Following KT Pickard

Followed K. Thomas “KT” Pickard by KT Pickard (GenomeDad Blog)
Big data: From medical imaging to genomics In 2006, a Scientific American article written by George Church, “Genomics for All,” rekindled my interest in genomics. I went back to school in 2009 to contemplate the business of genomic medicine, and celebrated my MBA by writing a Wikipedia entry for the word, “Exome.” I was hooked. We started our odyssey by genotyping our family using 23andMe, and later my wife and I had our whole genomes sequenced. Realizing that genomics were starting to yield clinically useful information, we crowdsourced the sequencing of our kid’s genomes to look for genetic clues in their autism. We found interesting results, gave talks and wrote papers. Along the way, I realized that medical imaging and genomics are highly complementary: genomics informs or identifies conditions, and radiology localizes them. Sarah-Jane Dawson pointed this out at a Future of Genomic Medicine conference in 2014.

Someone who’s also into some of my favorite topics: genomics, medical imaging, and bread? And bonus points for the blog name https://genomedad.com/.

How could I not follow him?

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NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016

Bookmarked NIMBioS Tutorial: Evolutionary Quantitative Genetics 2016 by NIMBioS (nimbios.org)
This tutorial will review the basics of theory in the field of evolutionary quantitative genetics and its connections to evolution observed at various time scales. Quantitative genetics deals with the inheritance of measurements of traits that are affected by many genes. Quantitative genetic theory for natural populations was developed considerably in the period from 1970 to 1990 and up to the present, and it has been applied to a wide range of phenomena including the evolution of differences between the sexes, sexual preferences, life history traits, plasticity of traits, as well as the evolution of body size and other morphological measurements. Textbooks have not kept pace with these developments, and currently few universities offer courses in this subject aimed at evolutionary biologists. There is a need for evolutionary biologists to understand this field because of the ability to collect large amounts of data by computer, the development of statistical methods for changes of traits on evolutionary trees and for changes in a single species through time, and the realization that quantitative characters will not soon be fully explained by genomics. This tutorial aims to fill this need by reviewing basic aspects of theory and illustrating how that theory can be tested with data, both from single species and with multiple-species phylogenies. Participants will learn to use R, an open-source statistical programming language, to build and test evolutionary models. The intended participants for this tutorial are graduate students, postdocs, and junior faculty members in evolutionary biology.

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