Data Production and Analysis in Population Genomics pp 235-260

Part of the Methods in Molecular Biology book series (MIMB, volume 888) | Cite as

Population Genomic Analysis of Model and Nonmodel Organisms Using Sequenced RAD Tags

  • Paul A. Hohenlohe
  • Julian Catchen
  • William A. Cresko

Abstract

The evolutionary processes of mutation, migration, genetic drift, and natural selection shape patterns of genetic variation among individuals, populations, and species, and they can do so differentially across genomes. The field of population genomics provides a comprehensive genome-scale view of these processes, even beyond traditional model organisms. Until recently, genome-wide studies of genetic variation have been prohibitively expensive. However, next-generation sequencing (NGS) technologies are revolutionizing the field of population genomics, allowing for genetic analysis at scales not previously possible even in organisms for which few genomic resources presently exist. To speed this revolution in evolutionary genetics, we and colleagues developed Restriction site Associated DNA (RAD) sequencing, a method that uses Illumina NGS to simultaneously type and score tens to hundreds of thousands of single nucleotide polymorphism (SNP) markers in hundreds of individuals for minimal investment of resources. The core molecular protocol is described elsewhere in this volume, which can be modified to suit a diversity of evolutionary genetic questions. In this chapter, we outline the conceptual framework of population genomics, relate genomic patterns of variation to evolutionary processes, and discuss how RAD sequencing can be used to study population genomics. In addition, we discuss bioinformatic considerations that arise from unique aspects of NGS data as compared to traditional marker based approaches, and we outline some general analytical approaches for RAD-seq and similar data, including a computational pipeline that we developed called Stacks. This software can be used for the analysis of RAD-seq data in organisms with and without a reference genome. Nonetheless, the development of analytical tools remains in its infancy, and further work is needed to fully quantify sampling variance and biases in these data types. As data-gathering technology continues to advance, our ability to understand genomic evolution in natural populations will be limited more by conceptual and analytical weaknesses than by the amount of molecular data.

Key words

Genetic mapping Population genetics Genomics Evolution Genotyping Single nucleotide polymorphisms Next-generation sequencing RAD-seq 

References

  1. 1.
    Fisher RA (1958) The genetical theory of ­natural selection. Dover, New YorkGoogle Scholar
  2. 2.
    Wright S (1931) Evolution in Mendelian populations. Genetics 16:97–159PubMedGoogle Scholar
  3. 3.
    Kimura M (1991) Recent development of the neutral theory viewed from the Wrightian tradition of theoretical population genetics. Proc Natl Acad Sci USA 88:5969–5973PubMedCrossRefGoogle Scholar
  4. 4.
    Wright S (1978) Evolution and the genetics of populations. University of Chicago Press, ChicagoGoogle Scholar
  5. 5.
    Avise JC (2004) Molecular markers, natural history and evolution, 2nd edn. Sinauer Associates, Sunderland, MAGoogle Scholar
  6. 6.
    Birney E, Stamatoyannopoulos JA, Dutta A et al (2007) Identification and analysis of ­functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447:799–816PubMedCrossRefGoogle Scholar
  7. 7.
    Stranger BE, Nica AC, Forrest MS et al (2007) Population genomics of human gene expression. Nat Genet 39:1217–1224PubMedCrossRefGoogle Scholar
  8. 8.
    Sabeti PC, Varilly P, Fry B et al (2007) Genome-wide detection and characterization of positive selection in human populations. Nature 449:913–918PubMedCrossRefGoogle Scholar
  9. 9.
    Beaumont MA, Balding DJ (2004) Identifying adaptive genetic divergence among populations from genome scans. Mol Ecol 13:969–980PubMedCrossRefGoogle Scholar
  10. 10.
    Liti G, Carter DM, Moses AM et al (2009) Population genomics of domestic and wild yeasts. Nature 458:337–341PubMedCrossRefGoogle Scholar
  11. 11.
    Rockman MV, Kruglyak L (2009) Recombinational landscape and population genomics of Caenorhabditis elegans. PLoS Genet 5:e1000419PubMedCrossRefGoogle Scholar
  12. 12.
    Butlin RK (2010) Population genomics and speciation. Genetica 138:409–418PubMedCrossRefGoogle Scholar
  13. 13.
    Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003) The power and promise of population genomics: from genotyping to genome typing. Nat Rev Genet 4:981–994PubMedCrossRefGoogle Scholar
  14. 14.
    Slatkin M (2008) Linkage disequilibrium – understanding the evolutionary past and ­mapping the medical future. Nat Rev Genet 9:477–485PubMedCrossRefGoogle Scholar
  15. 15.
    Pritchard JK, Pickrell JK, Coop G (2010) The genetics of human adaptation: hard sweeps, soft sweeps, and polygenic adaptation. Curr Biol 20:R208–R215PubMedCrossRefGoogle Scholar
  16. 16.
    Charlesworth B, Betancourt AJ, Kaiser VB, Gordo I (2009) Genetic recombination and molecular evolution. Cold Spring Harb Symp Quant Biol 74:177–186PubMedCrossRefGoogle Scholar
  17. 17.
    Boitard S, Schlotterer C, Futschik A (2009) Detecting selective sweeps: a new approach based on hidden markov models. Genetics 181:1567–1578PubMedCrossRefGoogle Scholar
  18. 18.
    Nielsen R, Williamson S, Kim Y et al (2005) Genomic scans for selective sweeps using SNP data. Genome Res 15:1566–1575PubMedCrossRefGoogle Scholar
  19. 19.
    Pickrell JK, Coop G, Novembre J et al (2009) Signals of recent positive selection in a worldwide sample of human populations. Genome Res 19:826–837PubMedCrossRefGoogle Scholar
  20. 20.
    Grossman SR, Shylakhter I, Karlsson EK et al (2010) A composite of multiple signals distinguishes causal variants in regions of positive selection. Science 327:883–886PubMedCrossRefGoogle Scholar
  21. 21.
    Przeworski M, Coop G, Wall JD (2005) The signature of positive selection on standing genetic variation. Evolution 59:2312–2323PubMedCrossRefGoogle Scholar
  22. 22.
    Hermisson J, Pennings PS (2005) Soft sweeps: molecular population genetics of adaptation from standing genetic variation. Genetics 169:2335–2352PubMedCrossRefGoogle Scholar
  23. 23.
    Storz JF (2005) Using genome scans of DNA polymorphism to infer adaptive population divergence. Mol Ecol 14:671–688PubMedCrossRefGoogle Scholar
  24. 24.
    Hohenlohe PA, Phillips PC, Cresko WA (2010) Using population genomics to detect selection in natural populations: key concepts and methodological considerations. Int J Plant Sci 171(9):1059–1071Google Scholar
  25. 25.
    Teshima KM, Coop G, Przeworski M (2006) How reliable are empirical genomic scans for selective sweeps? Genome Res 16:702–712PubMedCrossRefGoogle Scholar
  26. 26.
    Wares JP (2010) Natural distributions of mitochondrial sequence diversity support new null hypotheses. Evolution 64:1136–1142PubMedCrossRefGoogle Scholar
  27. 27.
    Hohenlohe P, Bassham S, Stiffler N, Johnson EA, Cresko WA (2010) Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet 6:e1000862PubMedCrossRefGoogle Scholar
  28. 28.
    Akey JM (2009) Constructing genomic maps of positive selection in humans: where do we go from here? Genome Res 19:711–722PubMedCrossRefGoogle Scholar
  29. 29.
    Pool JE, Hellmann I, Jensen JD, Nielsen R (2010) Population genetic inference from genomic sequence variation. Genome Res 20:291–300PubMedCrossRefGoogle Scholar
  30. 30.
    Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5:621–628PubMedCrossRefGoogle Scholar
  31. 31.
    Marguerat S, Wilhelm BT, Bahler J (2008) Next-generation sequencing: applications beyond genomes. Biochem Soc Trans 36:1091–1096PubMedCrossRefGoogle Scholar
  32. 32.
    Mardis ER (2008) Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet 9:387–402PubMedCrossRefGoogle Scholar
  33. 33.
    Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26:1135–1145PubMedCrossRefGoogle Scholar
  34. 34.
    Mardis ER (2008) The impact of next-generation sequencing technology on genetics. Trends Genet 24:133–141PubMedCrossRefGoogle Scholar
  35. 35.
    Van Tassell CP, Smith TP, Matukumalli LK et al (2008) SNP discovery and allele frequency estimation by deep sequencing of reduced representation libraries. Nat Methods 5:247–252PubMedCrossRefGoogle Scholar
  36. 36.
    Baird NA, Etter PD, Atwood TS et al (2008) Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLoS One 3:e3376PubMedCrossRefGoogle Scholar
  37. 37.
    Emerson KJ, Merz CR, Catchen JM et al (2010) Resolving post-glacial phylogeography using high throughput sequencing. Proc Natl Acad Sci USAGoogle Scholar
  38. 38.
    Gompert Z, Lucas LK, Fordyce JA, Forister ML, Nice CC (2010) Secondary contact between Lycaeides idas and L. melissa in the Rocky Mountains: extensive admixture and a patchy hybrid zone. Mol Ecol 19:3171–3192PubMedCrossRefGoogle Scholar
  39. 39.
    Rokas A, Abbot P (2009) Harnessing genomics for evolutionary insights. Trends Ecol Evol 24:192–200PubMedCrossRefGoogle Scholar
  40. 40.
    Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA (2007) Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res 17:240–248PubMedCrossRefGoogle Scholar
  41. 41.
    Hohenlohe PA, Amish JS, Catchen MJ, Allendorf WF, Luikart G (2011) Next-Generation RAD Sequencing Identifies Thousands of SNPs for Assessing Hybridization Between Rainbow and Westslope Cutthroat Trout. Molecular Ecology Resources 11 (Suppl 1):117–122Google Scholar
  42. 42.
    Dettman JR, Anderson JB, Kohn LM (2010) Genome-wide investigation of reproductive isolation in experimental lineages and natural species of Neurospora: identifying candidate regions by microarray-based genotyping and mapping. Evolution 64:694–709PubMedCrossRefGoogle Scholar
  43. 43.
    Lewis ZA, Shiver AL, Stiffler N et al (2007) High-density detection of restriction-site-­associated DNA markers for rapid mapping of mutated loci in Neurospora. Genetics 177:1163–1171PubMedCrossRefGoogle Scholar
  44. 44.
    Miller MR, Atwood TS, Eames BF et al (2007) RAD marker microarrays enable rapid mapping of zebrafish mutations. Genome Biol 8:R105PubMedCrossRefGoogle Scholar
  45. 45.
    Amores A, Catchen J, Ferrara A, Fontenot Q, Postlethwait JH (2011) Genome Evolution and Meiotic Maps by Massively Parallel DNA Sequencing: Spotted Gar, an Outgroup for the Teleost Genome Duplication. Genetics 188(4): 799–808Google Scholar
  46. 46.
    Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res 8:186–194PubMedGoogle Scholar
  47. 47.
    Ewing B, Hillier L, Wendl MC, Green P (1998) Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res 8:175–185PubMedGoogle Scholar
  48. 48.
    Altschul SF, Madden TL, Schaffer AA et al (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25:3389–3402PubMedCrossRefGoogle Scholar
  49. 49.
    Kent WJ (2002) BLAT – the BLAST-like alignment tool. Genome Res 12:656–664PubMedGoogle Scholar
  50. 50.
    Vinga S, Almeida J (2003) Alignment-free sequence comparison-a review. Bioinformatics 19:513–523PubMedCrossRefGoogle Scholar
  51. 51.
    Simpson JT, Wong K, Jackman SD et al (2009) ABySS: a parallel assembler for short read sequence data. Genome Res 19:1117–1123PubMedCrossRefGoogle Scholar
  52. 52.
    Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18:821–829PubMedCrossRefGoogle Scholar
  53. 53.
    Charlesworth B, Nordborg M, Charlesworth D (1997) The effects of local selection, balanced polymorphism and background selection on equilibrium patterns of genetic diversity in subdivided populations. Genet Res 70:155–174PubMedCrossRefGoogle Scholar
  54. 54.
    Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–595PubMedGoogle Scholar
  55. 55.
    Thornton K (2005) Recombination and the properties of Tajima’s D in the context of approximate-likelihood calculation. Genetics 171:2143–2148PubMedCrossRefGoogle Scholar
  56. 56.
    Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured population. Heredity 103:285–298PubMedCrossRefGoogle Scholar
  57. 57.
    Bedford T, Cobey S, Beerli P, Pascual M (2010) Global migration dynamics underlie evolution and persistence of human influenza A (H3N2). PLoS Pathog 6:e1000918PubMedCrossRefGoogle Scholar
  58. 58.
    Beerli P, Palczewski M (2010) Unified framework to evaluate panmixia and migration direction among multiple sampling locations. Genetics 185:313–326PubMedCrossRefGoogle Scholar
  59. 59.
    Gutenkunst RN, Hernandez RD, Williamson SH, Bustamante CD (2009) Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet 5:e1000695PubMedCrossRefGoogle Scholar
  60. 60.
    Lynch M (2009) Estimation of allele frequencies from high-coverage genome-sequencing projects. Genetics 182:295–301PubMedCrossRefGoogle Scholar
  61. 61.
    Holsinger KE, Weir BS (2009) Genetics in geographically structure populations: defining, estimating and interpreting FST. Nat Rev Genet 10:639–650PubMedCrossRefGoogle Scholar
  62. 62.
    Schlotterer C, Kauer M, Dieringer D (2004) Allele excess at neutrally evolving microsatellites and the implications for tests of neutrality. Proc Biol Sci 271:869–874PubMedCrossRefGoogle Scholar
  63. 63.
    Kelly JK (2006) Geographical variation in selection, from phenotypes to molecules. Am Nat 167:481–495PubMedCrossRefGoogle Scholar
  64. 64.
    Storz JF, Kelly JK (2008) Effects of spatially varying selection on nucleotide diversity and linkage disequilibrium: insights from deer mouse globin genes. Genetics 180:367–379PubMedCrossRefGoogle Scholar
  65. 65.
    Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CrossRefGoogle Scholar
  66. 66.
    Slatkin M (1991) Inbreeding coefficients and coalescence times. Genet Res 58:167–175PubMedCrossRefGoogle Scholar
  67. 67.
    Beaumont MA (2005) Adaptation and speciation: what can Fst tell us? Trends Ecol Evol 20:435–440PubMedCrossRefGoogle Scholar
  68. 68.
    Sabeti P, Reich DE, Higgins JM et al (2002) Detecting recent positive selection in the human genome from haplotype structure. Nature 419:832–837PubMedCrossRefGoogle Scholar
  69. 69.
    Kane NC, Rieseberg LH (2007) Selective sweeps reveal candidate genes for adaptation to drought and salt tolerance in common sunflower, Helianthus annuus. Genetics 175: 1823–1834PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Paul A. Hohenlohe
    • 1
  • Julian Catchen
    • 1
  • William A. Cresko
    • 1
  1. 1.Center for Ecology and Evolutionary BiologyUniversity of OregonEugeneUSA

Personalised recommendations