Opportunities and challenges
in the genetic analysis of
high-throughput phenotypes

Karl Broman

Biostatistics & Medical Informatics, University of Wisconsin – Madison

kbroman.org
github.com/kbroman
@kwbroman

slides: bit.ly/HTP2015

Inbred mice

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Human vs mouse

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Genome scan for QTL

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Genome scan for QTL

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Genome-scale phenotypes

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Why?

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But more is not necessarily better

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Challenges: diagnostics

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Challenges: diagnostics

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Challenges: metadata

What the heck is “FAD_NAD SI 8.3_3.3G”?

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Challenges: Organizing & automating

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Challenges: Scale of the results

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What was the question again?

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The ridiculome

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Pleiotropy?

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Causal?

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Multivariate analysis

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Multivariate analysis

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Composite phenotypes

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Share more data, sooner.

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Training

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Interactive graphics

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All graphs can be improved with interactivity

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Genome scan for a longitudinal phenotype

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Interactivity is great for teaching

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Interactive eQTL plot

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D3 is awesome!

You just need to learn html, css, svg, and javascript.

And don’t forget .enter()

http://mbostock.github.io/d3/talk/20111018/collision.html

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R/qtlcharts

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Summary

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Acknowledgments

Alan Attie
Mark Keller
Biochemistry, UW–Madison
Brian Yandell Statistics and Horticulture, UW–Madison
Christina Kendziorski
Aimee Teo Broman
Biostatistics & Medical Informatics, UW–Madison
Eric Schadt Mount Sinai
Danielle Greenawalt
Amit Kulkarni
Merck & Co., Inc.
Śaunak Sen Epidemiology & Biostatistics, UC-San Francisco
Edgar Spalding
Candace Moore
Logan Johnson
Botany, UW-Madison

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slides: bit.ly/HTP2015  

kbroman.org

github.com/kbroman

@kwbroman

kbroman.org/qtlcharts

31