Testing whether trait divergence is neutral by Bruce Walsh

Second Bangalore School on Population Genetics and Evolution

URL: http://www.icts.res.in/program/popgen...

DESCRIPTION:
Just as evolution is central to our understanding of biology, population genetics theory provides the basic framework to comprehend evolutionary processes. Population genetics theory allows quantitative predictions of evolutionary processes, integrating mathematical and statistical concepts with fundamental biological principles of genetic inheritance and processes such as mutation and selection. Population genetics theory is thus critical to understanding many pressing issues in biology, such as the evolution of antibiotic resistance in pathogens, the formation of new species and the emergence of cooperative and altruistic behaviors.

This school aims to expose students and researchers from diverse backgrounds to the basics and the forefront of current research in population genetics. Students from the disciplines of biology, mathematics, medicine, physics, and statistics who are interested in evolutionary theory are all welcome to apply for participation in this program. The school will introduce and develop an understanding of population genetics and quantitative genetics, and their applications. Research seminars and poster sessions will also be held during this school.

ORGANIZERS: Deepa Agashe, Kavita Jain

Table of Contents (powered by https://videoken.com)
0:00:00 CENTRE for THEORETICAL SCIENCES
0:00:06 Bruce Walsh
0:00:44 Oviedo, Asturias, Spain
0:01:18 Lecture 4: Test for Neutrality in Trait Evolution
0:01:53 Outline
0:03:20 Trait mean divergence
0:04:49 Drift in traits
0:08:26 The drift process also generates a correlation among the values of the mean within a given population over time
0:09:55 Measures of divergence
0:11:28 Connection between d2, VB
0:12:12 Chi-square and distributions
0:14:48 Tests for excessive divergence
0:16:05 Lande's Constant Variance test Expected: VB(t) = VA (0) t/Ne
0:17:42 Thus it follows that
0:23:21 Mutation and Drift
0:25:18 Between-group variance at generation
0:27:13 Brownian Motion tests
0:31:22 Tests using are thus based on the normal
0:32:52 Tests of divergence
0:32:56 Example 12.3. Reyment (1982) observed a change 1.490,
0:33:49 Pr (lu(t) - "(0) | d) = pr ( |#(t) -#(0)1 d) =pr
0:37:39 Turelli et al. test
0:37:47 Example 12.3. Reyment (1982) observed a changeof 1.490,
0:38:03 Example 12.4. Let's return to Reyment's foraminiferadata from Example 12.3. Using the original Lande model,
0:41:26 Ornstein-Uhlenbeck Models
0:43:22 Mean 0, Variance b/(2a)
0:44:09 Application to expression data
0:47:10 Fat VS est
0:49:27 Idea
0:51:22 What do the data show?
0:51:48 Lots! Of pitfalls with this approach
0:54:17 QTL-based tests: Orr's method
0:57:51 [Code Walkthrough]
1:02:13 QTL sign test (QTLST)
1:03:57 Two interesting applications of sign tests to particular problems were by Albertson et al.
1:06:15 Divergence in expression
1:06:36 Comparing within vs. between group variation
1:08:53 Cis vs trans
1:11:11 Allele-specific expression (ASE)
1:12:46 eQTLs and sign tests
1:15:17 Example 12.8.