Presentation is loading. Please wait.

Presentation is loading. Please wait.

Modeling genetic and phenotypic data with the use of statistics Discovery of phenotypes influenced by the season of birth Can environment modify genetic.

Published byBrooke Bridges Modified over 9 years ago

Similar presentations

Presentation on theme: "Modeling genetic and phenotypic data with the use of statistics Discovery of phenotypes influenced by the season of birth Can environment modify genetic."— Presentation transcript:

1 Modeling genetic and phenotypic data with the use of statistics Discovery of phenotypes influenced by the season of birth Can environment modify genetic effects on human anthropometric traits? Genetics of liver abnormalities in obese subjects Genetics of liver markers and their interaction with obesity “Solving biological problems that require Maths” Projects supervised by: Zoltán Kutalik Diana Marek Murielle Bochud Pedro Marques-Vidal

2 But des projets –Sensibiliser à une recherche clinique concrète, impliquant des notions et des données de génétique ainsi que des phénotypes, mesurés dans une population Données de génotypage (SNPs) Phénotypes Interactions avec des facteurs environnementaux Détection d'association entre un SNP et les variations d'un phénotype –Mise en pratique de théories mathématiques /statistiques permettant de modéliser la question biologique Regression linéaire et logistique Utilisation de Matlab ®

3 6’189 individuals Phenotypes 159 measurement 144 questions Genotypes 500.000 SNPs Données: CoLaus (Cohort Lausanne) Collaboration with: Vincent Mooser (GSK), Peter Vollenweider & Gerard Waeber (CHUV)

4 ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG… ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG… ATTGCAATCCGTGG...ATCGAGCCA…TACGATTGCACGCCG…ATTGCAAGCCGTGG...ATCTAGCCA…TACGATTGCAAGCCG… Variants génétiques: SNPs (Single Nucleotide Polymorphisms)

5 What is association? chromosomeSNPstrait variant Genetic variation yields phenotypic variation Population with ‘ ’ allele Distributions of “trait”

6 Association using regression genotypeCoded genotype phenotype

7 Regression formalism (monotonic) transformation phenotype (response variable) of individual i effect size (regression coefficient) coded genotype (feature) of individual i p(β=0) error (residual) Goal: Find effect size that explains best all (potentially transformed) phenotypes as a linear function of the genotypes and estimate the probability (p-value) for the data being consistent with the null hypothesis (i.e. no effect)

8 Whole Genome Association

9 Current microarrays probe ~1M SNPs! Standard approach: Evaluate significance for association of each SNP independently: significance

10 Whole Genome Association significance Manhattan plot observed significance Expected significance Quantile-quantile plot Chromosome & position GWA screens include large number of statistical tests! Huge burden of correcting for multiple testing! Can detect only highly significant associations ( p < α / #(tests) ~ 10 -7 )

11 Discovery of phenotypes influenced by the season of birth Background: It has been evidenced for model organisms, e.g. mouse, that the perinatal photoperiod can have long term influence on behaviour and regulation of the Circadian clock genes. Goal: The goal of this project is to use the Cohorte Lausannois (CoLaus) data to discover phenotypes with statistical evidence of being influenced by the season of birth of the individual. Special emphasis will be on psychological traits. Mathematical tools: Statistics. The students will learn how to use Matlab to read in large data sets, conduct linear and logistic regression analysis. Biological or Medical aspects: The effect of imprinting on complex human traits is poorly understood, we aim to elucidate a special aspect of it. Supervisors: Zoltan Kutalik & Diana MarekZoltan KutalikDiana Marek References: Ciarleglio CM, Axley JC, Strauss BR, Gamble KL, and McMahon DG. Perinatal photoperiod imprints the circadian clock. Nat Neurosci 2011 Jan; 14(1) 25-7. doi:10.1038/nn.2699 pmid:21131951.

12

13

14

15 Can environment modify genetic effects on human anthropometric traits? Background: Large studies (including hundreds of thousands of individuals) identified genetic factors influencing human height, body-mass- index (BMI) and waist-to-hip ratio (WHR). It is currently unknown whether the effect of the discovered genetic variants are modified by environmental factors. Goal: The goal of this project is to use the Cohorte Lausannois (CoLaus) data to find environmental factors (e.g. smoking, alcohol consumption, physical activity) that modify genetic effects influencing human height, BMI, WHR. Mathematical tools: Statistics. The students will learn how to use Matlab to read in large data sets including genetic data; conduct linear and logistic regression and interaction analysis. Biological or Medical aspects: Supervisors: Zoltan Kutalik & Diana MarekZoltan KutalikDiana Marek References: Lango Allen et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010 Oct 14;467(7317):832-8.

16 Genetics of liver abnormalities in obese subjects Goal The goal is to perform a genome-wide association study that will potentially identify SNPs that are related to liver abnormalities in obese subjects (NAFL or NASH) by a) performing a GWAS on liver abnormalities; b) search for interactions with body mass index or obesity of the potential candidate SNPs identified; c) perform a GWAS comparing obese subjects with and without hepatic abnormalities. The tool of choice for this project is logistic regression analysis and linear regression analysis. The student will learn the basics of regressing a given phenotype to a genotype and how this analysis is implemented on a computer to handle a large number of SNPs. Supervisors Pedro Marques-Vidal & Zoltan KutalikPedro Marques-VidalZoltan Kutalik Kotronen et al. (2009) A common variant in PNPLA3, which encodes adiponutrin, is associated with liver fat content in humans. Diabetologia 52:1056–1060

17 Genetics of liver markers and their interaction with obesity Goal The goal is to perform a genome-wide association study that will potentially identify SNPs that are related to liver markers by a) performing a GWAS on liver markers; b) search for interactions with body mass index or alcohol consumption of the potential candidate SNPs identified; The tool of choice for this project is logistic regression analysis and linear regression analysis. The student will learn the basics of regressing a given phenotype to a genotype and how this analysis is implemented on a computer to handle a large number of SNPs. Supervisors Pedro Marques-Vidal & Zoltan KutalikPedro Marques-VidalZoltan Kutalik

Download ppt "Modeling genetic and phenotypic data with the use of statistics Discovery of phenotypes influenced by the season of birth Can environment modify genetic."

Similar presentations

Statistical methods for genetic association studies

Statistical methods for genetic association studies
Lecture 2 Strachan and Read Chapter 13

Lecture 2 Strachan and Read Chapter 13
What is an association study? Define linkage disequilibrium

What is an association study? Define linkage disequilibrium
Single Nucleotide Polymorphism And Association Studies Stat 115 Dec 12, 2006.

Single Nucleotide Polymorphism And Association Studies Stat 115 Dec 12, 2006.
Meta-analysis for GWAS BST775 Fall 2013. DEMO Replication Criteria for a successful GWAS P

Meta-analysis for GWAS BST775 Fall DEMO Replication Criteria for a successful GWAS P
The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL FastANOVA: an Efficient Algorithm for Genome-Wide Association Study Xiang Zhang Fei Zou Wei Wang University.

The UNIVERSITY of NORTH CAROLINA at CHAPEL HILL FastANOVA: an Efficient Algorithm for Genome-Wide Association Study Xiang Zhang Fei Zou Wei Wang University.
Ingredients for a successful genome-wide association studies: A statistical view Scott Weiss and Christoph Lange Channing Laboratory Pulmonary and Critical.

Ingredients for a successful genome-wide association studies: A statistical view Scott Weiss and Christoph Lange Channing Laboratory Pulmonary and Critical.
Lab 13: Association Genetics. Goals Use a Mixed Model to determine genetic associations. Understand the effect of population structure and kinship on.

Lab 13: Association Genetics. Goals Use a Mixed Model to determine genetic associations. Understand the effect of population structure and kinship on.
Gene-gene and gene-environment interactions Manuel Ferreira Massachusetts General Hospital Harvard Medical School Center for Human Genetic Research.

Gene-gene and gene-environment interactions Manuel Ferreira Massachusetts General Hospital Harvard Medical School Center for Human Genetic Research.
1 FSTL4 and SEMA5A are associated with alcohol dependence: meta- analysis of two genome-wide association studies Kesheng Wang, PhD Department of Biostatistics.

1 FSTL4 and SEMA5A are associated with alcohol dependence: meta- analysis of two genome-wide association studies Kesheng Wang, PhD Department of Biostatistics.
Variant discovery Different approaches: With or without a reference? With a reference – Limiting factors are CPU time and memory required – Crossbow –

Variant discovery Different approaches: With or without a reference? With a reference – Limiting factors are CPU time and memory required – Crossbow –
More Powerful Genome-wide Association Methods for Case-control Data Robert C. Elston, PhD Case Western Reserve University Cleveland Ohio.

More Powerful Genome-wide Association Methods for Case-control Data Robert C. Elston, PhD Case Western Reserve University Cleveland Ohio.
Biomedical Master Introduction to genome-wide association studies Metabolic diseases (B. Thorens) Biomedical Master: Metabolic diseases Lausanne, November.

Biomedical Master Introduction to genome-wide association studies Metabolic diseases (B. Thorens) Biomedical Master: Metabolic diseases Lausanne, November.
Biomedical Master Introduction to genome-wide association studies Metabolic diseases (B. Thorens) Biomedical Master: Metabolic diseases Lausanne, October.

Biomedical Master Introduction to genome-wide association studies Metabolic diseases (B. Thorens) Biomedical Master: Metabolic diseases Lausanne, October.
MSc GBE Course: Genes: from sequence to function Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue.

MSc GBE Course: Genes: from sequence to function Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue.
Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis Dinu et al, J. Biomedical.

Integrating domain knowledge with statistical and data mining methods for high-density genomic SNP disease association analysis Dinu et al, J. Biomedical.
Single nucleotide polymorphisms Usman Roshan. SNPs DNA sequence variations that occur when a single nucleotide is altered. Must be present in at least.

Single nucleotide polymorphisms Usman Roshan. SNPs DNA sequence variations that occur when a single nucleotide is altered. Must be present in at least.
Using biological networks to search for interacting loci in genome-wide association studies Mathieu Emily et. al. European journal of human genetics, e-pub.

Using biological networks to search for interacting loci in genome-wide association studies Mathieu Emily et. al. European journal of human genetics, e-pub.
BSc Course: Experimental design“ Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue de Bugnon 27.

BSc Course: "Experimental design“ Genome-wide Association Studies Sven Bergmann Department of Medical Genetics University of Lausanne Rue de Bugnon 27.
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.

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.

Similar presentations

Ads by Google