness

The presence of close relatives in a sample of ostensibly unrelated individuals. It is characterized by a recent common ancestry that can be revealed from marker-based relatedness coefficients.

Genome-wide association study

Analysis of the entire genome using association models to identify regions of the genome that contribute to genetic variation in a phenotype. These studies typically analyse data from high-density SNP arrays.

Heritability

The proportion of phenotypic variation in a population that is attributable to genetic variation among individuals. Statistical methods are used to estimate the relative contributions of differences in genetic and non-genetic factors to the total phenotypic variation in a population.

Identity by descent

(IBD). Two or more alleles are IBD if they are identical copies of the same ancestral allele in a base population. IBD can be estimated for alleles at single loci in a diploid individual or between individuals.

Identity by state

(IBS). Refers to two or more alleles that 'look' the same. For example, if two individuals both carry a 'G' allele at a specific locus.

Pedigree

A population of individuals in which the mating records for multiple generations are known. Pedigree information is typically available for livestock populations, in which controlled breeding has been implemented to maximize the response to genetic selection.

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Powell, J., Visscher, P. & Goddard, M. Reconciling the analysis of IBD and IBS in complex trait studies. Nat Rev Genet 11, 800–805 (2010). https://doi.org/10.1038/nrg2865

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