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The collective effects of genetic variants and complex traits

Journal of Human Genetics volume 68, pages 255–262 (2023)Cite this article

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Abstract

Traditional approaches in studying the genetics of complex traits have focused on identifying specific genetic variants. However, the collective effects of variants have remained largely unexplored. Here, we evaluated whether traits could be influenced by the collective effects of variants across the entire protein coding-region of the genome or the entire genome. We studied the UK Biobank exome sequencing data of 167,246 individuals as well as the genome-wide SNP array data of 408,868 individuals. We calculated for each individual four different measures of genetic variation such as heterozygosity and number of variants and two different measures of the overall deleteriousness of all variants, and performed correlations with 17 representative traits that have been studied previously. Linear regression analysis was performed with adjustment for age, sex, and genetic principal components. The results showed a high correlation among the six different measures and an inverse association of two well-correlated traits (educational attainment and height) with the total number of all variants as well as the overall deleteriousness of all variants. We have also categorized the genes based on whether they are expressed in the brain and found that the association with educational attainment only held for the brain-expressed genes. No other traits examined showed a significant correlation with the brain-expressed genes. The study demonstrates that common traits could be studied by analyzing the overall genetic variation and suggests that educational attainment is inversely related to genetic variation.

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References

  1. Huang S. Inverse relationship between genetic diversity and epigenetic complexity. Nat. Preced. 2009. https://doi.org/10.1038/npre.2009.1751.2.

  2. Huang S. New thoughts on an old riddle: What determines genetic diversity within and between species? Genomics. 2016;108:3–10.

    Article  CAS  PubMed  Google Scholar 

  3. Bickel D. Testing hypotheses of molecular evolution. In: Phylogenetic trees and molecular evolution. Springer, Cham; 2022.

  4. Huguet G, Schramm C, Douard E, Tamer P, Main A, Monin P, et al. Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability. Mol Psychiatry. 2021;26:2663–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell 2017;169:1177–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fisher RA. The correlations between relatives on the supposition of Mendelian inheritance. Philos Trans R Soc Edinb. 1918;52:399–433.

    Article  Google Scholar 

  7. Povysil G, Petrovski S, Hostyk J, Aggarwal V, Allen AS, Goldstein DB. Rare-variant collapsing analyses for complex traits: guidelines and applications. Nat Rev Genet. 2019;20:747–59.

    Article  CAS  PubMed  Google Scholar 

  8. Marioni RE, Penke L, Davies G, Huffman JE, Hayward C, Deary IJ. The total burden of rare, non-synonymous exome genetic variants is not associated with childhood or late-life cognitive ability. Proc Biol Sci. 2014;281:20140117.

  9. Yuan D, Zhu Z, Tan X, Liang J, Zeng C, Zhang J, et al. Minor alleles of common SNPs quantitatively affect traits/diseases and are under both positive and negative selection. arXiv:12092911. 2012.

  10. Yuan D, Zhu Z, Tan X, Liang J, Zeng C, Zhang J, et al. Scoring the collective effects of SNPs: association of minor alleles with complex traits in model organisms. Sci China Life Sci. 2014;57:876–88.

    Article  CAS  PubMed  Google Scholar 

  11. Zhu Z, Yuan D, Luo D, Lu X, Huang S. Enrichment of minor alleles of common SNPs and improved risk prediction for Parkinson’s disease. PLoS One. 2015;10:e0133421.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Chen H, Lei X, Yuan D, Huang S. The relationship between the minor allele content and Alzheimer’s disease. Genomics. 2020;112:2426–32.

    Article  CAS  PubMed  Google Scholar 

  13. Gui Y, Lei X, Huang S. Collective effects of common SNPs and genetic risk prediction in type 1 diabetes. Clin Genet. 2017;93:1069–74.

    Article  Google Scholar 

  14. Lei X, Huang S. Enrichment of minor allele of SNPs and genetic prediction of type 2 diabetes risk in British population. PLoS One. 2017;12:e0187644.

    Article  PubMed  PubMed Central  Google Scholar 

  15. He P, Lei X, Yuan D, Zhu Z, Huang S. Accumulation of minor alleles and risk prediction in schizophrenia. Sci Rep. 2017;7:11661.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Lei X, Yuan J, Zhu Z, Huang S. Collective effects of common SNPs and risk prediction in lung cancer. Heredity. 2018. https://doi.org/10.1038/s41437-018-0063-4.

  17. Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019;47:D886–D94.

    Article  CAS  PubMed  Google Scholar 

  18. Suybeng V, Koeppel F, Harle A, Rouleau E. Comparison of pathogenicity prediction tools on somatic variants. J Mol Diagn. 2020;22:1383–92.

    Article  CAS  PubMed  Google Scholar 

  19. Szustakowski JD, Balasubramanian S, Kvikstad E, Khalid S, Bronson PG, Sasson A, et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat Genet. 2021;53:942–8.

    Article  CAS  PubMed  Google Scholar 

  20. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Marioni RE, Batty GD, Hayward C, Kerr SM, Campbell A, Hocking LJ, et al. Common genetic variants explain the majority of the correlation between height and intelligence: the generation Scotland study. Behav Genet. 2014;44:91–6.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ganna A, Verweij KJH, Nivard MG, Maier R, Wedow R, Busch AS, et al. Large-scale GWAS reveals insights into the genetic architecture of same-sex sexual behavior. Science. 2019;365:882–90.

  23. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38:e164.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478:483–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kircher M, Witten DM, Jain P, O’Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet. 2014;46:310–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Rietveld CA, Esko T, Davies G, Pers TH, Turley P, Benyamin B, et al. Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proc Natl Acad Sci USA. 2014;111:13790–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hill WD, Marioni RE, Maghzian O, Ritchie SJ, Hagenaars SP, McIntosh AM, et al. A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence. Mol Psychiatry. 2019;24:169–81.

    Article  CAS  PubMed  Google Scholar 

  28. Tambs K, Sundet JM, Magnus P, Berg K. Genetic and environmental contributions to the covariance between occupational status, educational attainment, and IQ: a study of twins. Behav Genet. 1989;19:209–22.

    Article  CAS  PubMed  Google Scholar 

  29. Quinodoz SA, Jachowicz JW, Bhat P, Ollikainen N, Banerjee AK, Goronzy IN, et al. RNA promotes the formation of spatial compartments in the nucleus. Cell. 2021;184:5775–90. e30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Haworth CM, Wright MJ, Luciano M, Martin NG, de Geus EJ, van Beijsterveldt CE, et al. The heritability of general cognitive ability increases linearly from childhood to young adulthood. Mol Psychiatry. 2010;15:1112–20.

    Article  CAS  PubMed  Google Scholar 

  31. Sohail M, Maier RM, Ganna A, Bloemendal A, Martin AR, Turchin MC, et al. Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife. 2019;8:e39702.

  32. Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife. 2020;9:e48376.

  33. Shen X, Song S, Li C, Zhang J. Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature. 2022;606:725–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Panizzon MS, Vuoksimaa E, Spoon KM, Jacobson KC, Lyons MJ, Franz CE, et al. Genetic and environmental influences of general cognitive ability: is g a valid latent construct? Intelligence. 2014;43:65–76.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet. 2018;50:1112–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19:491–504.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Latvala A, Rose RJ, Pulkkinen L, Dick DM, Kaprio J. Childhood verbal development and drinking behaviors from adolescence to young adulthood: a discordant twin-pair analysis. Alcohol Clin Exp Res. 2014;38:457–65.

    Article  PubMed  Google Scholar 

  38. Kievit RA, Fuhrmann D, Borgeest GS, Simpson-Kent IL, Henson RNA. The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank. Wellcome Open Res. 2018;3:38.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hagenaars SP, Harris SE, Davies G, Hill WD, Liewald DC, Ritchie SJ, et al. Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol Psychiatry. 2016;21:1624–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Franchini LF, Pollard KS. Human evolution: the non-coding revolution. BMC Biol. 2017;15:89.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Supported by the National Natural Science Foundation of China grant 81171880 (SH).

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Authors and Affiliations

  1. Center for Medical Genetics, Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, 110 Xiangya Road, Changsha, Hunan, 410078, PR China

    Mingrui Wang & Shi Huang

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  1. Mingrui Wang
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Contributions

MW and SH devised the project, performed data analysis, and wrote and edited the manuscript.

Corresponding author

Correspondence to Shi Huang.

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The authors declare no competing interests.

Ethical approval

This study utilized deidentified data from the baseline assessment of the UK Biobank, a prospective cohort study of 500,000 individuals (age 40–69 years) recruited across Great Britain during 2006–2010. UK Biobank had obtained ethics approval from the North West Multi-centre Research Ethics Committee, which covers the UK (approval number: 11/NW/0382) and had obtained informed consent from all participants. The UK Biobank approved an application for use of the data (ID 18051).

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Wang, M., Huang, S. The collective effects of genetic variants and complex traits. J Hum Genet 68, 255–262 (2023). https://doi.org/10.1038/s10038-022-01105-1

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