Population genomics

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Population genomics is the large-scale comparison of DNA sequences of populations. Population genomics is a neologism that is associated with population genetics. Population genomics studies genome wide effects to improve our understanding of microevolution so that we may learn the phylogenetic history and demography of a population.[1]

History[edit]

Population genomics has been of interest to scientists since Darwin. Some of the first methods used for studying genetic variability at multiple loci included gel electrophoresis and restriction enzyme mapping.[2] Previously genomics was restricted to only the study of a low amount of loci. However recent advancements in sequencing and computer storage and power have allowed for the study of hundreds of thousands of loci from populations.[3] Analysis of this data requires identification of non-neutral or outlier loci that indicate selection in that region of the genome. This will allow the researcher to remove these loci to study genome wide effects or to focus on these loci if they are of interest.

Research Applications[edit]

In the study of S. pombe (more commonly known as fission yeast), a popular model organism, population genomics has been used to understand the reason for the phenotypic variation within a species. However since the genetic variation within this species was previously poorly understood due to technological restrictions, population genomics allows us to learn about the species' genetic differences.[4] In the human population, population genomics has been used to study the genetic change since humans began to migrate away from Africa approximately 50,000-100,000 years ago. It has been shown that not only were genes related to fertility and reproduction highly selected for, but also that the further humans moved away from Africa, the greater the presence of lactase.[5]

A 2007 study done by Begun et al. compared the whole genome sequence of multiple lines of Drosophila simulans to the assembly of D. melanogaster and D. yakuba. This was done by aligning DNA from whole genome shotgun sequences of D. simulans to a standard reference sequence before carrying out whole genome analysis of polymorphism and divergence. This revealed a large number of proteins that had experienced directional selection. They discovered previously unknown, large scale fluctuations in both polymorphism and divergence along chromosome arms. They found that the X chromosome had faster divergence and significantly less polymorphism than previously expected. They also found regions of the genome (e.g. UTRs) that signaled adaptive evolution.[6]

In 2014 Jacquot et al. studied the diversification and epidemiology of endemic bacterial pathogens by using the Borrelia burgdorferi species complex (the bacteria responsible for Lyme disease) as a model. They also wished to compare the genetic structure between B. burgdorferi and the closely related species B. garinii and B. afzelii. They began by sequencing samples from a culture and then mapping the raw read onto reference sequences. SNP based and phylogenetic analyses were used on both intraspecific and interspecific levels. When looking at the degree of genetic isolation, they found that intraspecific recombination rate was ~50 times higher than the interspecific rate. They also found that by using most of the genome conspecific strains didn’t cluster in clades, raising questions about previous strategies used when investigating pathogen epidemiology.[7]

Mathematical Models[edit]

Understanding and analyzing the vast data that comes from population genomics studies requires various mathematical models. One method of analyzing this vast data is through QTL mapping. QTL mapping has been used to help find the genes that are responsible for adaptive phenotypes.[8] To quantify the genetic diversity within a population a value known as the fixation index, or FST is used. When used with Tajima's D, FST has been used to show how selection acts upon a population.[9] The McDonald-Kreitman test (or MK test) is also favored when looking for selection because it is not as sensitive to changes in a species' demography that would throw off other selection tests.[10]

Future Developments[edit]

Most developments within population genomics have to do with increases in the sequencing technology. For example restriction-site associated DNA sequencing, or RADSeq is a relatively new technology that sequences at a lower complexity and delivers higher resolution at a reasonable cost.[11] High-throughput sequencing technologies are also a rapidly growing field that allows for more information to be gathered on genomic divergence during speciation.[12] High-throughput sequencing is also very useful for SNP detection, which plays a key role in personalized medicine.[13] Another relatively new approach is reduced-representation library (RRL) sequencing which discovers and genotypes SNPs and also doesn't require reference genomes.[14]

See also[edit]

External links[edit]

Notes[edit]

  1. ^ Luikart, G.; England, P. R.; Tallmon, D.; Jordan S.; Taberlet P. (2003). "The Power and Promise of Population Genomics: From Genotyping to Genome Typing". Nature Reviews (4): 981-994
  2. ^ Charlesworth, B. (2011). "Molecular population genomics: A short history". Genetics Research 92 (5–6): 397. doi:10.1017/S0016672310000522.  edit
  3. ^ Schilling, M. P.; Wolf, P. G.; Duffy, A. M.; Rai, H. S.; Rowe, C. A.; Richardson, B. A.; Mock, K. E. (2014). "Genotyping-by-Sequencing for Populus Population Genomics: An Assessment of Genome Sampling Patterns and Filtering Approaches". PLoS ONE 9 (4): e95292. doi:10.1371/journal.pone.0095292. PMID 24748384.  edit
  4. ^ Fawcett, J. A.; Iida, T.; Takuno, S.; Sugino, R. P.; Kado, T.; Kugou, K.; Mura, S.; Kobayashi, T.; Ohta, K.; Nakayama, J. I.; Innan, H. (2014). "Population Genomics of the Fission Yeast Schizosaccharomyces pombe". PLoS ONE 9 (8): e104241. doi:10.1371/journal.pone.0104241.  edit
  5. ^ Lachance, J.; Tishkoff, S. A. (2013). "Population Genomics of Human Adaptation". Annual Review of Ecology, Evolution, and Systematics 44: 123. doi:10.1146/annurev-ecolsys-110512-135833.  edit
  6. ^ Begun, D. J.; Holloway, A. K.; Stevens, K.; Hillier, L. W.; Poh, Y. P.; Hahn, M. W.; Nista, P. M.; Jones, C. D.; Kern, A. D.; Dewey, C. N.; Pachter, L.; Myers, E.; Langley, C. H. (2007). "Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans". PLoS Biology 5 (11): e310. doi:10.1371/journal.pbio.0050310.  edit
  7. ^ Jacquot, M.; Gonnet, M.; Ferquel, E.; Abrial, D.; Claude, A.; Gasqui, P.; Choumet, V. R.; Charras-Garrido, M.; Garnier, M.; Faure, B.; Sertour, N.; Dorr, N.; De Goër, J.; Vourc'h, G. L.; Bailly, X. (2014). "Comparative Population Genomics of the Borrelia burgdorferi Species Complex Reveals High Degree of Genetic Isolation among Species and Underscores Benefits and Constraints to Studying Intra-Specific Epidemiological Processes". PLoS ONE 9 (4): e94384. doi:10.1371/journal.pone.0094384.  edit
  8. ^ Stinchcombe, J. R.; Hoekstra, H. E. (2007). "Combining population genomics and quantitative genetics: Finding the genes underlying ecologically important traits". Heredity 100 (2): 158. doi:10.1038/sj.hdy.6800937.  edit
  9. ^ Hohenlohe, P. A.; Bassham, S.; Etter, P. D.; Stiffler, N.; Johnson, E. A.; Cresko, W. A. (2010). "Population Genomics of Parallel Adaptation in Threespine Stickleback using Sequenced RAD Tags". PLoS Genetics 6 (2): e1000862. doi:10.1371/journal.pgen.1000862.  edit
  10. ^ Harpur, B. A.; Kent, C. F.; Molodtsova, D.; Lebon, J. M. D.; Alqarni, A. S.; Owayss, A. A.; Zayed, A. (2014). "Population genomics of the honey bee reveals strong signatures of positive selection on worker traits". Proceedings of the National Academy of Sciences 111 (7): 2614. doi:10.1073/pnas.1315506111.  edit
  11. ^ Davey, J. W.; Blaxter, M. L. (2011). "RADSeq: Next-generation population genetics". Briefings in Functional Genomics 9 (5–6): 416. doi:10.1093/bfgp/elq031.  edit
  12. ^ Ellegren, H. (2014). "Genome sequencing and population genomics in non-model organisms". Trends in Ecology & Evolution 29: 51. doi:10.1016/j.tree.2013.09.008.  edit
  13. ^ You, N.; Murillo, G.; Su, X.; Zeng, X.; Xu, J.; Ning, K.; Zhang, S.; Zhu, J.; Cui, X. (2012). "SNP calling using genotype model selection on high-throughput sequencing data". Bioinformatics 28 (5): 643. doi:10.1093/bioinformatics/bts001.  edit
  14. ^ Greminger, M. P.; Stölting, K. N.; Nater, A.; Goossens, B.; Arora, N.; Bruggmann, R. M.; Patrignani, A.; Nussberger, B.; Sharma, R.; Kraus, R. H. S.; Ambu, L. N.; Singleton, I.; Chikhi, L.; Van Schaik, C. P.; Krützen, M. (2014). "Generation of SNP datasets for orangutan population genomics using improved reduced-representation sequencing and direct comparisons of SNP calling algorithms". BMC Genomics 15: 16. doi:10.1186/1471-2164-15-16.  edit

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