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Structural variants exhibit widespread allelic heterogeneity and shape variation in complex traits

Chakraborty, Mahul, Emerson, J. J., Macdonald, Stuart J., Long, Anthony D.·2019·Nature Communications
DOI 10.1038/s41467-019-12884-1PMC PMC6814777

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Abstract

It has been hypothesized that individually-rare hidden structural variants (SVs) could account for a significant fraction of variation in complex traits. Here we identified more than 20,000 euchromatic SVs from 14 Drosophila melanogaster genome assemblies, of which ~40% are invisible to high specificity short-read genotyping approaches. SVs are common, with 31.5% of diploid individuals harboring a SV in genes larger than 5kb, and 24% harboring multiple SVs in genes larger than 10kb. SV minor allele frequencies are rarer than amino acid polymorphisms, suggesting that SVs are more deleterious. We show that a number of functionally important genes harbor previously hidden structural variants likely to affect complex phenotypes. Furthermore, SVs are overrepresented in candidate genes associated with quantitative trait loci mapped using the Drosophila Synthetic Population Resource. We conclude that SVs are ubiquitous, frequently constitute a heterogeneous allelic series, and can act as rare alleles of large effect.

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Research works printed but not linked: 75

  1. Mauricio, R. Mapping quantitative trait loci in plants: uses and caveats for evolutionary biology. Nat. Rev. Genet. 2, 370–381 (2001). · DOI
  2. Mackay, T. F., Stone, E. A. & Ayroles, J. F. The genetics of quantitative traits: challenges and prospects. Nat. Rev. Genet. 10, 565–577 (2009). · DOI
  3. Goddard, M. E. & Hayes, B. J. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet. 10, 381–391 (2009). · DOI
  4. Stranger, B. E., Stahl, E. A. & Raj, T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics 187, 367–383 (2011). · DOI · Paper page
  5. Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008). · DOI · Paper page
  6. Bansal, V. et al. Accurate detection and genotyping of SNPs utilizing population sequencing data. Genome Res. 20, 537–545 (2010). · DOI
  7. Varshney, R. K., Nayak, S. N., May, G. D. & Jackson, S. A. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol. 27, 522–530 (2009). · DOI
  8. Day-Williams, A. G. & Zeggini, E. The effect of next-generation sequencing technology on complex trait research. Eur. J. Clin. Invest. 41, 561–567 (2011). · DOI
  9. Davey, J. W. et al. Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510 (2011). · DOI
  10. Eichler, E. E. et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat. Rev. Genet. 11, 446–450 (2010). · DOI
  11. Spencer, C. C. A., Su, Z., Donnelly, P. & Marchini, J. Designing Genome-Wide Association Studies: Sample Size, Power, Imputation, and the Choice of Genotyping Chip. PLoS Genet. 5, doi:ARTN e1000477 https://doi.org/10.1371/journal.pgen.1000477 (2009). · DOI
  12. Huddleston, J. & Eichler, E. E. An incomplete understanding of human genetic variation. Genetics 202, 1251–1254 (2016). · DOI
  13. Chakraborty, M. et al. Hidden genetic variation shapes the structure of functional elements in Drosophila. Nat. Genet. 50, 20–25 (2018). · DOI
  14. Emerson, J. J., Cardoso-Moreira, M., Borevitz, J. O. & Long, M. Natural selection shapes genome-wide patterns of copy-number polymorphism in Drosophila melanogaster. Science 320, 1629–1631 (2008). · DOI
  15. Cridland, J. M., Macdonald, S. J., Long, A. D. & Thornton, K. R. Abundance and distribution of transposable elements in two Drosophila QTL mapping resources. Mol. Biol. Evol. 30, 2311–2327 (2013). · DOI
  16. Rogers, R. L. et al. Tandem duplications and the limits of natural selection in Drosophila yakuba and Drosophila simulans. PLoS. One. 10, e0132184 (2015). · DOI
  17. Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017). · DOI
  18. Chakraborty, M., Baldwin-Brown, J. G., Long, A. D. & Emerson, J. J. Contiguous and accurate de novo assembly of metazoan genomes with modest long read coverage. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw654 (2016). · DOI
  19. Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015). · DOI · Paper page
  20. King, E. G. et al. Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource. Genome Res. 22, 1558–1566 (2012). · DOI
  21. Long, A. D., Macdonald, S. J. & King, E. G. Dissecting complex traits using the Drosophila Synthetic Population Resource. Trends Genet. 30, 488–495 (2014). · DOI
  22. mod, E. C. et al. Identification of functional elements and regulatory circuits by Drosophila modENCODE. Science 330, 1787–1797 (2010). · DOI
  23. Graveley, B. R. et al. The developmental transcriptome of Drosophila melanogaster. Nature 471, 473–479 (2011). · DOI
  24. Schwartz, Y. B. et al. Nature and function of insulator protein binding sites in the Drosophila genome. Genome Res. 22, 2188–2198 (2012). · DOI
  25. Chang, C.-H. & Larracuente, A. M. Heterochromatin-enriched assemblies reveal the sequence and organization of the Drosophila melanogaster Y chromosome. Genetics 211, 333–348 (2019). · DOI
  26. Smith, C. D., Shu, S. Q., Mungall, C. J. & Karpen, G. H. The Release 5.1 annotation of Drosophila melanogaster heterochromatin. Science 316, 1586–1591 (2007). · DOI
  27. Khost, D. E., Eickbush, D. G. & Larracuente, A. M. Single-molecule sequencing resolves the detailed structure of complex satellite DNA loci in Drosophila melanogaster. Genome Res. 27, 709–721 (2017). · DOI
  28. Daborn, P. J. et al. A single P450 allele associated with insecticide resistance in Drosophila. Science 297, 2253–2256 (2002). · DOI
  29. Schmidt, J. M. et al. Copy number variation and transposable elements feature in recent, ongoing adaptation at the Cyp6g1 locus. PLoS Genet. 6, e1000998 (2010). · DOI
  30. Yang, W. Y. et al. Functional divergence of six isoforms of antifungal peptide Drosomycin in Drosophila melanogaster. Gene 379, 26–32 (2006). · DOI
  31. Warren, W. D., Palmer, S. & Howells, A. J. Molecular characterization of the cinnabar region of Drosophila melanogaster: identification of the cinnabar transcription unit. Genetica 98, 249–262 (1996). · DOI
  32. dos Santos, G. et al. FlyBase: introduction of the Drosophila melanogaster Release 6 reference genome assembly and large-scale migration of genome annotations. Nucleic Acids Res. 43, D690–D697 (2015). · DOI
  33. Tajima, F. Statistical-method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989). · DOI
  34. Slatkin, M. & Hudson, R. R. Pairwise comparisons of mitochondrial-DNA sequences in stable and exponentially growing populations. Genetics 129, 555–562 (1991). · DOI
  35. Andolfatto, P. Adaptive evolution of non-coding DNA in Drosophila. Nature 437, 1149–1152 (2005). · DOI
  36. Williamson, S. H. et al. Simultaneous inference of selection and population growth from patterns of variation in the human genome. Proc. Natl. Acad. Sci. U. S. A. 102, 7882–7887 (2005). · DOI
  37. Akashi, H. & Schaeffer, S. W. Natural selection and the frequency distributions of “silent” DNA polymorphism in Drosophila. Genetics 146, 295–307 (1997). · DOI
  38. Pritchard, J. K. Are rare variants responsible for susceptibility to complex diseases? Am. J. Hum. Genet. 69, 124–137 (2001). · DOI · Paper page
  39. Thornton, K. R., Foran, A. J. & Long, A. D. Properties and modeling of gwas when complex disease risk is due to non-complementing, deleterious mutations in genes of large effect. PLoS Genet. 9, doi:ARTN e1003258 https://doi.org/10.1371/journal.pgen.1003258 (2013). · DOI
  40. Lohmueller, K. E. The Impact of population demography and selection on the genetic architecture of complex traits. PLoS Genet. 10, doi:ARTN e1004379, https://doi.org/10.1371/journal.pgen.1004379 (2014). · DOI
  41. Sudmant, P. H. et al. An integrated map of structural variation in 2,504 human genomes. Nature 526, 75–+ (2015). · DOI · Paper page
  42. Marriage, T. N., King, E. G., Long, A. D. & Macdonald, S. J. Fine-mapping nicotine resistance loci in Drosophila using a multiparent advanced generation inter-cross population. Genetics 198, 45–57 (2014). · DOI
  43. Stanley, P. D., Ng’oma, E., O’Day, S. & King, E. G. Genetic dissection of nutrition-induced plasticity in insulin/insulin-like growth factor signaling and median life span in a Drosophila multiparent population. Genetics 206, 587–602 (2017). · DOI
  44. Tatar, M. et al. A mutant Drosophila insulin receptor homolog that extends life-span and impairs neuroendocrine function. Science 292, 107–110 (2001). · DOI
  45. Toivonen, J. M. & Partridge, L. Endocrine regulation of aging and reproduction in Drosophila. Mol. Cell. Endocrinol. 299, 39–50 (2009). · DOI
  46. Paaby, A. B., Bergland, A. O., Behrman, E. L. & Schmidt, P. S. A highly pleiotropic amino acid polymorphism in the Drosophila insulin receptor contributes to life-history adaptation. Evolution 68, 3395–3409 (2014). · DOI
  47. Paaby, A. B., Blacket, M. J., Hoffmann, A. A. & Schmidt, P. S. Identification of a candidate adaptive polymorphism for Drosophila life history by parallel independent clines on two continents. Mol. Ecol. 19, 760–774 (2010). · DOI
  48. Brogiolo, W. et al. An evolutionarily conserved function of the Drosophila insulin receptor and insulin-like peptides in growth control. Curr. Biol. 11, 213–221 (2001). · DOI · Paper page
  49. Rauschenbach, I. Y. et al. Interplay of insulin and dopamine signaling pathways in the control of Drosophila melanogaster fitness. Dokl. Biochem. Biophys. 461, 135–138 (2015). · DOI
  50. Wei, Y. et al. Complex cis-regulatory landscape of the insulin receptor gene underlies the broad expression of a central signaling regulator. Development 143, 3591–3603 (2016). · DOI
  51. Negre, N. et al. A cis-regulatory map of the Drosophila genome. Nature 471, 527–531 (2011). · DOI
  52. Mackay, T. F. C. et al. The Drosophila melanogaster Genetic Reference Panel. Nature 482, 173–178 (2012). · DOI
  53. Pool, J. E. et al. Population Genomics of sub-saharan Drosophila melanogaster: African diversity and non-African admixture. PLoS Genet. 8, e1003080 (2012). · DOI
  54. Lack, J. B. et al. The Drosophila genome nexus: a population genomic resource of 623 Drosophila melanogaster genomes, including 197 from a single ancestral range population. Genetics 199, 1229–1241 (2015). · DOI
  55. Ortiz, J. G. M., Opoka, R., Kane, D. & Cartwright, I. L. Investigating arsenic susceptibility from a genetic perspective in drosophila reveals a key role for glutathione synthetase. Toxicol. Sci. 107, 416–426 (2009). · DOI
  56. Logan-Garbisch, T. et al. Developmental ethanol exposure leads to dysregulation of lipid metabolism and oxidative stress in Drosophila. G3-Genes Genom. Genet 5, 49–59 (2015). · DOI
  57. McClellan, J. & King, M. C. Genetic heterogeneity in human disease. Cell 141, 210–217 (2010). · DOI · Paper page
  58. Turelli, M. Heritable genetic-variation via mutation selection balance - lerch zeta meets the abdominal bristle. Theor. Popul. Biol. 25, 138–193 (1984). · DOI
  59. Johnson, T. & Barton, N. Theoretical models of selection and mutation on quantitative traits. Philos. Trans. R. Soc. B-Biol. Sci. 360, 1411–1425 (2005). · DOI
  60. Ye, C., Hill, C. M., Wu, S., Ruan, J. & Ma, Z. S. DBG2OLC: efficient assembly of large genomes using long erroneous reads of the third generation sequencing technologies. Sci. Rep. 6, 31900 (2016). · DOI · Paper page
  61. Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017). · DOI
  62. Solares, E. A. et al. Rapid low-cost assembly of the Drosophila Melanogaste reference genome using low-coverage, long-read sequencing. G3. 8, 3143–3154 (2018). · DOI
  63. Lam, K. K., LaButti, K., Khalak, A. & Tse, D. FinisherSC: a repeat-aware tool for upgrading de novo assembly using long reads. Bioinformatics 31, 3207–3209 (2015). · DOI
  64. Walker, B. J. et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS. One. 9, e112963 (2014). · DOI
  65. Hoskins, R. A. et al. The Release 6 reference sequence of the Drosophila melanogaster genome. Genome Res. 25, 445–458 (2015). · DOI
  66. Kurtz, S. et al. Versatile and open software for comparing large genomes. Genome Biol. 5, R12 (2004). · DOI · Paper page
  67. Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msx319 (2017). · DOI · Paper page
  68. Quinlan, A. R. BEDTools: The Swiss-Army Tool for Genome Feature Analysis. Curr. Protoc. Bioinforma. 47, 11–34 (2014). · DOI
  69. Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002). · DOI · Paper page
  70. Rogers, R. L. et al. Landscape of standing variation for tandem duplications in Drosophila yakuba and Drosophila simulans. Mol. Biol. Evol. 31, 1750–1766 (2014). · DOI
  71. Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009). · DOI
  72. Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011). · DOI · Paper page
  73. King, E. G., Sanderson, B. J., McNeil, C. L., Long, A. D. & Macdonald, S. J. Genetic dissection of the Drosophila melanogaster female head transcriptome reveals widespread allelic heterogeneity. PLoS Genet. 10, e1004322 (2014). · DOI
  74. Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011). · DOI
  75. Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011). · DOI

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Metadata

Authors
Chakraborty, M., Emerson, J. J., Macdonald, S. J., & Long, A. D.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2019-10-25
Volume
10
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-019-12884-1
PMC
PMC6814777
Cited by
186
References
6
Language
en
Licence
Funders
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (K99GM129411)
  • U.S. Department of Health & Human Services | National Institutes of Health (OD010974)

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