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Bird's Eye View of QTLs

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Central Dogma

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Complicated Biochemical Pathways

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From Markers to Maps

Dozens of markers initially provided isolated tests of association

  • early work going back to Sax (1923) and probably earlier

Lander and Botstein (1989) introduced concept of map of markers

  • Analytical calculations (Lander & Botstein 1989)
  • Simulations (Lander & Botstein 1989)
  • Permutation tests (Churchill & Doerge 1994)
  • www.stat.wisc.edu/~yandell/statgen/reference/all.html

Physical maps to DNA base pairs and SNPs

  • progressively denser marker maps
  • DNA sequences to SNPs

https://knowgenetics.org/snps/

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From 1 QTL to Genetic Architecture

Classic QTL: is there evidence for a quantitative trait locus here?

Could there be 2 QTL?

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From 1 QTL to Genetic Architecture

Markers and QTLs

  • Lander-Botstein (1989) interval mapping
  • Haley-Knott (1992) marker regression
  • Kao-Zeng (1997, 2002; and many others) multiple QTLs, additive & dominance
  • Jiang-Zeng (1995, 1997; and many others) multiple traits

Genetic Architecture

  • how many QTL? where are they?
  • is there evidence for epistasis (interaction) among QTL?
  • more detailed issues of gene action (additive, dominance)

Model Selection

  • stats approach to relating response (trait) to predictors (QTL)
  • find "parsimonious" model--not too simple, not over fitting

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Bigger Picture of Genetic Architecture

Many genetic "things" affect organism over its lifetime

  • large, major effects (QTL)
  • minor "modifiers"
  • negligible "polygenes"

Measured trait is a snapshot

  • complicated, ongoing process
  • trait is only an approximation

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Liu et al Laurie (1997)

complicated shape trait

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ordered shape trait

multiple QTL model fit

Zeng et al Laurie (2000) 19 QTL

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From 1 Trait to Multiple Related Traits

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Chaibub Neto et al.

  • 2008 Genetics
  • 2010 AnnApplStat

Pearl 2000 Causality book

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QTL2 Software Resources

There are many resources for R and Rstudio these days. Karl Broman maintains an excellent web site about QTLs, in particular is R/qtl and R/qtl2 packages. Brian Yandell has developed some useful extensions.

  • R/qtl2
  • R/qtl2 extensions (on github)
    • R/qtl2ggplot adds ggplot2 graphics (on CRAN)
    • R/qtl2pattern explores SNP patterns (on CRAN)
    • R/qtl2shiny offers interactive (shiny) app
  • QTL Viewer
  • TIMBR

Many other people developed QTL software over the years, and some of those have been incorporated into comprehensive pipelines. Always insist on open source so that you can evaluate exactly what is done with any tools.

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“The ideal … is the study of the direct influence of one condition on another …[when] all other possible causes of variation are eliminated.... The degree of correlation between two variables … [includes] all connecting paths of influence…. [Path coefficients combine] knowledge of … correlation among the variables in a system with … causal relations.

Sewall Wright (1921) Correlation and causation. J Agric Res

“The old view of cause and effect … could only fail; things are not in our experience either independent or causative. All classes of phenomena are linked together, and the problem in each case is how close is the degree of association.”

Karl Pearson (1911) The Grammar of Science

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Systems Genetics

"phenotypic variation … often results from multiple interactions among numerous genetic and environmental factors. Systems genetics seeks to understand this complexity by integrating the questions and methods of systems biology with those of genetics to solve the fundamental problem of interrelating genotype and phenotype in complex traits and disease."

Joe Nadeau and Aimée Dudley (2011 Science)

Systems genetics is an approach to understand the flow of biological information that underlies complex traits. It … integrate[s] intermediate phenotypes, such as transcript, protein or metabolite levels … [to provide] the first global view of the molecular architecture of complex traits and are useful for the identification of genes, pathways and networks that underlie common human diseases.

Mete Civelek and Jake Lucis (2013 Nature Review Genetics)

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Systems Genetics

Our lab is actively applying a systems approach to study the genetics of health and disease, incorporating new statistical methods for the investigation of complex disease-related traits in the mouse. We are developing new methods and software that will improve the power of quantitative trait loci mapping and microarray analysis, as well as graphical models that aim to characterize the genetic architecture of disease intuitively and precisely.

Gary Churchill Lab Site

www.jax.org/research-and-faculty/research-labs/the-churchill-lab

see extensive references