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Genetic architecture reconciles linkage and association studies of complex traits

Nature Genetics volume 56, pages 2352–2360 (2024)Cite this article

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Abstract

Linkage studies have successfully mapped loci underlying monogenic disorders, but mostly failed when applied to common diseases. Conversely, genome-wide association studies (GWASs) have identified replicable associations between thousands of SNPs and complex traits, yet capture less than half of the total heritability. In the present study we reconcile these two approaches by showing that linkage signals of height and body mass index (BMI) from 119,000 sibling pairs colocalize with GWAS-identified loci. Concordant with polygenicity, we observed the following: a genome-wide inflation of linkage test statistics; that GWAS results predict linkage signals; and that adjusting phenotypes for polygenic scores reduces linkage signals. Finally, we developed a method using recombination rate-stratified, identity-by-descent sharing between siblings to unbiasedly estimate heritability of height (0.76 ± 0.05) and BMI (0.55 ± 0.07). Our results imply that substantial heritability remains unaccounted for by GWAS-identified loci and this residual genetic variation is polygenic and enriched near these loci.

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Fig. 1: RR-stratified estimates of heritability (\({\boldsymbol{h}}_{\mathbf{FS}}^{\mathbf{2}}\)) and proportion of variance owing to common sibling effects are not correlated with IBD sharing for height and BMI.
Fig. 2: Chromosomes containing loci significantly linked with height.
Fig. 3: Colocalization between observed and GWAS-predicted linkage signals.
Fig. 4: Correlation between chromosome length and estimates of variance explained from linkage analyses of height.

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Data availability

Individual-level data used in the present study are available through application to the relevant cohort. The individual-level UKB data are available upon application to the UKB (http://www.ukbiobank.ac.uk, accessed under project no. 12505). Average IBD status across four groups of loci defined by quartiles of the RR distribution will be returned (to the UKB) for 21,756 sib-pairs analyzed in the present study. These data will be accessible to researchers registered with the UKB. A genetic map for linkage analyses of height and BMI was downloaded from https://github.com/joepickrell/1000-genomes-genetic-maps/tree/master/interpolated_OMNI. A genetic map used in simulations was obtained from Bcftools: https://samtools.github.io/bcftools/bcftools.html. Summary statistics from GWASs of BMI conducted in the present study are available in Supplementary Data and in the GWAS Catalog (https://www.ebi.ac.uk/gwas) under accession no. GCST90446645.

Code availability

The customized code generated in this paper (source code of predLINK, R script to simulate sib-pairs, R script to run restricted maximum likelihood estimation for QISPs) is available via Zenodo at https://doi.org/10.5281/zenodo.10416893 (ref. 62). All other analyses were performed using publicly available software. Statistical analyses were performed using R (v.4.1.0, v.4.2.1; https://cran.r-project.org). MERLIN v.1.1.2 software was used to estimate IBD sharing (https://csg.sph.umich.edu/abecasis/Merlin/download/). KING v.2.2.7 software was used to identify sib-pairs (https://www.kingrelatedness.com/Download.shtml). GWAS of BMI was performed using BOLT-LMM v.2.4.1 (https://alkesgroup.broadinstitute.org/BOLT-LMM/BOLT-LMM_manual.html). GCTA software (gcta_1.93.1beta, v.1.93.2beta) was used for genotype data quality control (including principal component (PC) calculation, SNP loading calculation and PC projection for ancestry inference), SNP heritability estimation and COJO analysis (https://yanglab.westlake.edu.cn/software/gcta/index.html). Genotype data quality control, including filtering and LD pruning, as well as allelic scoring, was performed with PLINK v.1.90b6.20 (https://www.cog-genomics.org/plink).

References

  1. Polderman, T. J. C. et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702–709 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Risch, N. & Merikangas, K. The future of genetic studies of complex human diseases. Science 273, 1516–1517 (1996).

    Article  CAS  PubMed  Google Scholar 

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

    Google Scholar 

  4. Botstein, D. & Risch, N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat. Genet. 33, 228–237 (2003).

    Article  CAS  PubMed  Google Scholar 

  5. Hall, J. M. et al. Linkage of early-onset familial breast cancer to chromosome 17q21. Science 250, 1684–1689 (1990).

    Article  CAS  PubMed  Google Scholar 

  6. Goate, A. et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 349, 704–706 (1991).

    Article  CAS  PubMed  Google Scholar 

  7. Risch, N. J. Searching for genetic determinants in the new millennium. Nature 405, 847–856 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Weiss, K. M. & Terwilliger, J. D. How many diseases does it take to map a gene with SNPs? Nat. Genet. 26, 151–157 (2000).

    Article  CAS  PubMed  Google Scholar 

  9. Visscher, P. M., Brown, M. A., McCarthy, M. I. & Yang, J. Five years of GWAS discovery. Am. J. Hum. Genet. 90, 7–24 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010).

    Article  CAS  PubMed  Google Scholar 

  11. Klein, R. J., Xu, X., Mukherjee, S., Willis, J. & Hayes, J. Successes of genome-wide association studies. Cell 142, 350–351 (2010).

    Article  CAS  PubMed  Google Scholar 

  12. Wang, K., Bucan, M., Grant, S. F. A., Schellenberg, G. & Hakonarson, H. Strategies for genetic studies of complex diseases. Cell 142, 351–353 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Visscher, P. et al. Assumption-free estimation of heritability from genome-wide identity-by-descent sharing between full siblings. PLoS Genet. 2, e41 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Young, A. I. et al. Relatedness disequilibrium regression estimates heritability without environmental bias. Nat. Genet. 50, 1304–1310 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kong, A. et al. The nature of nurture: effects of parental genotypes. Science 359, 424–428 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Howe, L. J. et al. Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects. Nat. Genet. 54, 581–592 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50, 1112–1121 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Smith, B. H. et al. Cohort profile: Generation Scotland: Scottish family health study (GS: SFHS). The study, its participants and their potential for genetic research on health and illness. Int. J. Epidemiol. 42, 689–700 (2013).

  20. Scholtens, S. et al. Cohort profile: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 44, 1172–1180 (2015).

    Article  PubMed  Google Scholar 

  21. Sijtsma, A. et al. Cohort profile update: LifeLines, a three-generation cohort study and biobank. Int. J. Epidemiol. 51, e295–e302 (2022).

    Article  PubMed  Google Scholar 

  22. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Leitsalu, L. et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147 (2015).

    Article  PubMed  Google Scholar 

  24. Brumpton, B. M. et al. The HUNT study: a population-based cohort for genetic research. Cell Genom. 2, 100193 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Åsvold, B. O. et al. Cohort profile update: the HUNT study, Norway. Int. J. Epidemiol. 52, e80–e91 (2023).

  26. Kemper, K. E. et al. Phenotypic covariance across the entire spectrum of relatedness for 86 billion pairs of individuals. Nat. Commun. 12, 1050 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schousboe, K. et al. Sex differences in heritability of BMI: a comparative study of results from twin studies in eight countries. Twin Res. 6, 409–421 (2003).

    Article  PubMed  Google Scholar 

  29. Silventoinen, K. et al. Heritability of adult body height: a comparative study of twin cohorts in eight countries. Twin Res. 6, 399–408 (2003).

    Article  PubMed  Google Scholar 

  30. Lander, E. & Kruglyak, L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet. 11, 241–247 (1995).

    Article  CAS  PubMed  Google Scholar 

  31. Lander, E. S. & Botstein, D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121, 185–199 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dekkers, J. C. M. & Dentine, M. R. Quantitative genetic variance associated with chromosomal markers in segregating populations. Theor. Appl. Genet. 81, 212–220 (1991).

    Article  CAS  PubMed  Google Scholar 

  33. Yengo, L. et al. A saturated map of common genetic variants associated with human height. Nature 610, 704–712 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Visscher, P. M. Proportion of the variation in genetic composition in backcrossing programs explained by genetic markers. J. Heredity 87, 136–138 (1996).

    Article  Google Scholar 

  36. Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hodge, S. E. Linkage analysis versus association analysis: distinguishing between two models that explain disease-marker associations. Am. J. Hum. Genet. 53, 367–384 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Hemani, G. et al. Inference of the genetic architecture underlying BMI and height with the use of 20,240 sibling pairs. Am. J. Hum. Genet. 93, 865–875 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Hivert, V. et al. Estimation of non-additive genetic variance in human complex traits from a large sample of unrelated individuals. Am. J. Hum. Genet. 108, 786–798 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hill, W. G., Goddard, M. E. & Visscher, P. M. Data and theory point to mainly additive genetic variance for complex traits. PLoS Genet. 4, e1000008 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Wainschtein, P. et al. Assessing the contribution of rare variants to complex trait heritability from whole-genome sequence data. Nat. Genet. 54, 263–273 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Backman, J. D. et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature 599, 628–634 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Akbari, P. et al. Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity. Science 373, eabf8683 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Tenesa, A., Rawlik, K., Navarro, P. & Canela-Xandri, O. Genetic determination of height-mediated mate choice. Genome Biol. 16, 269 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yengo, L. et al. Imprint of assortative mating on the human genome. Nat. Hum. Behav. 2, 948–954 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Robinson, M. R. et al. Genetic evidence of assortative mating in humans. Nat. Hum. Behav. 1, 16 (2017).

    Article  Google Scholar 

  48. Visscher, P. M. & Haley, C. S. Detection of putative quantitative trait loci in line crosses under infinitesimal genetic models. Theor. Appl. Genet. 93, 691–702 (1996).

    Article  CAS  PubMed  Google Scholar 

  49. Sham, P. C., Cherny, S. S., Purcell, S. & Hewitt, J. K. Power of linkage versus association analysis of quantitative traits, by use of variance-components models, for sibship data. Am. J. Hum. Genet. 66, 1616–1630 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Visscher, P. M. & Hopper, J. L. Power of regression and maximum likelihood methods to map QTL from sib-pair and DZ twin data. Ann. Hum. Genet. 65, 583–601 (2001).

    Article  CAS  PubMed  Google Scholar 

  51. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Abecasis, G. R., Cherny, S. S., Cookson, W. O. & Cardon, L. R. Merlin—rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. 30, 97–101 (2002).

  53. Locke, A. E. et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Loh, P. R., Kichaev, G., Gazal, S., Schoech, A. P. & Price, A. L. Mixed-model association for biobank-scale datasets. Nat. Genet. 50, 906–908 (2018).

  55. Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zaitlen, N. et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Yengo, L., Wray, N. R. & Visscher, P. M. Extreme inbreeding in a European ancestry sample from the contemporary UK population. Nat. Commun. 10, 3719 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Delaneau, O., Zagury, J.-F. & Marchini, J. Improved whole-chromosome phasing for disease and population genetic studies. Nat. Methods 10, 5–6 (2013).

  61. Wray, N. R. et al. Pitfalls of predicting complex traits from SNPs. Nat. Rev. Genet. 14, 507–515 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yengo, L. Genetic architecture reconciles linkage and association studies of complex traits. Zenodo https://doi.org/10.5281/zenodo.10416893 (2023).

Download references

Acknowledgements

We thank the participants and analysts in each cohort contributing to the present study. L.Y. was funded by the Australian Research Council (grant nos DE200100425 and FT220100069). P.M.V. was funded by the Australian Research Council (grant no. FL180100072) and the Australian National Health and Medical Research Council (NHMRC; grant no. 113400). B.C.-D. is supported by NHMRC’s CJ Martin Fellowship (grant no. APP1161356). G.-H.M. is the recipient of an Australian Research Council Discovery Early Career Award (project no. DE220101226) funded by the Australian Government and supported by the Research Council of Norway (project grant no. 325640). D.C. is supported by the Ragnar Söderberg Foundation (grant no. E42/15), D.C. and D.J.B. by Open Philanthropy (grant no. 010623-00001 to D.J.B.) and D.J.B. by the National Institute on Aging/National Institutes of Health (grant nos R24-AG065184 and R01-AG042568). D.M.E. is supported by an Australian NHMRC Investigator Award (no. 2017942). Additional acknowledgements are provided in Supplementary Information.

Author information

Author notes

  1. These authors jointly supervised this work: Peter M. Visscher, Loic Yengo.

  2. Full lists of members and their affiliations appear in the Supplementary Information.

Authors and Affiliations

  1. Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia

    Julia Sidorenko, Baptiste Couvy-Duchesne, Kathryn E. Kemper, Gunn-Helen Moen, David M. Evans, Peter M. Visscher & Loic Yengo

  2. QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia

    Baptiste Couvy-Duchesne, Scott Gordon, Sarah E. Medland & Nicholas G. Martin

  3. Sorbonne University, Paris Brain Institute—ICM, CNRS, INRIA, INSERM, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France

    Baptiste Couvy-Duchesne

  4. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway

    Gunn-Helen Moen

  5. K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway

    Gunn-Helen Moen, Laxmi Bhatta, Bjørn Olav Åsvold, Kristian Hveem & Ben Brumpton

  6. The Frazer Institute, University of Queensland, Woolloongabba, Queensland, Australia

    Gunn-Helen Moen & David M. Evans

  7. HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, Norway

    Bjørn Olav Åsvold, Kristian Hveem & Ben Brumpton

  8. Department of Endocrinology, Clinic of Medicine, St Olavs Hospital, Trondheim, Norway

    Bjørn Olav Åsvold

  9. Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia

    Reedik Mägi, Ilja M. Nolte & Andres Metspalu

  10. Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Alireza Ani, Rujia Wang, Ilja M. Nolte & Harold Snieder

  11. Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran

    Alireza Ani

  12. MRC Human Genetics Unit, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Caroline Hayward & David Porteous

  13. Centre for Genomic and Experimental Medicine, Institute of Genetics & Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Archie Campbell

  14. Human Genetics Department, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA

    Daniel J. Benjamin

  15. Behavioral Decision Making Group, Anderson School of Management, University of California Los Angeles, Los Angeles, CA, USA

    Daniel J. Benjamin

  16. National Bureau of Economic Research, Cambridge, MA, USA

    Daniel J. Benjamin & David Cesarini

  17. Department of Economics, New York University, New York, NY, USA

    David Cesarini

  18. Center for Experimental Social Science, New York University, New York, NY, USA

    David Cesarini

  19. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

    David M. Evans

  20. Centre for AgriBioscience, Agriculture Victoria, Bundoora, Victoria, Australia

    Michael E. Goddard

  21. Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Victoria, Australia

    Michael E. Goddard

  22. MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

    Chris S. Haley

  23. Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, UK

    Chris S. Haley

  24. Coupland Craft Cider, Coupland, Northumberland, UK

    Chris S. Haley

  25. Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK

    Peter M. Visscher

Authors
  1. Julia Sidorenko
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  2. Baptiste Couvy-Duchesne
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Consortia

Estonian Biobank Research Team

  • Reedik Mägi
  •  & Andres Metspalu

Lifelines Cohort Study

  • Alireza Ani
  • , Rujia Wang
  • , Ilja M. Nolte
  •  & Harold Snieder

Contributions

P.M.V. and L.Y. conceptualized and jointly supervised the study. J.S. conducted statistical analyses (and meta-analyses) of UKB, QIMR, GS, LL and EBB data with assistance or guidance from P.M.V., L.Y. and K.E.K. G.-H.M. and B.B. performed linkage analyses using data from the HUNT study. B.C.-D., A.C., C.H., S.G., A.A., R.W., I.M.N., R.M., B.O.A. and L.B. prepared data in the respective cohorts. D.J.B., D.C., D.M.E., M.E.G. and C.S.H. contributed through suggestions and comments on study design, methods, analyses and their interpretation. D.P., S.E.M., N.G.M., H.S., A.M., K.H. and B.B. contributed to data collection, data management and scientific leadership of the respective cohorts. L.Y., P.M.V. and J.S. wrote the manuscript with the participation of all authors. All the authors approved the final version of the manuscript.

Corresponding authors

Correspondence to Julia Sidorenko, Peter M. Visscher or Loic Yengo.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Genetics thanks Luke O’Connor, Daniel Weeks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Observed and theoretically predicted statistics for locus-specific linkage analysis.

a,The observed and predicted mean test statistics of linkage (χ2) test statistics for height and BMI. The error-bars indicate standard errors (s.e.) calculated as the standard deviation of locus-specific statistics divided by the square root of the effective number independent markers, that is ~94 (Supplementary Table 8). The size of the circle is proportional to sample size. The theoretically predicted values are based on the REML estimates of heritability from genome wide IBD regression (\(\widehat{{h}_{FS}^{2}}\)) and the observed correlation between siblings. b, The proportion of loci with positive (i) estimated linkage (the bars and the values) and (ii) theoretically predicted (the black rectangles +/- s.e., Methods). The dotted horizontal line represents the proportion (that is, 0.5) expected in the absence of a genetic contribution to the trait. The data is shown for Generation Scotland (GS, number of quasi-independent sib-pairs (n) = 8,368), the Queensland Institute of Medical Research cohort (QIMR, n = 12,844), the Lifelines Cohort (LL, n = 16,581), the UK Biobank (UKB, n = 21,756), the Estonian Biobank (EBB, n = 25,333) the HUNT study (HUNT, n = 34,575) and the meta-analysis combining all cohorts (META, n = 119,457). The numerical values for mean and median χ2 and proportion of χ2 > 0 are presented in Supplementary Table 7a.

Extended Data Fig. 2 Effect of polygenicity and sample size of linkage studies on the correlation between predicted and observed linkage signals in simulated data.

The results are shown for 8 simulated genetic architectures (polygenicity = 0.1%-100%) with a genome-wide h2 = 1. a-b, show the observed and predicted linkage signals (measured as variance explained) on chromosomes 1 (a) and 22 (b), respectively, for one simulation replicate. The simulated causal variants are depicted as green stars. The predicted signal, estimated as a weighted sum of simulated effects (Methods, equation (1)) is depicted by the black curve. The grey and yellow lines show the observed linkage signal from the analysis of 20,000 and 100,000 simulated sib-pairs, respectively, where the phenotypes were simulated using the same causal variants (green stars). The correlations \(\hat{\phi }\) for each polygenicity panel are the chromosome-wide estimates for each linkage sample size (yellow: n=20,000; grey: n=100,000). c, the summary of results across 100 replicates. \(\hat{\phi }\) is estimated per chromosome across the grid of 0.5 cM, then a chromosome length weighted average is calculated for each replicate. Each symbol represents a mean value across 100 simulation replicates and the error bars are standard deviation across replicates. The left-most enlarged symbols for each polygenicity panel indicate that the true simulated SNP effects were used predict linkage signal, that is, the expected prediction accuracy from polygenic scores (\({R}_{g}^{2}\)) using these causal variants = 1. To approximate estimation errors of SNP effects in a GWAS of finite sample, \(\hat{\phi }\) was also calculated using causal variants with \({R}_{g}^{2}\) <1 (regular symbols). For the numeric values see Supplementary Table 9. Estimated variance components were not constrained to ensure unbiasedness. Therefore, if a region of the genome does not explain any genetic variation, then 50% of the estimates are expected to be negative.

Extended Data Fig. 3 Colocalization between GWAS-predicted and observed linkage signals for traits adjusted for polygenic scores (PGS).

a, The correlation between observed linkage signals for PGS-adjusted height and predicted linkage signals from 12,010 height-associated SNPs. b, The correlation between observed linkage signals for PGS-adjusted BMI and predicted linkage signals from 787 BMI-associated SNPs. Height was adjusted using a PGS based on the same 12,010 height-associated SNPs (explaining 38% of height variance), while BMI was adjusted using a PGS including 4,582 SNPs (explaining 9% of BMI variance). The x-axis in each panel displays the correlation (\(\hat{\phi }\)) between observed and predicted (from GWAS results; Methods) linkage signals. In each panel, the vertical dashed line represents the correlation between observed and predicted linkage signals from either height-associated SNPs (a) or 787 BMI-associated SNPs (b). Predicted linkage signals were also obtained under the null hypothesis (that is ‘the correlation between observed and predicted linkage signals is due to the curvature effect’) using 1,000 draws of random SNPs with similar minor allele frequency and linkage disequilibrium properties as trait-associated SNPs. The histogram in each panel represents the distribution of correlations (under the null) between observed linkage for the trait indicated in the corresponding column-panel and predicted linkage obtained from these 1,000 draws. The mean of correlations obtained under the null hypothesis is denoted \({\hat{\phi }}_{{\rm{CE}}}\). The P-values (P) reported in the top-left corner of each panel assess the statistical significance of the difference between \(\hat{\phi }\) and \({\hat{\phi }}_{{\rm{CE}}}\) using a two-sided Wald test. Numeric values are presented in Supplementary Table 10.

Extended Data Fig. 4 Correlation between chromosome length and estimates of variance explained from linkage analyses of BMI.

Analyses were based on summary statistics from a linkage meta-analysis of BMI and BMI adjusted for polygenic score (PGS). The x axis represents the physical length of each chromosome relative to the size of the autosome (that is, ~2879 Mb). The y axis represents the expected variance explained (\({q}_{{\rm{s}}}^{2}\)) for each chromosome (s = 1–22) estimated as \({q}_{{\rm{s}}}^{2}=\,{m}_{s}{\bar{q}}^{2}\), where \({\bar{q}}^{2}\) is the mean across the chromosome of estimates of locus-specific variance, and ms an effective number of independent markers per chromosome (Supplementary Table 8). Error bars around each dot represent ms times the standard deviation of linkage estimate across the chromosomes. Standard errors (s.e.) of the regression slopes were obtained using a leave-one-chromosome-out jackknife approach. 95% confidence intervals (CI) were calculated as 1.96×s.e.

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Supplementary Information

Supplementary Figs. 1–8, Tables 1–17, Notes 1–3, Methods, Discussion and references, Acknowledgements and Consortium author list.

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Supplementary Code 1

Multiple *.R files and *.Rdata files to generate the corresponding figures. Figure1.R: R script to generate Fig. 1 from Figure1.Rdata. Figure2.R: R script to generate Fig. 2 from Figure2.Rdata. Figure3.R: R script to generate Fig. 3 from Figure3.Rdata. Figure4.R: R script to generate Fig. 4 from Figure4.Rdata. ExtData_Figure1.R: R script to generate Extended Data Fig. 1 from ExtData_Figure1.Rdata. ExtData_Figure2.R: R script to generate Extended Data Fig. 2 from ExtData_Figure2.Rdata. ExtData_Figure3.R: R script to generate Extended Data Fig. 3 from ExtData_Figure3.Rdata. ExtData_Figure4.R: R script to generate Extended Data Fig. 4 from ExtData_Figure4.Rdata.

Supplementary Data

GWAS summary statistics for BMI-associated variants generated in the present study. The list of independent variants was obtained using the GCTA-COJO algorithm.

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Sidorenko, J., Couvy-Duchesne, B., Kemper, K.E. et al. Genetic architecture reconciles linkage and association studies of complex traits. Nat Genet 56, 2352–2360 (2024). https://doi.org/10.1038/s41588-024-01940-2

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