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A Review of Genome-Wide Approaches to Study the Genetic Basis for Spermatogenic Defects

  • Kenneth I. Aston
  • Donald F. Conrad
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 927)

Abstract

Rapidly advancing tools for genetic analysis on a genome-wide scale have been instrumental in identifying the genetic bases for many complex diseases. About half of male infertility cases are of unknown etiology in spite of tremendous efforts to characterize the genetic basis for the disorder. Advancing our understanding of the genetic basis for male infertility will require the application of established and emerging genomic tools. This chapter introduces many of the tools available for genetic studies on a genome-wide scale along with principles of study design and data analysis.

Key words

GWA Whole genome Next-generation sequencing Sperm Male infertility Microarray 

References

  1. 1.
    Iguchi N et al (2006) Expression profiling reveals meiotic male germ cell mRNAs that are translationally up- and down-regulated. Proc Natl Acad Sci USA 103:7712–7717PubMedCrossRefGoogle Scholar
  2. 2.
    Schultz N et al (2003) A multitude of genes expressed solely in meiotic or postmeiotic spermatogenic cells offers a myriad of contraceptive targets. Proc Natl Acad Sci USA 100:12201–12206PubMedCrossRefGoogle Scholar
  3. 3.
    Dohle GR et al (2005) EAU guidelines on male infertility. Eur Urol 48:703–711PubMedCrossRefGoogle Scholar
  4. 4.
    Poongothai J et al (2009) Genetics of human male infertility. Singapore Med J 50:336–347PubMedGoogle Scholar
  5. 5.
    Paduch DA et al (2009) Reproduction in men with Klinefelter syndrome: the past, the present, and the future. Semin Reprod Med 27:137–148PubMedCrossRefGoogle Scholar
  6. 6.
    Martin RH (2008) Cytogenetic determinants of male fertility. Hum Reprod Update 14:379–390PubMedCrossRefGoogle Scholar
  7. 7.
    Lander ES et al (2001) Initial sequencing and analysis of the human genome. Nature 409:860–921PubMedCrossRefGoogle Scholar
  8. 8.
    Almal SH, Padh H (2011) Implications of gene copy-number variation in health and diseases. J Hum Genet 57(1):6–13PubMedCrossRefGoogle Scholar
  9. 9.
    Ku CS et al (2011) Regions of homozygosity and their impact on complex diseases and traits. Hum Genet 129:1–15PubMedCrossRefGoogle Scholar
  10. 10.
    Ku CS et al (2010) The pursuit of genome-wide association studies: where are we now? J Hum Genet 55:195–206PubMedCrossRefGoogle Scholar
  11. 11.
    Conrad DF et al (2010) Origins and functional impact of copy number variation in the human genome. Nature 464:704–712PubMedCrossRefGoogle Scholar
  12. 12.
    Stankiewicz P, Lupski JR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61:437–455PubMedCrossRefGoogle Scholar
  13. 13.
    Grant SF, Hakonarson H (2008) Microarray technology and applications in the arena of genome-wide association. Clin Chem 54:1116–1124PubMedCrossRefGoogle Scholar
  14. 14.
    Davey JW et al (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat Rev Genet 12:499–510PubMedCrossRefGoogle Scholar
  15. 15.
    Oostlander AE et al (2004) Microarray-based comparative genomic hybridization and its applications in human genetics. Clin Genet 66:488–495PubMedCrossRefGoogle Scholar
  16. 16.
    Schuster SC (2008) Next-generation sequencing transforms today’s biology. Nat Methods 5:16–18PubMedCrossRefGoogle Scholar
  17. 17.
    Schadt EE et al (2010) A window into third-generation sequencing. Hum Mol Genet 19:R227–R240PubMedCrossRefGoogle Scholar
  18. 18.
    Glessner JT, Hakonarson H (2011) Genome-wide association: from confounded to confident. Neuroscientist 17:174–184PubMedCrossRefGoogle Scholar
  19. 19.
    Kim SY et al (2010) Design of association studies with pooled or un-pooled next-generation sequencing data. Genet Epidemiol 34:479–491PubMedCrossRefGoogle Scholar
  20. 20.
    Pahl R et al (2009) Optimal multistage designs – a general framework for efficient genome-wide association studies. Biostatistics 10:297–309PubMedCrossRefGoogle Scholar
  21. 21.
    Zondervan KT, Cardon LR (2007) Designing candidate gene and genome-wide case-control association studies. Nat Protoc 2:2492–2501PubMedCrossRefGoogle Scholar
  22. 22.
    Anderson CA et al (2010) Data quality control in genetic case-control association studies. Nat Protoc 5:1564–1573PubMedCrossRefGoogle Scholar
  23. 23.
    (2010) A map of human genome variation from population-scale sequencing. Nature 467: 1061–1073Google Scholar
  24. 24.
    Sebastiani P et al (2011) Retraction. Science 333:404PubMedCrossRefGoogle Scholar
  25. 25.
    Storey JD (2003) The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Statist 31:2013–2035CrossRefGoogle Scholar
  26. 26.
    Clarke GM et al (2011) Basic statistical analysis in genetic case-control studies. Nat Protoc 6:121–133PubMedCrossRefGoogle Scholar
  27. 27.
    Pe’er I et al (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 32:381–385PubMedCrossRefGoogle Scholar
  28. 28.
    Barnes C et al (2008) A robust statistical method for case-control association testing with copy number variation. Nat Genet 40:1245–1252PubMedCrossRefGoogle Scholar
  29. 29.
    International Schizophrenia Consortium (2008) Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455:237–241CrossRefGoogle Scholar
  30. 30.
    Lencz T et al (2007) Runs of homozygosity reveal highly penetrant recessive loci in schizophrenia. Proc Natl Acad Sci USA 104:19942–19947PubMedCrossRefGoogle Scholar
  31. 31.
    Nalls MA et al (2009) Extended tracts of homozygosity identify novel candidate genes associated with late-onset Alzheimer’s disease. Neurogenetics 10:183–190PubMedCrossRefGoogle Scholar
  32. 32.
    Browning SR, Browning BL (2010) High-resolution detection of identity by descent in unrelated individuals. Am J Hum Genet 86:526–539PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Department of Surgery, Division of Urology, Andrology & IVF LaboratoriesUniversity of Utah School of MedicineSalt Lake CityUSA
  2. 2.Department of GeneticsWashington University School of MedicineSt. LouisUSA

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