Doing some background reading on genomics topics such as:

  • Cross-population predictive validity of genomic scores (polygenic scores).
  • LD-decay.
  • Causal variant tagging / distribution of causal variants / genetic architecture.
  • Admixture mapping accuracy given LD decay.
  • Implications of the above for research into genetic causes of group differences in polygenic traits.
  • Implications of the above for the genomic improvement of humans via direct editing vs. embryo selection.

Papers:

  • Lee, J. J., Vattikuti, S., & Chow, C. C. (2016). Uncovering the Genetic Architectures of Quantitative Traits. Computational and Structural Biotechnology Journal, 14, 28–34. https://doi.org/10.1016/j.csbj.2015.10.002
    • Nice review. Pushes for LASSO regression! :) Has simplish explanations of GREML and LD regression.
  • Ntzani, E. E., Liberopoulos, G., Manolio, T. A., & Ioannidis, J. P. A. (2012). Consistency of genome-wide associations across major ancestral groups. Human Genetics, 131(7), 1057–1071. https://doi.org/10.1007/s00439-011-1124-4
  • Mahajan, A., Go, M. J., Zhang, W., Below, J. E., Gaulton, K. J., Ferreira, T., … Morris, A. P. (2014). Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nature Genetics, 46(3), 234–244. https://doi.org/10.1038/ng.2897
  • Sanna, S., Li, B., Mulas, A., Sidore, C., Kang, H. M., Jackson, A. U., … Abecasis, G. R. (2011). Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability. PLoS Genetics, 7(7). https://doi.org/10.1371/journal.pgen.1002198
  • Li, Y. R., & Keating, B. J. (2014). Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Medicine, 6, 91. https://doi.org/10.1186/s13073-014-0091-5
  • Mancuso, N., Shi, H., Goddard, P., Kichaev, G., Gusev, A., & Pasaniuc, B. (2017). Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits. The American Journal of Human Genetics, 0(0). https://doi.org/10.1016/j.ajhg.2017.01.031
  • Zanetti, D., & Weale, M. E. (2016). True causal effect size heterogeneity is not required to explain trans-ethnic differences in GWAS signals. bioRxiv, 085092. https://doi.org/10.1101/085092
    • Very neat simulation study suggesting that LD decay is enough to explain cross-population replication rates in humans, and that we don’t need to posit different causal variants. Important if true! They used 1000 genomes data to do this. Interestingly enough, I independently came up with this method and was thus very pleased to see that someone already carried it out!
  • Veroneze, R. (2015). Linkage disequilibrium and genomic selection in pigs. Retrieved from https://www.researchgate.net/publication/283416305_Linkage_disequilibrium_and_genomic_selection_in_pigs
  • Samorè, A. B., & Fontanesi, L. (2016). Genomic selection in pigs: state of the art and perspectives. Italian Journal of Animal Science, 15(2), 211–232. https://doi.org/10.1080/1828051X.2016.1172034

The nice thing about reading the animal breeding literature is that they do not beat around the bush, but get directly to the point: how much, how reliably, how fast can be improve the genetic value of the stock? In animal breeding, they call the value to be optimized for the GEBV, the genomic breeding value. For humans, this is essentially a weighted mean of the traits and their related genomic variants. Different people will want to optimize in somewhat different directions, but nearly everybody wants to be more healthy and so this represents a plausible stepping stone to getting to cognitive ability.