Statistical Genetics. Matthew Stephens. Statistics Retreat, October 26th 2012
|
|
- Christopher Pitts
- 5 years ago
- Views:
Transcription
1 Statistical Genetics Statistics Retreat, October 26th 2012
2 Two stories The two most influential statistical ideas in analysis of genetic association studies. 1 Sequence, sequence, everywhere. 1 With apologies to Steve Stigler
3 Story I: Genetic Association Studies Genetic association studies aim to identify genetic variants that modify risk of common diseases or affect other phenotypes (e.g. Type I Diabetes, height, LDL cholestrol). The idea is absurdly simple: measure genetic variants (usually SNPs), and phenotypes in randomly-sampled individuals, and see which SNPs are correlated with phenotypes.
4 Story I: Genetic Association Studies Typical recent genome-wide studies have typed 500K-1M SNPs in thousands of (unrelated) phenotyped individuals. Basic Analysis: test each SNP, one-by-one, for statistical association with each phenotype.
5 Progress identifying variants underlying common disease Published Genome Wide Associations through 09/2011 1,617 published GWA at p 5X10 8 for 249 traits NHGRI GWA Catalog Credit:
6 The two most influential statistical ideas in GWAS Correction for unmeasured confounding (population structure). Imputation to combine studies.
7 Population Structure and Unmeasured Confounding The Problem in a nutshell: What would happen if you conducted a Genetic Association study for Chopstick Use in San Francisco?
8 Population Structure and Unmeasured Confounding If you know the genetic background of the individuals in your study (e.g. which continent they inherited their genes from), then you can correct for it. What if you don t know it?
9 Principal Components Analysis to the rescue! Novembre et al, Nature, 2008
10 Principal Components Analysis to the rescue! Test for significance of genetic effect β, controlling for effects of genetic background (α): y = vα + xβ + ɛ Price et al, Nature Genetics, 2006
11 The two most influential statistical ideas in GWAS Correction for unmeasured confounding (population structure). Imputation to combine studies. Credit: Bryan Howie
12 Genotype(imputa-on(background( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% 1% 2% 0% 0% 1% 1% 1% 1% 0%?% 0% 0% 0% 1% 1% 1% 0% 1% 1% 2% 0% 0% 1% 1%?% 2% 0% 0% 0% 0% 1% 1% 1% 1% 0%?% 0% 2% 0% 0% 1% 1% 1% 1% 1% 1% 1% 2% Reference( haplotypes( Phenotyped( GWAS( samples( SNPs%genotyped%on%an%array%
13 Genotype(imputa-on(background( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% 1%?%?%?% 2%?% 0%?%?%?%?% 0% 1%?% 1% 1%?%?%?% 1%?% 0%?%?%?%?%?% 0%?% 0% 0%?%?%?% 1%?% 1%?%?%?%?% 1% 0%?% 1% 1%?%?%?% 2%?% 0%?%?%?%?% 0% 1%?% 1%?%?%?%?% 2%?% 0%?%?%?%?% 0% 0%?% 0% 1%?%?%?% 1%?% 1%?%?%?%?% 1% 0%?%?% 0%?%?%?% 2%?% 0%?%?%?%?% 0% 1%?% 1% 1%?%?%?% 1%?% 1%?%?%?%?% 1% 1%?% 2% Reference( haplotypes( Phenotyped( GWAS( samples( Untyped%SNPs%
14 Genotype(imputa-on(background( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% 1% 1% 2% 2% 2% 0% 0% 1% 2% 0% 0% 0% 1% 1% 1% 1% 1% 1% 1% 1% 0% 0% 1% 2% 1% 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 2% 1% 0% 1% 1% 0% 0% 1% 1% 2% 2% 2% 2% 0% 0% 1% 2% 0% 0% 0% 1% 1% 1% 2% 1% 2% 2% 2% 0% 0% 0% 2% 2% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 0% 0% 1% 0% 0% 2% 2% 2% 0% 0% 2% 2% 2% 2% 0% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 1% 2% Reference( haplotypes( Phenotyped( GWAS( samples( Associa8on% signal%
15 Imputa-on(facilitates(meta>analysis( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% 1% 2% 0% 0% 1% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 2% 0% 0% 1% 1% 0% 1% 1% 0% 1% 1% 0% 0% 1% 0% 2% 0% 2% 2% 0% 0% 1% 0% 1% 1% 0% 1% 1% 1% Reference( haplotypes( GWAS(1( GWAS(2(
16 Imputa-on(facilitates(meta>analysis( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% Reference( haplotypes( Associa8on% signal% 1% 1% 2% 2% 2% 0% 0% 1% 1% 2% 0% 0% 1% 1% 1% 1% 1% 1% 1% 1% 0% 0% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 2% 1% 1% 1% 1% 0% 0% 1% 1% 2% 2% 2% 2% 0% 0% 1% 0% 1% 0% 0% 1% 1% 1% 0% 0% 0% 1% 1% 1% 1% 2% 0% 1% 1% 1% 1% 1% 2% 0% 0% 0% 0% 0% 1% 1% 2% 0% 2% 1% 1% 0% 0% 1% 1% 1% 2% 2% 1% 0% 0% 1% 0% 1% 1% 1% 0% 0% 1% 0% 0% 1% 1% 1% 0% 0% 2% 1% 1% 0% 0% 1% 1% 1% GWAS(1( GWAS(2(
17 Imputa-on(facilitates(meta>analysis( 0% 0% 1% 1% 1% 0% 0% 1% 1% 0% 0% 0% 1% 1% 1% 0% 0% 0% 0% 0% 1% 1% 1% 0% 1% 1% 1% 0% 0% 1% 1% 1% 1% 1% 1% 0% 0% 0% 1% 0% 0% 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 1% 1% 1% 1% 1% 0% 0% 1% 1% 1% 2% 2% 2% 0% 0% 1% 1% 2% 0% 0% 1% 1% 1% 1% 1% 1% 1% 1% 0% 0% 1% 0% 1% 0% 0% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 2% 1% 1% 1% 1% 0% 0% 1% 1% 2% 2% 2% 2% 0% 0% 1% 0% 1% 0% 0% 1% 1% 1% 0% 0% 0% 1% 1% 1% 1% 2% 0% 1% 1% 1% 1% 1% 2% 0% 0% 0% 0% 0% 1% 1% 2% 0% 2% 1% 1% 0% 0% 1% 1% 1% 2% 2% 1% 0% 0% 1% 0% 1% 1% 1% 0% 0% 1% 0% 0% 1% 1% 1% 0% 0% 2% 1% 1% 0% 0% 1% 1% 1% Reference( haplotypes( GWAS(1( GWAS(2( Type%1%diabetes:%Cooper%et%al.,%Nov%2008%(Nature'Gene*cs)% Type%2%diabetes:%Zeggini%et%al.,%May%2008%(Nature'Gene*cs)% Crohn s%disease:%barreh%et%al.,%aug%2008%(nature'gene*cs)%
18 Story II: Sequence, Sequence, Everywhere
19 Sequencing Assays, and Statistical Challenges Although DNA sequencing is best known for obtaining genome sequences, it is now routinely used for measuring cellular processes to try to understand how cells operate. For example: Gene expression (RNA-seq). Chromatin openness (DNase-seq). Transcription Factor Binding (ChIP-seq) Histone modifications (ChIP-seq) A key question is how/why cells differ from one another (they share the same DNA!).
20 Chromatin and DNA structure Figure from Felsenfeld and Groudine. Nature, 2003
21 The Data The basic structure of these assays is the same: Do something clever to get bits of the DNA that you want (e.g. the bits that contact a modified histone, or the bits that are bound by a particular transcription factor). Sequence these bits (producing millions of little sequences). Work out where in the genome each sequence came from. The number of sequences coming from each location (usually 0 or 1) is a measure of the intensity of the process at that location. Basic model: an inhomogeneous Poisson process, x ib Poi(λ ib ).
22 Example: Histone Modification H3K4me1 Can you spot the difference? Left Ventricle, H3K4me Right Ventricle, H3K4me Data from Scott Smemo, Nobrega lab
23 Advertisement: STAT We have preliminary ideas and methods for dealing with these data, based on wavelets for count data (work with H. Shim). In STAT we will try crowd-sourcing these ideas, to see how much further progress we can make. Aim: to combine expertises in Bioinformatics, Computing, Biology and Statistics, to make more progress together than any of us could do alone!
24 Acknowledgements Bryan Howie, Heejung Shim. Funding: NHGRI, NIH GTEX project, and NIH ENDGAME consortium.
GENOME-WIDE ASSOCIATION STUDIES
GENOME-WIDE ASSOCIATION STUDIES SUCCESSES AND PITFALLS IBT 2012 Human Genetics & Molecular Medicine Zané Lombard IDENTIFYING DISEASE GENES??? Nature, 15 Feb 2001 Science, 16 Feb 2001 IDENTIFYING DISEASE
More informationChromHMM Tutorial. Jason Ernst Assistant Professor University of California, Los Angeles
ChromHMM Tutorial Jason Ernst Assistant Professor University of California, Los Angeles Talk Outline Chromatin states analysis and ChromHMM Accessing chromatin state annotations for ENCODE2 and Roadmap
More informationAccessing and Using ENCODE Data Dr. Peggy J. Farnham
1 William M Keck Professor of Biochemistry Keck School of Medicine University of Southern California How many human genes are encoded in our 3x10 9 bp? C. elegans (worm) 959 cells and 1x10 8 bp 20,000
More informationNot IN Our Genes - A Different Kind of Inheritance.! Christopher Phiel, Ph.D. University of Colorado Denver Mini-STEM School February 4, 2014
Not IN Our Genes - A Different Kind of Inheritance! Christopher Phiel, Ph.D. University of Colorado Denver Mini-STEM School February 4, 2014 Epigenetics in Mainstream Media Epigenetics *Current definition:
More informationGenome-wide Association Studies (GWAS) Pasieka, Science Photo Library
Lecture 5 Genome-wide Association Studies (GWAS) Pasieka, Science Photo Library Chi-squared test to evaluate whether the odds ratio is different from 1. Corrected for multiple testing Source: wikipedia.org
More informationComputational aspects of ChIP-seq. John Marioni Research Group Leader European Bioinformatics Institute European Molecular Biology Laboratory
Computational aspects of ChIP-seq John Marioni Research Group Leader European Bioinformatics Institute European Molecular Biology Laboratory ChIP-seq Using highthroughput sequencing to investigate DNA
More informationBST227 Introduction to Statistical Genetics. Lecture 4: Introduction to linkage and association analysis
BST227 Introduction to Statistical Genetics Lecture 4: Introduction to linkage and association analysis 1 Housekeeping Homework #1 due today Homework #2 posted (due Monday) Lab at 5:30PM today (FXB G13)
More informationGenome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data
Genome-Wide Localization of Protein-DNA Binding and Histone Modification by a Bayesian Change-Point Method with ChIP-seq Data Haipeng Xing, Yifan Mo, Will Liao, Michael Q. Zhang Clayton Davis and Geoffrey
More informationBioinformatics and Computational Pharmacology
University of Colorado, Boulder CU Scholar Science Boot Camp for Librarians West University Libraries Spring 5-1-2013 Bioinformatics and Computational Pharmacology Lawrence Hunter Ph.D. University of Colorado
More informationProcessing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data
Processing, integrating and analysing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data Bioinformatics methods, models and applications to disease Alex Essebier ChIP-seq experiment To
More informationIntroduction to the Genetics of Complex Disease
Introduction to the Genetics of Complex Disease Jeremiah M. Scharf, MD, PhD Departments of Neurology, Psychiatry and Center for Human Genetic Research Massachusetts General Hospital Breakthroughs in Genome
More informationDuring the hyperinsulinemic-euglycemic clamp [1], a priming dose of human insulin (Novolin,
ESM Methods Hyperinsulinemic-euglycemic clamp procedure During the hyperinsulinemic-euglycemic clamp [1], a priming dose of human insulin (Novolin, Clayton, NC) was followed by a constant rate (60 mu m
More informationBST227: Introduction to Statistical Genetics
BST227: Introduction to Statistical Genetics Lecture 11: Heritability from summary statistics & epigenetic enrichments Guest Lecturer: Caleb Lareau Success of GWAS EBI Human GWAS Catalog As of this morning
More informationChromatin marks identify critical cell-types for fine-mapping complex trait variants
Chromatin marks identify critical cell-types for fine-mapping complex trait variants Gosia Trynka 1-4 *, Cynthia Sandor 1-4 *, Buhm Han 1-4, Han Xu 5, Barbara E Stranger 1,4#, X Shirley Liu 5, and Soumya
More informationAn epigenetic approach to understanding (and predicting?) environmental effects on gene expression
www.collaslab.com An epigenetic approach to understanding (and predicting?) environmental effects on gene expression Philippe Collas University of Oslo Institute of Basic Medical Sciences Stem Cell Epigenetics
More informationPatterns of Histone Methylation and Chromatin Organization in Grapevine Leaf. Rachel Schwope EPIGEN May 24-27, 2016
Patterns of Histone Methylation and Chromatin Organization in Grapevine Leaf Rachel Schwope EPIGEN May 24-27, 2016 What does H3K4 methylation do? Plant of interest: Vitis vinifera Culturally important
More informationSession 6: Integration of epigenetic data. Peter J Park Department of Biomedical Informatics Harvard Medical School July 18-19, 2016
Session 6: Integration of epigenetic data Peter J Park Department of Biomedical Informatics Harvard Medical School July 18-19, 2016 Utilizing complimentary datasets Frequent mutations in chromatin regulators
More informationMissing Heritablility How to Analyze Your Own Genome Fall 2013
Missing Heritablility 02-223 How to Analyze Your Own Genome Fall 2013 Heritability Heritability: the propor>on of observed varia>on in a par>cular trait (as height) that can be agributed to inherited gene>c
More informationRNA-seq Introduction
RNA-seq Introduction DNA is the same in all cells but which RNAs that is present is different in all cells There is a wide variety of different functional RNAs Which RNAs (and sometimes then translated
More informationSTAT1 regulates microrna transcription in interferon γ stimulated HeLa cells
CAMDA 2009 October 5, 2009 STAT1 regulates microrna transcription in interferon γ stimulated HeLa cells Guohua Wang 1, Yadong Wang 1, Denan Zhang 1, Mingxiang Teng 1,2, Lang Li 2, and Yunlong Liu 2 Harbin
More informationYue Wei 1, Rui Chen 2, Carlos E. Bueso-Ramos 3, Hui Yang 1, and Guillermo Garcia-Manero 1
Genome-wide CHIP-Seq Analysis of Histone Methylation Reveals Modulators of NF- B Signaling And the Histone Demethylase JMJD3 Implicated in Myelodysplastic Syndrome Yue Wei 1, Rui Chen 2, Carlos E. Bueso-Ramos
More informationHeritability enrichment of differentially expressed genes. Hilary Finucane PGC Statistical Analysis Call January 26, 2016
Heritability enrichment of differentially expressed genes Hilary Finucane PGC Statistical Analysis Call January 26, 2016 1 Functional genomics + GWAS gives insight into disease relevant tissues Trynka
More informationSupplemental Figure S1. Tertiles of FKBP5 promoter methylation and internal regulatory region
Supplemental Figure S1. Tertiles of FKBP5 promoter methylation and internal regulatory region methylation in relation to PSS and fetal coupling. A, PSS values for participants whose placentas showed low,
More informationResearch Article Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq Data
BioMed Volume 2016, Article ID 2395341, 6 pages http://dx.doi.org/10.1155/2016/2395341 Research Article Identifying Liver Cancer-Related Enhancer SNPs by Integrating GWAS and Histone Modification ChIP-seq
More informationAn expanded view of complex traits: from polygenic to omnigenic
BIRS 2017 An expanded view of complex traits: from polygenic to omnigenic How does human genetic variation drive variation in complex traits/disease risk? Yang I Li Stanford University Evan Boyle Jonathan
More informationComputational Analysis of UHT Sequences Histone modifications, CAGE, RNA-Seq
Computational Analysis of UHT Sequences Histone modifications, CAGE, RNA-Seq Philipp Bucher Wednesday January 21, 2009 SIB graduate school course EPFL, Lausanne ChIP-seq against histone variants: Biological
More informationNature Structural & Molecular Biology: doi: /nsmb.2419
Supplementary Figure 1 Mapped sequence reads and nucleosome occupancies. (a) Distribution of sequencing reads on the mouse reference genome for chromosome 14 as an example. The number of reads in a 1 Mb
More informationSudin Bhattacharya Institute for Integrative Toxicology
Beyond the AHRE: the Role of Epigenomics in Gene Regulation by the AHR (or, Varied Applications of Computational Modeling in Toxicology and Ingredient Safety) Sudin Bhattacharya Institute for Integrative
More informationAssociation mapping (qualitative) Association scan, quantitative. Office hours Wednesday 3-4pm 304A Stanley Hall. Association scan, qualitative
Association mapping (qualitative) Office hours Wednesday 3-4pm 304A Stanley Hall Fig. 11.26 Association scan, qualitative Association scan, quantitative osteoarthritis controls χ 2 test C s G s 141 47
More informationHuman population sub-structure and genetic association studies
Human population sub-structure and genetic association studies Stephanie A. Santorico, Ph.D. Department of Mathematical & Statistical Sciences Stephanie.Santorico@ucdenver.edu Global Similarity Map from
More informationCS2220 Introduction to Computational Biology
CS2220 Introduction to Computational Biology WEEK 8: GENOME-WIDE ASSOCIATION STUDIES (GWAS) 1 Dr. Mengling FENG Institute for Infocomm Research Massachusetts Institute of Technology mfeng@mit.edu PLANS
More informationChallenges of CGH array testing in children with developmental delay. Dr Sally Davies 17 th September 2014
Challenges of CGH array testing in children with developmental delay Dr Sally Davies 17 th September 2014 CGH array What is CGH array? Understanding the test Benefits Results to expect Consent issues Ethical
More informationAllelic reprogramming of the histone modification H3K4me3 in early mammalian development
Allelic reprogramming of the histone modification H3K4me3 in early mammalian development 张戈 Method and material STAR ChIP seq (small-scale TELP-assisted rapid ChIP seq) 200 mouse embryonic stem cells PWK/PhJ
More informationHost Genomics of HIV-1
4 th International Workshop on HIV & Aging Host Genomics of HIV-1 Paul McLaren École Polytechnique Fédérale de Lausanne - EPFL Lausanne, Switzerland paul.mclaren@epfl.ch Complex trait genetics Phenotypic
More informationNature Genetics: doi: /ng Supplementary Figure 1
Supplementary Figure 1 Illustrative example of ptdt using height The expected value of a child s polygenic risk score (PRS) for a trait is the average of maternal and paternal PRS values. For example,
More informationFunctional annotation of farm animal genomes: ChIP-seq.
Functional annotation of farm animal genomes: ChIP-seq Richard Crooijmans 2018, PAGXXVI Richard.Crooijmans@wur.nl Why FAANG is important Understanding the genotype to phenotype link This needs: - genomic
More informationSupervised Learner for the Prediction of Hi-C Interaction Counts and Determination of Influential Features. Tyler Yue Lab
Supervised Learner for the Prediction of Hi-C Interaction Counts and Determination of Influential Features Tyler Derr @ Yue Lab tsd5037@psu.edu Background Hi-C is a chromosome conformation capture (3C)
More informationQTL Studies- Past, Present and Future. David Evans
QTL Studies Past, Present and Future David Evans Genetic studies of complex diseases have not met anticipated success Glazier et al, Science (2002) 298:23452349 Korstanje & Pagan (2002) Nat Genet Korstanje
More information5/2/18. After this class students should be able to: Stephanie Moon, Ph.D. - GWAS. How do we distinguish Mendelian from non-mendelian traits?
corebio II - genetics: WED 25 April 2018. 2018 Stephanie Moon, Ph.D. - GWAS After this class students should be able to: 1. Compare and contrast methods used to discover the genetic basis of traits or
More informationNature Immunology: doi: /ni Supplementary Figure 1. Characteristics of SEs in T reg and T conv cells.
Supplementary Figure 1 Characteristics of SEs in T reg and T conv cells. (a) Patterns of indicated transcription factor-binding at SEs and surrounding regions in T reg and T conv cells. Average normalized
More informationEpigenetics. Jenny van Dongen Vrije Universiteit (VU) Amsterdam Boulder, Friday march 10, 2017
Epigenetics Jenny van Dongen Vrije Universiteit (VU) Amsterdam j.van.dongen@vu.nl Boulder, Friday march 10, 2017 Epigenetics Epigenetics= The study of molecular mechanisms that influence the activity of
More informationNew Enhancements: GWAS Workflows with SVS
New Enhancements: GWAS Workflows with SVS August 9 th, 2017 Gabe Rudy VP Product & Engineering 20 most promising Biotech Technology Providers Top 10 Analytics Solution Providers Hype Cycle for Life sciences
More informationRESEARCHER S NAME: Làszlò Tora RESEARCHER S ORGANISATION: Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC)
Thursday 5 November EU-India PARTNERING EVENT Theme: Health RESEARCHER S NAME: Làszlò Tora RESEARCHER S ORGANISATION: Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC) CNRS, INSERM,
More informationSupplementary Figure 1. Nature Genetics: doi: /ng.3736
Supplementary Figure 1 Genetic correlations of five personality traits between 23andMe discovery and GPC samples. (a) The values in the colored squares are genetic correlations (r g ); (b) P values of
More informationThe Epigenome Tools 2: ChIP-Seq and Data Analysis
The Epigenome Tools 2: ChIP-Seq and Data Analysis Chongzhi Zang zang@virginia.edu http://zanglab.com PHS5705: Public Health Genomics March 20, 2017 1 Outline Epigenome: basics review ChIP-seq overview
More informationEpigenetic Mechanisms
RCPA Lecture Epigenetic chanisms Jeff Craig Early Life Epigenetics Group, MCRI Dept. of Paediatrics Overview What is epigenetics? Chromatin The epigenetic code What is epigenetics? the interactions of
More informationIntroduction to Systems Biology of Cancer Lecture 2
Introduction to Systems Biology of Cancer Lecture 2 Gustavo Stolovitzky IBM Research Icahn School of Medicine at Mt Sinai DREAM Challenges High throughput measurements: The age of omics Systems Biology
More informationThe Risk of Anti-selection in Protection Business from Advances in Statistical Genetics
The Risk of Anti-selection in Protection Business from Advances in Statistical Genetics Richard Russell, PhD Lead Health Data Scientist Stephen Courquin Head of UK Actuarial Research Peter Banthorpe SVP,
More information7SK ChIRP-seq is specifically RNA dependent and conserved between mice and humans.
Supplementary Figure 1 7SK ChIRP-seq is specifically RNA dependent and conserved between mice and humans. Regions targeted by the Even and Odd ChIRP probes mapped to a secondary structure model 56 of the
More informationSupplementary Figure 1: Attenuation of association signals after conditioning for the lead SNP. a) attenuation of association signal at the 9p22.
Supplementary Figure 1: Attenuation of association signals after conditioning for the lead SNP. a) attenuation of association signal at the 9p22.32 PCOS locus after conditioning for the lead SNP rs10993397;
More informationFOXO3 Regulates Fetal Hemoglobin Levels in Sickle Cell Anemia. Yankai Zhang, Jacy R. Crosby, Eric Boerwinkle, Vivien A. Sheehan
FOXO3 Regulates Fetal Hemoglobin Levels in Sickle Cell Anemia Yankai Zhang, Jacy R. Crosby, Eric Boerwinkle, Vivien A. Sheehan Sickle Cell Anemia Steinberg MH. N Engl J Med 1999;340:1021-1030. Akinsheye
More informationTranscript-indexed ATAC-seq for immune profiling
Transcript-indexed ATAC-seq for immune profiling Technical Journal Club 22 nd of May 2018 Christina Müller Nature Methods, Vol.10 No.12, 2013 Nature Biotechnology, Vol.32 No.7, 2014 Nature Medicine, Vol.24,
More informationAnnotation of Functional Regulatory Elements in Livestock Species
Annotation of Functional Regulatory Elements in Livestock Species Midwest ASAS March 17, 2015 Huaijun Zhou 1, Pablo Ross 1, Ian Korf 1, Mary Delany 1, Hans Cheng 2, Juan Medrano 1, Alison Van Eenennaam
More informationGene Regulation Part 2
Michael Cummings Chapter 9 Gene Regulation Part 2 David Reisman University of South Carolina Other topics in Chp 9 Part 2 Protein folding diseases Most diseases are caused by mutations in the DNA that
More informationIntroduction to genetic variation. He Zhang Bioinformatics Core Facility 6/22/2016
Introduction to genetic variation He Zhang Bioinformatics Core Facility 6/22/2016 Outline Basic concepts of genetic variation Genetic variation in human populations Variation and genetic disorders Databases
More informationHigh-Throughput Sequencing Course
High-Throughput Sequencing Course Introduction Biostatistics and Bioinformatics Summer 2017 From Raw Unaligned Reads To Aligned Reads To Counts Differential Expression Differential Expression 3 2 1 0 1
More informationMendelian Randomization
Mendelian Randomization Drawback with observational studies Risk factor X Y Outcome Risk factor X? Y Outcome C (Unobserved) Confounders The power of genetics Intermediate phenotype (risk factor) Genetic
More informationRaymond Auerbach PhD Candidate, Yale University Gerstein and Snyder Labs August 30, 2012
Elucidating Transcriptional Regulation at Multiple Scales Using High-Throughput Sequencing, Data Integration, and Computational Methods Raymond Auerbach PhD Candidate, Yale University Gerstein and Snyder
More informationTranscriptional control in Eukaryotes: (chapter 13 pp276) Chromatin structure affects gene expression. Chromatin Array of nuc
Transcriptional control in Eukaryotes: (chapter 13 pp276) Chromatin structure affects gene expression Chromatin Array of nuc 1 Transcriptional control in Eukaryotes: Chromatin undergoes structural changes
More informationA rare variant in MYH6 confers high risk of sick sinus syndrome. Hilma Hólm ESC Congress 2011 Paris, France
A rare variant in MYH6 confers high risk of sick sinus syndrome Hilma Hólm ESC Congress 2011 Paris, France Disclosures I am an employee of decode genetics, Reykjavik, Iceland. Sick sinus syndrome SSS is
More informationEPIGENOMICS PROFILING SERVICES
EPIGENOMICS PROFILING SERVICES Chromatin analysis DNA methylation analysis RNA-seq analysis Diagenode helps you uncover the mysteries of epigenetics PAGE 3 Integrative epigenomics analysis DNA methylation
More informationGolden Helix s End-to-End Solution for Clinical Labs
Golden Helix s End-to-End Solution for Clinical Labs Steven Hystad - Field Application Scientist Nathan Fortier Senior Software Engineer 20 most promising Biotech Technology Providers Top 10 Analytics
More informationgenomics for systems biology / ISB2020 RNA sequencing (RNA-seq)
RNA sequencing (RNA-seq) Module Outline MO 13-Mar-2017 RNA sequencing: Introduction 1 WE 15-Mar-2017 RNA sequencing: Introduction 2 MO 20-Mar-2017 Paper: PMID 25954002: Human genomics. The human transcriptome
More informationFragile X Syndrome. Genetics, Epigenetics & the Role of Unprogrammed Events in the expression of a Phenotype
Fragile X Syndrome Genetics, Epigenetics & the Role of Unprogrammed Events in the expression of a Phenotype A loss of function of the FMR-1 gene results in severe learning problems, intellectual disability
More informationHigh Throughput Sequence (HTS) data analysis. Lei Zhou
High Throughput Sequence (HTS) data analysis Lei Zhou (leizhou@ufl.edu) High Throughput Sequence (HTS) data analysis 1. Representation of HTS data. 2. Visualization of HTS data. 3. Discovering genomic
More informationGenetics in the Health of African Americans: Obesity and Ovarian Cancer. Taylor Walker
Genetics in the Health of African Americans: Obesity and Ovarian Cancer Creative Inquiry for a Senior Thesis Taylor Walker twalke30@illinois.edu Molecular and Cellular Biology with Pre-Medicine Concentration,
More informationComparison of open chromatin regions between dentate granule cells and other tissues and neural cell types.
Supplementary Figure 1 Comparison of open chromatin regions between dentate granule cells and other tissues and neural cell types. (a) Pearson correlation heatmap among open chromatin profiles of different
More informationGenes, Aging and Skin. Helen Knaggs Vice President, Nu Skin Global R&D
Genes, Aging and Skin Helen Knaggs Vice President, Nu Skin Global R&D Presentation Overview Skin aging Genes and genomics How do genes influence skin appearance? Can the use of Genomic Technology enable
More informationGENETIC SUSCEPTIBILITY TO CANCER
page 1 / 5 page 2 / 5 genetic susceptibility to cancer pdf Coverage Policy...1 General Criteria for Germline Mutation Genetic Testing: Hereditary Cancer Susceptibility/Risk Assessment...2 Genetic Testing
More informationAre you the way you are because of the
EPIGENETICS Are you the way you are because of the It s my fault!! Nurture Genes you inherited from your parents? Nature Experiences during your life? Similar DNA Asthma, Autism, TWINS Bipolar Disorders
More informationFrontiers in Personalized Medicine. PW-GW-AS DNA sequencing Reverse human genetics
Frontiers in Personalized Medicine PW-GW-AS DNA sequencing Reverse human genetics Published Genome-Wide Associations through 06/2011, 1,449 published GWA at p 5x10-8 for 237 traits NHGRI GWA Catalog www.genome.gov/gwastudies
More informationYingying Wei George Wu Hongkai Ji
Stat Biosci (2013) 5:156 178 DOI 10.1007/s12561-012-9066-5 Global Mapping of Transcription Factor Binding Sites by Sequencing Chromatin Surrogates: a Perspective on Experimental Design, Data Analysis,
More informationRare Variant Burden Tests. Biostatistics 666
Rare Variant Burden Tests Biostatistics 666 Last Lecture Analysis of Short Read Sequence Data Low pass sequencing approaches Modeling haplotype sharing between individuals allows accurate variant calls
More informationDoing more with genetics: Gene-environment interactions
2016 Alzheimer Disease Centers Clinical Core Leaders Meeting Doing more with genetics: Gene-environment interactions Haydeh Payami, PhD On behalf of NeuroGenetics Research Consortium (NGRC) From: Joseph
More information10/19/2017. How Nutritional Genomics Affects You in Nutrition Research and Practice Joyanna Hansen, PhD, RD & Kristin Guertin, PhD, MPH
Disclosures Joyanna Hansen How Affects You in Nutrition Research and Practice Joyanna Hansen, PhD, RD & Kristin Guertin, PhD, MPH Consultant Nutricia North America Research Support Academy of Nutrition
More informationLarge-scale identity-by-descent mapping discovers rare haplotypes of large effect. Suyash Shringarpure 23andMe, Inc. ASHG 2017
Large-scale identity-by-descent mapping discovers rare haplotypes of large effect Suyash Shringarpure 23andMe, Inc. ASHG 2017 1 Why care about rare variants of large effect? Months from randomization 2
More informationDISSERTATION. Adam Michael Suhy. Graduate Program in Integrated Biomedical Science Program. The Ohio State University. Dissertation Committee:
Regulation of Cholesteryl Ester Transfer Protein and Expression of Transporters in the Blood Brain Barrier DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
More informationGenetic association analysis incorporating intermediate phenotypes information for complex diseases
University of Iowa Iowa Research Online Theses and Dissertations Fall 2011 Genetic association analysis incorporating intermediate phenotypes information for complex diseases Yafang Li University of Iowa
More informationChapter 1 : Genetics 101
Chapter 1 : Genetics 101 Understanding the underlying concepts of human genetics and the role of genes, behavior, and the environment will be important to appropriately collecting and applying genetic
More informationSupplemental Figure 1: Asymmetric chromatin maturation leads to epigenetic asymmetries on sister chromatids.
Supplemental Material: Annu. Rev. Cell Dev. Biol. 2017. 33:291 318 https://doi.org/10.1146/annurev-cellbio-100616-060447 The Inherent Asymmetry of DNA Replication Snedeker, Wooten, and Chen Supplemental
More informationNature Genetics: doi: /ng Supplementary Figure 1. Assessment of sample purity and quality.
Supplementary Figure 1 Assessment of sample purity and quality. (a) Hematoxylin and eosin staining of formaldehyde-fixed, paraffin-embedded sections from a human testis biopsy collected concurrently with
More informationGenetic Analysis of Anxiety Related Behaviors by Gene Chip and In situ Hybridization of the Hippocampus and Amygdala of C57BL/6J and AJ Mice Brains
Genetic Analysis of Anxiety Related Behaviors by Gene Chip and In situ Hybridization of the Hippocampus and Amygdala of C57BL/6J and AJ Mice Brains INTRODUCTION To study the relationship between an animal's
More informationBreast cancer. Risk factors you cannot change include: Treatment Plan Selection. Inferring Transcriptional Module from Breast Cancer Profile Data
Breast cancer Inferring Transcriptional Module from Breast Cancer Profile Data Breast Cancer and Targeted Therapy Microarray Profile Data Inferring Transcriptional Module Methods CSC 177 Data Warehousing
More informationChIP-seq data analysis
ChIP-seq data analysis Harri Lähdesmäki Department of Computer Science Aalto University November 24, 2017 Contents Background ChIP-seq protocol ChIP-seq data analysis Transcriptional regulation Transcriptional
More informationGeneOverlap: An R package to test and visualize
GeneOverlap: An R package to test and visualize gene overlaps Li Shen Contact: li.shen@mssm.edu or shenli.sam@gmail.com Icahn School of Medicine at Mount Sinai New York, New York http://shenlab-sinai.github.io/shenlab-sinai/
More informationREVIEWERS' COMMENTS: Reviewer #1 (Remarks to the Author):
Editorial Note: this manuscript has been previously reviewed at another journal that is not operating a transparent peer review scheme. This document only contains reviewer comments and rebuttal letters
More informationTranscription and chromatin. General Transcription Factors + Promoter-specific factors + Co-activators
Transcription and chromatin General Transcription Factors + Promoter-specific factors + Co-activators Cofactor or Coactivator 1. work with DNA specific transcription factors to make them more effective
More informationEpigenetics: Basic Principals and role in health and disease
Epigenetics: Basic Principals and role in health and disease Cambridge Masterclass Workshop on Epigenetics in GI Health and Disease 3 rd September 2013 Matt Zilbauer Overview Basic principals of Epigenetics
More informationWalking upright Specific changes in chewing design: teeth, jaws and skull. Homonoidea, Hominidae, Hominininae, Hominini, Hominina, Homo
Bio 1M: Hominins (complete) 1 Emergence Hominins refer to people and our upright ancestors Characterized by: Walking upright Specific changes in chewing design: teeth, jaws and skull Taxonomy Homonoidea,
More informationIdentifying the Zygosity Status of Twins Using Bayes Network and Estimation- Maximization Methodology
Identifying the Zygosity Status of Twins Using Bayes Network and Estimation- Maximization Methodology Yicun Ni (ID#: 9064804041), Jin Ruan (ID#: 9070059457), Ying Zhang (ID#: 9070063723) Abstract As the
More informationDavid Tamborero, PhD
David Tamborero, PhD Lopez-Bigas' lab Study of Tumor Genomes Study of Tumor Genomes Study sequencing data of tumors to understand the biological mechanisms shaping the mutational processes observed at
More informationInteraction of Genes and the Environment
Some Traits Are Controlled by Two or More Genes! Phenotypes can be discontinuous or continuous Interaction of Genes and the Environment Chapter 5! Discontinuous variation Phenotypes that fall into two
More informationResearch in IBD at University of Colorado Denver
Research in IBD at University of Colorado Denver Blair Fennimore, MD Assistant Professor of Medicine Division of Gastroenterology and Hepatology UCH Crohn s and Colitis Center Mucosal Inflammation Program
More informationAn Introduction to Quantitative Genetics I. Heather A Lawson Advanced Genetics Spring2018
An Introduction to Quantitative Genetics I Heather A Lawson Advanced Genetics Spring2018 Outline What is Quantitative Genetics? Genotypic Values and Genetic Effects Heritability Linkage Disequilibrium
More informationQuantitative genetics: traits controlled by alleles at many loci
Quantitative genetics: traits controlled by alleles at many loci Human phenotypic adaptations and diseases commonly involve the effects of many genes, each will small effect Quantitative genetics allows
More informationAssessing Accuracy of Genotype Imputation in American Indians
Assessing Accuracy of Genotype Imputation in American Indians Alka Malhotra*, Sayuko Kobes, Clifton Bogardus, William C. Knowler, Leslie J. Baier, Robert L. Hanson Phoenix Epidemiology and Clinical Research
More informationPeak-calling for ChIP-seq and ATAC-seq
Peak-calling for ChIP-seq and ATAC-seq Shamith Samarajiwa CRUK Autumn School in Bioinformatics 2017 University of Cambridge Overview Peak-calling: identify enriched (signal) regions in ChIP-seq or ATAC-seq
More informationEpigenetics and Autoimmune Disease
Epigenetics and Autoimmune Disease Lisa F. Barcellos, PhD, MPH Associate Professor UC Berkeley School of Public Health QB3 Genetic Epidemiology and Genomics Laboratory ACTRIMS, May 30, 2014 Dallas, TX
More informationAlice Sigurdson, Ph.D.
Alice Sigurdson, Ph.D. Radiation Epidemiology Branch Division of Cancer Epidemiology and Genetics ICRP Committee 1 ICRP Symposium on the International System of Radiological Protection Radiation Effects:
More informationEVOLUTION. Reading. Research in my Lab. Who am I? The Unifying Concept in Biology. Professor Carol Lee. On your Notecards please write the following:
Evolution 410 9/5/18 On your Notecards please write the following: EVOLUTION (1) Name (2) Year (3) Major (4) Courses taken in Biology (4) Career goals (5) Email address (6) Why am I taking this class?
More information