Lab 13: Association Genetics December 5, 2011. Goals Use Mixed Models and General Linear Models to determine genetic associations. Understand the effect.

Slides:



Advertisements
Similar presentations
Statistical methods for genetic association studies
Advertisements

Lecture 2 Strachan and Read Chapter 13
CZ5225 Methods in Computational Biology Lecture 9: Pharmacogenetics and individual variation of drug response CZ5225 Methods in Computational Biology.
Association Tests for Rare Variants Using Sequence Data
ASSOCIATION MAPPING WITH TASSEL Presenter: VG SHOBHANA PhD Student CPMB.
GBS & GWAS using the iPlant Discovery Environment
Perspectives from Human Studies and Low Density Chip Jeffrey R. O’Connell University of Maryland School of Medicine October 28, 2008.
Whole genome association mapping of beta-glucan content ir barley Ieva Mežaka, Nils Rostoks Advances in Plant Biotechnology in Baltic Sea region1.
Genome-wide association mapping Introduction to theory and methodology
Objectives Cover some of the essential concepts for GWAS that have not yet been covered Hardy-Weinberg equilibrium Meta-analysis SNP Imputation Review.
Linkage Analysis: An Introduction Pak Sham Twin Workshop 2001.
PAG 2011 TASSEL Terry Casstevens 1, Peter Bradbury 2,3, Zhiwu Zhang 1, Yang Zhang 1, Edward Buckler 1,2,4 1 Institute.
Association Mapping David Evans. Outline Definitions / Terminology What is (genetic) association? How do we test for association? When to use association.
Ingredients for a successful genome-wide association studies: A statistical view Scott Weiss and Christoph Lange Channing Laboratory Pulmonary and Critical.
Association Modeling With iPlant
Lab 13: Association Genetics. Goals Use a Mixed Model to determine genetic associations. Understand the effect of population structure and kinship on.
CS177 Lecture 9 SNPs and Human Genetic Variation Tom Madej
Estimating “Heritability” using Genetic Data David Evans University of Queensland.
MSc GBE Course: Genes: from sequence to function Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue.
Computational Complexity The complexity of the MG model for a single SNP is determined by the complexity of the matrix operations in formulas used to iteratively.
Monte Carlo methods for estimating population genetic parameters Rasmus Nielsen University of Copenhagen.
Chapter 17: Evolution of Populations
Give me your DNA and I tell you where you come from - and maybe more! Lausanne, Genopode 21 April 2010 Sven Bergmann University of Lausanne & Swiss Institute.
Modes of selection on quantitative traits. Directional selection The population responds to selection when the mean value changes in one direction Here,
SNPs Daniel Fernandez Alejandro Quiroz Zárate. A SNP is defined as a single base change in a DNA sequence that occurs in a significant proportion (more.
The Complexities of Data Analysis in Human Genetics Marylyn DeRiggi Ritchie, Ph.D. Center for Human Genetics Research Vanderbilt University Nashville,
A single-nucleotide polymorphism tagging set for human drug metabolism and transport Kourosh R Ahmadi, Mike E Weale, Zhengyu Y Xue, Nicole Soranzo, David.
The medical relevance of genome variability Gabor T. Marth, D.Sc. Department of Biology, Boston College Medical Genomics Course – Debrecen,
Gene Hunting: Linkage and Association
Microarray data analysis David A. McClellan, Ph.D. Introduction to Bioinformatics Brigham Young University Dept. Integrative Biology.
Input: A set of people with/without a disease (e.g., cancer) Measure a large set of genetic markers for each person (e.g., measurement of DNA at various.
Genome-Wide Association Study (GWAS)
National Taiwan University Department of Computer Science and Information Engineering Pattern Identification in a Haplotype Block * Kun-Mao Chao Department.
Experimental Design and Data Structure Supplement to Lecture 8 Fall
Quantitative Genetics. Continuous phenotypic variation within populations- not discrete characters Phenotypic variation due to both genetic and environmental.
Type 1 Error and Power Calculation for Association Analysis Pak Sham & Shaun Purcell Advanced Workshop Boulder, CO, 2005.
Quantitative Genetics
Overview of developments. Nested Association Mapping (NAM) Jianming Yu, James B. Holland, Michael D. McMullen and Edward S. Buckler, Genetics, Vol. 178,
Genes in human populations n Population genetics: focus on allele frequencies (the “gene pool” = all the gametes in a big pot!) n Hardy-Weinberg calculations.
Gene Mapping ROBERT SANTOS ENGLISH 100 ESP NOVEMBER
Statistical Issues in Genetic Association Studies
Errors in Genetic Data Gonçalo Abecasis. Errors in Genetic Data Pedigree Errors Genotyping Errors Phenotyping Errors.
Separation of the largest eigenvalues in eigenanalysis of genotype data from discrete populations Katarzyna Bryc Postdoctoral Fellow, Reich Lab, Harvard.
C2BAT: Using the same data set for screening and testing. A testing strategy for genome-wide association studies in case/control design Matt McQueen, Jessica.
Lecture 16 Tuesday, April 9, 2013 BiSc 001 Spring 2013 Guest Lecture Dr. Jihye Park.
Biostatistics-Lecture 19 Linkage Disequilibrium and SNP detection
Linkage Disequilibrium and Recent Studies of Haplotypes and SNPs
Mx modeling of methylation data: twin correlations [means, SD, correlation] ACE / ADE latent factor model regression [sex and age] genetic association.
Lectures 7 – Oct 19, 2011 CSE 527 Computational Biology, Fall 2011 Instructor: Su-In Lee TA: Christopher Miles Monday & Wednesday 12:00-1:20 Johnson Hall.
Evololution Part 1 Genes and Variation Part 1: Genes and Variation.
Linkage. Announcements Problem set 1 is available for download. Due April 14. class videos are available from a link on the schedule web page, and at.
Linkage. Announcements Problem set 1 is available for download. Due April 14. class videos are available from a link on the schedule web page, and at.
GENETIC EVOLUTION. Gene Pool All genetic information from a population of a specific species.
Principal Components Analysis ( PCA)
Principal components analysis
Genes and Variation Genotypes and phenotypes in evolution Natural selection acts on phenotypes and does not directly on genes. Natural selection.
Genome-Wides Association Studies (GWAS) Veryan Codd.
Common variation, GWAS & PLINK
upstream vs. ORF binding and gene expression?
Principal components analysis
Genome Wide Association Studies using SNP
Introduction to bioinformatics lecture 11 SNP by Ms.Shumaila Azam
Mapping Quantitative Trait Loci
Genome-wide Association Studies
Genome-wide Association
Exercise: Effect of the IL6R gene on IL-6R concentration
Linkage analysis and genetic mapping
Heat map of additive effects for PCs QTL
Association Design Begins with KNOWN polymorphism theoretically expected to be associated with the trait (e.g., DRD2 and schizophrenia). Genotypes.
Presentation transcript:

Lab 13: Association Genetics December 5, 2011

Goals Use Mixed Models and General Linear Models to determine genetic associations. Understand the effect of population structure and kinship on associations. Use Trait Analysis by aSSociation, Evolution and Linkage (TASSEL) to calculate phenotype- genotype associations.

Mixed Model phenotype (response variable) of individual i effect of target SNP Family effect (Kinship coefficient) Population Effect (e.g., Admixture coefficient from Structure or values of Principal Components) effects of background SNPs

Principal Component Analysis (PCA) PCA is computationally much more efficient than maximum likelihood method. PCA reduces dimensionality of the data so that the correlated variables are transformed into uncorrelated variables called principal components. PC1 captures as much of the variation as possible and proceeds with PC2, PC3…. Requires elimination of monomorphic markers and imputation of missing values.

Imputing Missing Genotypes Typically accomplished with software such as IMPUTE, PLINK, MACH, BEAGLE, and fastPHASE From Isik and Wetten 2011 Workshop on Genomic Selection

PCA and Population Structure

Population Structure Unequal distribution of alleles unrelated to disease between cases and controls. Any allele more common in diseased population may spuriously appear to be associated with disease. Cases Controls Genotype Pop 1 Pop 2 Pop 2 TT AT AA

Problem 1 Use the Tassel Tutorial Data to explore how to perform association genetic analyses for some commercially- important Maize phenotypes: flowering time, ear height, and ear width. a)Which traits are significantly associated with polymorphisms in the Dwarf8 gene? Propose a reasonable biological hypothesis for these associations? See Thornsberry et al. (2001) and information from public genome databases for necessary background information. b)Are there any patterns to the locations of the significant SNPs within the gene (e.g., are the significant SNPs clustered or dispersed, where in the gene do they occur)? What are some possible reasons for these patterns? c)How do the corrections for population structure and kinship change the associations? Why?