Measuring fitness and selection on traits 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 INTERNATIONAL
0:00:03 Genetics and
0:00:06 Bruce Walsh
0:01:30 Volume 2: Evolution and Selection of Quantitative Traits Bruce Walsh and Michael Lynch
0:02:43 Changes in the Variance under Selection The infinitesimal model -- each locus has a very small effect on the trait.
0:07:18 Additive variance with LD:
0:10:34 Key: Under the infinitesimal model, no (selection-induced) changes in genic variance o'a
0:13:27 Consider the parent-offspring regression
0:16:28 Change in d = change from recombination plus change from selection
0:18:45 Rendel (1943) observed that while the change mean weight weight (in all vs.
0:27:27 Specific models of selection-induced changes in variances
0:29:27 Equilibrium h2 under direction truncation selection
0:30:23 Directional truncation selection
0:30:36 Disruptive Selection Stabilizing Selection
0:34:07 Changes in the variance - changes in h? and even (under truncation selection)
0:41:09 Multivariate Selection
0:41:12 Genetic vs. Phenotypic correlations
0:42:38 The phenotypic values between traits and within an individual are correlated
0:43:30 Genetic & Environmental Correlations
0:44:19 The relative contributions of genetic and environmental correlations to the phenotypic correlation
0:44:53 Estimating Genetic Correlations
0:49:03 Correlated Response to Selection
0:51:25 Phenotypic correlations induce within-generation changes Phenotypic values
0:52:34 Example
0:53:23 Phenotypic values are misleading,
0:54:45 Predicting the correlated response The change in character in response to selection on is the regression of the breeding value of
0:56:11 CR = i h, hyrA Op (y)
0:57:43 Direct vs.
1:02:28 Matrices
1:03:08 CR = i h, hrA Op (y)
1:03:09 We can rewrite CRy = bAylAx Rx as follows First, note that RX = h2,
1:03:24 CR = ihhyrA Op (y)
1:03:59 Matrix Multiplication
1:05:25 For a square matrix A, define the Inverse of A, A-1, as the matrix satisfying
1:07:05 The inverse serves the role of division in matrix multiplication
1:07:52 The Multivariate Breeders' Equation
1:08:52 SI S2
1:10:11 We can rewrite CR = bAylAx Rx as follows First,
1:14:51 Sources of within-generation change in the mean
1:16:15 Within-generation change in the mean
1:18:10 Example in R
1:18:54 Enter G, P, and
1:22:13 versus B : Observed change versus targets of Selection, B = P-1 S,
1:22:55 Quantifying Multivariate Constraints to Response Is there genetic variation in the direction of selection? Consider the following and B:
1:24:18 G-20 10)
1:24:31 Quantifying Multivariate Constraints to Response Is there genetic variation in the direction of selections Consider the following and
1:26:06 The multivariate breeder's equation