Modeling Depression in Mice to Identify Genetic Mechanisms of Mood Disorder Cristina Santos 1, Brooke Miller 2, Matthew Pletcher 2, Andrew Su 4, Lisa Tarantino.

Slides:



Advertisements
Similar presentations
The genetic dissection of complex traits
Advertisements

Planning breeding programs for impact
Why this paper Causal genetic variants at loci contributing to complex phenotypes unknown Rat/mice model organisms in physiology and diseases Relevant.
Introduction Materials and methods SUBJECTS : Balb/cJ and C57BL/6J inbred mouse strains, and inbred fruit fly strains number 11 and 70 from the recombinant.
Potato Mapping / QTLs Amir Moarefi VCR
Frary et al. Advanced Backcross QTL analysis of a Lycopersicon esculentum x L. pennellii cross and identification of possible orthologs in the Solanaceae.
Pepper Mapping & Major Genes Mapping of chlorophyll retainer (cl) mutation in pepper The Pun1 gene for pungency QTL mapping for fruit size and shape.
Combined sequence based and genetic mapping analysis of complex traits in outbred rats Baud, A. et al. Rat Genome Sequencing and Mapping Consortium Presented.
A Systematic approach to the Large-Scale Analysis of Genotype- Phenotype correlations Paul Fisher Dr. Robert Stevens Prof. Andrew Brass.
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL FastANOVA: an Efficient Algorithm for Genome-Wide Association Study Xiang Zhang Fei Zou Wei Wang University.
A multi-phenotype protocol for fine scale mapping of QTL in outbred heterogeneous stock mice LC Solberg, C Arboledas, P Burns, S Davidson, G Nunez, A Taylor,
1 QTL mapping in mice Lecture 10, Statistics 246 February 24, 2004.
A Study on Variations of HDL Levels in Female vs. Male Mice The Battle of the Sexes: Presented by: Sean Roney Teresa Leslie Courtney Deshayes.
Genetic Traits Quantitative (height, weight) Dichotomous (affected/unaffected) Factorial (blood group) Mendelian - controlled by single gene (cystic fibrosis)
The role of parallel genetic changes in domestication: Fruit size in the plant family Solanaceae Matt Robinson.
The Future of Behavioural Genetics. Quantitative Genetics More fine-grained cognitive abilities, personality traits, disorders, childhood origins Integrating.
Positional Cloning LOD Sib pairs Chromosome Region Association Study Genetics Genomics Physical Mapping/ Sequencing Candidate Gene Selection/ Polymorphism.
CISC667, F05, Lec24, Liao1 CISC 667 Intro to Bioinformatics (Fall 2005) DNA Microarray, 2d gel, MSMS, yeast 2-hybrid.
Discussion Our current results suggest that it is possible to identify susceptibility regions using this methodology. The presented method takes advantage.
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL Graph Regularized Dual Lasso for Robust eQTL Mapping Wei Cheng 1 Xiang Zhang 2 Zhishan Guo 1 Yu Shi 3 Wei.
EXtreme Array Mapping and Haplotype analysis Using Arrays Justin Borevitz Salk Institute naturalvariation.org.
Polymorphisms – SNP, InDel, Transposon BMI/IBGP 730 Victor Jin, Ph.D. (Slides from Dr. Kun Huang) Department of Biomedical Informatics Ohio State University.
Genetics & Addiction Jonathan D. Pollock, Ph.D. Division of Neuroscience & Behavioral Research National Institute on Drug Abuse National Institutes of.
Haplotype Discovery and Modeling. Identification of genes Identify the Phenotype MapClone.
Manolis Kellis Broad Institute of MIT and Harvard
Design Considerations in Large- Scale Genetic Association Studies Michael Boehnke, Andrew Skol, Laura Scott, Cristen Willer, Gonçalo Abecasis, Anne Jackson,
Understanding Genetics of Schizophrenia
From QTL to QTG: Are we getting closer? Sagiv Shifman and Ariel Darvasi The Hebrew University of Jerusalem.
Natural Variation in Arabidopsis ecotypes. Using natural variation to understand diversity Correlation of phenotype with environment (selective pressure?)
Multifactorial Traits
The Center for Medical Genomics facilitates cutting-edge research with state-of-the-art genomic technologies for studying gene expression and genetics,
The medical relevance of genome variability Gabor T. Marth, D.Sc. Department of Biology, Boston College Medical Genomics Course – Debrecen,
Regulation of gene expression in the mammalian eye and its relevance to eye disease Todd Scheetz et al. Presented by John MC Ma.
HUMAN-MOUSE CONSERVED COEXPRESSION NETWORKS PREDICT CANDIDATE DISEASE GENES Ala U., Piro R., Grassi E., Damasco C., Silengo L., Brunner H., Provero P.
A Genome-wide association study of Copy number variation in schizophrenia Andrés Ingason CNS Division, deCODE Genetics. Research Institute of Biological.
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.
Complex Traits Most neurobehavioral traits are complex Multifactorial
Quantitative Genetics
QTL Mapping in Heterogeneous Stocks Talbot et al, Nature Genetics (1999) 21: Mott et at, PNAS (2000) 97:
Lauren Gerard Koch Functional Genomics Laboratory Medical College of Ohio Toledo, Ohio A Genome Scan for Aerobic Running Capacity QTLs in Rats.
Lab 13: Association Genetics December 5, Goals Use Mixed Models and General Linear Models to determine genetic associations. Understand the effect.
MEME homework: probability of finding GAGTCA at a given position in the yeast genome, based on a background model of A = 0.3, T = 0.3, G = 0.2, C = 0.2.
Pedagogical Objectives Bioinformatics/Neuroinformatics Unit Review of genetics Review/introduction of statistical analyses and concepts Introduce QTL.
Genetic correlations and associative networks for CNS transcript abundance and neurobehavioral phenotypes in a recombinant inbred mapping panel Elissa.
PLANT BIOTECHNOLOGY & GENETIC ENGINEERING (3 CREDIT HOURS) LECTURE 13 ANALYSIS OF THE TRANSCRIPTOME.
13 October 2004Statistics: Yandell © Inferring Genetic Architecture of Complex Biological Processes Brian S. Yandell 12, Christina Kendziorski 13,
Systems Genetics Approach to the Study of Brain Iron Regulation Byron C. Jones Professor of Biobehavioral Health & Pharmacology The Pennsylvania State.
Accelerating positional cloning in mice using ancestral haplotype patterns Mark Daly Whitehead Institute for Biomedical Research.
Bayesian Variable Selection in Semiparametric Regression Modeling with Applications to Genetic Mappping Fei Zou Department of Biostatistics University.
Identifying candidate genes for the regulation of the response to Trypanosoma congolense infection Introduction African cattle breeds differ significantly.
Noyes HA1 Agaba M2 Gibson J3 Ogugo M2 Iraqi F2 Brass A4 Anderson S5
University of Tennessee-Memphis
A multi-strain, high-resolution mouse haplotype map reveals three distinctive genetic signatures Laboratory of Population Genetics.
Invest. Ophthalmol. Vis. Sci ;52(6): doi: /iovs Figure Legend:
Gene Hunting: Design and statistics
Gene mapping in mice Karl W Broman Department of Biostatistics
Volume 77, Issue 3, Pages (February 2010)
Power to detect QTL Association
Genetic architecture of behaviour
Genome-wide Association Studies
Inferring Genetic Architecture of Complex Biological Processes Brian S
Linkage analysis and genetic mapping
Volume 11, Issue 5, Pages (May 2015)
A Flexible Bayesian Framework for Modeling Haplotype Association with Disease, Allowing for Dominance Effects of the Underlying Causative Variants  Andrew.
Genetic Investigations of Kidney Disease: Core Curriculum 2013
Evan G. Williams, Johan Auwerx  Cell 
Volume 125, Issue 2, Pages (August 2003)
Fig. 2 Genotype-induced differential gene expression is different in MDMi cells compared to monocytes. Genotype-induced differential gene expression is.
Cancer as a Complex Genetic Trait
Presentation transcript:

Modeling Depression in Mice to Identify Genetic Mechanisms of Mood Disorder Cristina Santos 1, Brooke Miller 2, Matthew Pletcher 2, Andrew Su 4, Lisa Tarantino 5, and Tim Wiltshire 1 1 University of North Carolina at Chapel Hill, Division of Pharmacotherapy and Experimental Therapeutics 2 Department of Molecular Pharmaceutics The Scripps Institute Jupiter, Florida 3 Genomics Institute of Novartis Research Foundation La Jolla, California 4 University of North Carolina at Chapel Hill, Department of Psychiatry Background Evidence from twin studies support the role of genetics in depression. Inbred mouse strains are good models to study the genetic basis of disease since mice within the same strain are genetically similar. By comparing inter- strain phenotypic differences and correlating each trait with known genotypes, we can identify genomic regions associated with depression known as quantitative trait loci (QTL). We hypothesize that regions associated with variable neurobiochemical levels and behavior contain candidate genes involved in depression. Methods Depressive Behavior: Mice were suspended in air by its tail and percent time spent immobile within 5 minutes was measured as an index of depressive behavior (n=10/strain). Neurobiochemical Levels: Analytes proposed to have a significant role in depression were simultaneously measured in brain and serum using a reverse-array ELISA. Chip was spotted at a range 0-2mg/mL of brain lysates. QTL Identification: Genotype information obtained for over 600,000 SNPs at a density of one SNP every 4.3 kB (Ding et al 2009) was used in an efficient mixed model association (Kang et al 2008) and haplotype association mapping algorithms (Pletcher et al 2004) to identify putative QTLs. Only QTLs that had 1/1000 likelihood to be false positive (-logP>3) were considered significant. i Results Immobility in the tail- suspension test was measured as depressive- like behavior in mice Genotype information for over 600,00 SNPs using the mouse diversity array chip Levels of 31 neurobiochemical analytes were measured in brain and serum of testing –naïve mice 1. Neurobiochemical Levels Table1 lists 31 neurobiochemical markers that were measured by ELISA. Figure1 shows differential levels of GAD67, APOD, and PAQR8 between strains which suggests role of genetics in modulating neurobiochemical levels. 2.Variable Depressive Behavior Figure2 shows greater inter-strain differences in depressive behavior, therefore response in this assay is likely to be modulated by genes. Immobility is an index of depressive behavior since it models “hopelessness” and is reduced by anti-depressants (data not shown). 3.Genes Associated with Neurobiochemical Levels and Depressive Behavior Fig3aFig3c Fig3b Likelihood of Association (-logP) Cdh2: candidate gene within QTL on Chr18 Igsf4a: candidate gene within QTL on Chr9 Fig3a-d: Genome-wide association plots display QTLs or genomic regions associated with depressive behavior (Fig3a and Fig3c) and neurobiochemical levels (Fig3b and Fig3d). Fig3a-b: Region on chr18 (Chr18: ) was significantly correlated with depressive behavior (-logP>5) as well as levels of glutathione reductase (-logP>5, Fig3b ), PAQR8, MCH, NR3C1, and APOD (data not shown). Genes within candidate QTL on chr 18 include Cdh2 and I20Rik. Fig3c-d: Region on chr9 (Chr9: ) was significantly correlated with depressive behavior (-logP>6) and levels of glutamate decarboxylase67 (-logP>5, Fig3d). Igsf4a (also known as Cadm1) and N01Rik are the two genes found within putative depressive QTL on chr9. Fig3d 4.Cdh2 and Igsf4a are Involved in Cell Adhesion Discussions Fig4aFig4b Fig4a: Cdh2 and Igsf4a are cell adhesion molecules in the neural system. Pathway was derived from KEGG database Fig4b: Interaction of Cadm1 (also known as Igsf4a) with other molecules like IL22 and progesterone can mediate behavior. Cadm1 pathway was derived from Ingenuity  Modeling depression in mice can identify mechanisms underlying disease and treatment  Genes associated with behavior and biochemical levels are likely to have a critical role in depression.