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Current Psychiatry Reports

, 15:333 | Cite as

Detecting Rare Variants for Psychiatric Disorders Using Next Generation Sequencing: A Methods Primer

  • Andre Altmann
  • Carina Quast
  • Peter Weber
Genetic Disorders (JF Cubells and EB Binder, Section Editors)
  • 478 Downloads
Part of the following topical collections:
  1. Topical Collection on Genetic Disorders

Abstract

Recent advances in massively parallel sequencing (MPS) have had an extensive impact on research in medical genomics. In particular, the analysis of rare variants using MPS promises to lead to a better understanding of complex disorders. Nevertheless, for meaningful studies that address the genetic basis for neuropsychiatric disorders, at least hundreds of patient samples have to be analyzed. This undertaking is still not feasible for single research groups on a whole-genome scale and in individual samples. Thus, researchers increasingly employ strategies for reducing the amount of sequencing efforts, such as target enrichment and non-barcoded sample pooling. This review provides an overview of current technologies, discusses options for reduced experimental designs, and illustrates the successful application of the presented methodologies in a recent study of panic disorder patients. Thereby, it aims to introduce the emerging field of MPS into neuropsychiatric research and might serve as a guide for further studies.

Keywords

Next-generation sequencing NGS Massively parallel sequencing MPS Targeted re-sequencing Target enrichment Exome enrichment Sample pooling DNA pooling Rare genetic variants Bioinformatics Complex disease Psychiatric disorder Anxiety disorder Panic disorder Human genetics Genetic disorders Genome-wide association studies GWAS Example study Review Psychiatry 

Notes

Disclosure

No potential conflicts of interest relevant to this article were reported.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    A Catalog of Published Genome-Wide Association Studies [database on the Internet]. Available from: http://www.genome.gov/gwastudies/. Accessed: August 8th 2012.
  2. 2.
    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53. doi: 10.1038/nature08494.PubMedCrossRefGoogle Scholar
  3. 3.
    Visscher PM, Hill WG, Wray NR. Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet. 2008;9(4):255–66. doi: 10.1038/nrg2322.PubMedCrossRefGoogle Scholar
  4. 4.
    Hettema JM, Neale MC, Kendler KS. A review and meta-analysis of the genetic epidemiology of anxiety disorders. Am J Psychiatry. 2001;158(10):1568–78.PubMedCrossRefGoogle Scholar
  5. 5.
    Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–52. doi: 10.1038/nature08185.PubMedGoogle Scholar
  6. 6.••
    Maher B. Personal genomes: the case of the missing heritability. Nature. 2008;456(7218):18–21. doi: 10.1038/456018a. Review that points out limitations of published GWAS in common diseases. It introduces to the phenomenon of missing heritability and presents explanation models.PubMedCrossRefGoogle Scholar
  7. 7.
    Frank RA, McRae AF, Pocklington AJ, van de Lagemaat LN, Navarro P, Croning MD, et al. Clustered coding variants in the glutamate receptor complexes of individuals with schizophrenia and bipolar disorder. PLoS One. 2011;6(4):e19011. doi: 10.1371/journal.pone.0019011PONE-D-10-05384.PubMedCrossRefGoogle Scholar
  8. 8.
    Johansen CT, Wang J, Lanktree MB, Cao H, McIntyre AD, Ban MR, et al. Excess of rare variants in genes identified by genome-wide association study of hypertriglyceridemia. Nat Genet. 2010;42(8):684–7. doi: 10.1038/ng.628.PubMedCrossRefGoogle Scholar
  9. 9.
    Trynka G, Hunt KA, Bockett NA, Romanos J, Mistry V, Szperl A, et al. Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease. Nat Genet. 2011;43(12):1193–201. doi: 10.1038/ng.998.PubMedCrossRefGoogle Scholar
  10. 10.
    Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnol. 2008;26(10):1135–45. doi: 10.1038/nbt1486.PubMedCrossRefGoogle Scholar
  11. 11.
    Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431(7011):931–45. doi: 10.1038/nature03001.
  12. 12.
    Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409(6822):860–921. doi: 10.1038/35057062.PubMedCrossRefGoogle Scholar
  13. 13.
    Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291(5507):1304–51. doi: 10.1126/science.1058040291/5507/1304.PubMedCrossRefGoogle Scholar
  14. 14.••
    Metzker ML. Sequencing technologies—the next generation. Nat Rev Genet. 2010;11(1):31–46. doi: 10.1038/nrg2626. A detailed overview over the most used massively parallel sequencing platforms, working principles, and their applications.PubMedCrossRefGoogle Scholar
  15. 15.•
    Li H, Homer N. A survey of sequence alignment algorithms for next-generation sequencing. Brief Bioinform. 2010;11(5):473–83. doi: 10.1093/bib/bbq015. The authors review currently available mapping methods for MPS data.PubMedCrossRefGoogle Scholar
  16. 16.
    Yi X, Liang Y, Huerta-Sanchez E, Jin X, Cuo ZX, Pool JE, et al. Sequencing of 50 human exomes reveals adaptation to high altitude. Science. 2010;329(5987):75–8. doi: 10.1126/science.1190371.PubMedCrossRefGoogle Scholar
  17. 17.
    Stratton M. Genome resequencing and genetic variation. Nat Biotechnol. 2008;26(1):65–6. doi: 10.1038/nbt0108-65.PubMedCrossRefGoogle Scholar
  18. 18.•
    Mamanova L, Coffey AJ, Scott CE, Kozarewa I, Turner EH, Kumar A, et al. Target-enrichment strategies for next-generation sequencing. Nat Methods. 2010;7(2):111–8. doi: 10.1038/nmeth.1419. This review provides a quick and profound overview over target enrichment strategies and some ideas about choosing the most suitable technology.PubMedCrossRefGoogle Scholar
  19. 19.
    Chen X, Listman JB, Slack FJ, Gelernter J, Zhao H. Biases and errors on allele frequency estimation and disease association tests of next-generation sequencing of pooled samples. Genet Epidemiol. 2012. doi: 10.1002/gepi.21648.
  20. 20.
    Altmann A, Weber P, Bader D, Preuß M, Binder EB, Müller-Myhsok B. A beginners guide To SNP calling from high-throughput DNA-sequencing data. Hum Gen. 2012;131(10):1541–54. doi: 10.1007/s00439-012-1213-z.
  21. 21.••
    Nielsen R, Paul JS, Albrechtsen A, Song YS. Genotype and SNP calling from next-generation sequencing data. Nat Rev Genet. 2011;12(6):443–51. doi: 10.1038/nrg2986. A review on currently available SNP calling methods in non-pooled DNA.PubMedCrossRefGoogle Scholar
  22. 22.
    Vallania FL, Druley TE, Ramos E, Wang J, Borecki I, Province M, et al. High-throughput discovery of rare insertions and deletions in large cohorts. Genome Res. 2010;20(12):1711–8. doi: 10.1101/gr.109157.110.PubMedCrossRefGoogle Scholar
  23. 23.
    Bansal V. A statistical method for the detection of variants from next-generation resequencing of DNA pools. Bioinformatics. 2010;26(12):i318–24. doi: 10.1093/bioinformatics/btq214.PubMedCrossRefGoogle Scholar
  24. 24.
    Altmann A, Weber P, Quast C, Rex-Haffner M, Binder EB, Muller-Myhsok B. vipR: variant identification in pooled DNA using R. Bioinformatics. 2011;27(13):i77–84. doi: 10.1093/bioinformatics/btr205.PubMedCrossRefGoogle Scholar
  25. 25.
    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. doi: 10.1093/nar/gkq603.PubMedCrossRefGoogle Scholar
  26. 26.
    Yuan HY, Chiou JJ, Tseng WH, Liu CH, Liu CK, Lin YJ et al. FASTSNP: an always up-to-date and extendable service for SNP function analysis and prioritization. Nucleic Acids Res. 2006;34(Web Server issue):W635–41. doi: 10.1093/nar/gkl236.Google Scholar
  27. 27.
    Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13(9):2129–41. doi: 10.1101/gr.77240313/9/2129.PubMedCrossRefGoogle Scholar
  28. 28.
    Liu X, Jian X, Boerwinkle E. dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Hum Mutat. 2011;32(8):894–9.PubMedCrossRefGoogle Scholar
  29. 29.
    Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009;4(7):1073–81. doi: 10.1038/nprot.2009.86.PubMedCrossRefGoogle Scholar
  30. 30.
    Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006. doi: 10.1101/gr.229102.PubMedGoogle Scholar
  31. 31.
    Quast C, Altmann A, Weber P, Arloth J, Bader D, Heck A et al. Rare variants in TMEM132D in a case-control sample for panic disorder. Am J Med Genet B Neuropsychiatr Genet. 2012;(in press).Google Scholar
  32. 32.
    Erhardt A, Czibere L, Roeske D, Lucae S, Unschuld PG, Ripke S, et al. TMEM132D, a new candidate for anxiety phenotypes: evidence from human and mouse studies. Mol Psychiatr. 2010;16(6):647–63. doi: 10.1038/mp.2010.41.CrossRefGoogle Scholar
  33. 33.
    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. doi: 10.1093/bioinformatics/btp324.PubMedCrossRefGoogle Scholar
  34. 34.••
    Bansal V, Libiger O, Torkamani A, Schork NJ. Statistical analysis strategies for association studies involving rare variants. Nat Rev Genet. 2010;11(11):773–85. doi: 10.1038/nrg2867. An excellent overview about the statistical challenges when working with rare genetic variants.PubMedCrossRefGoogle Scholar
  35. 35.
    Morgenthaler S, Thilly WG. A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: a cohort allelic sums test (CAST). Mutat Res. 2007;615(1–2):28–56. doi: 10.1016/j.mrfmmm.2006.09.003.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Neurology & Neurological Sciences, Functional Imaging in Neurodegenerative Disorders LaboratoryStanford UniversityStanfordUSA
  2. 2.Max Planck Institute of Psychiatry, Molecular Genetics of Affective DisorderMunichGermany
  3. 3.Palo AltoUSA

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