Genome Wide Association Studies Zhiwu Zhang Washington State University.

Slides:



Advertisements
Similar presentations
Elluminate as a virtual classroom Fang Lou 1. Outline of the session What is Elluminate? How do we use it? Overview of the Elluminate Different levels.
Advertisements

Statistical Methods in Computer Science Course Introduction Ido Dagan.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Course outline and schedule Introduction (Sec )
Object Oriented Programming (OOP) Design Lecture 1 : Course Overview Bong-Soo Sohn Assistant Professor School of Computer Science and Engineering Chung-Ang.
Object Oriented Programming (OOP) Design Lecture 1 : Course Overview Bong-Soo Sohn Associate Professor School of Computer Science and Engineering Chung-Ang.
Course Introduction Software Engineering
ICS 6B Boolean Logic and Algebra Fall 2015
Part 0 -- Introduction Statistical Inference and Regression Analysis: Stat-GB , C Professor William Greene Stern School of Business IOMS.
Modern Control Hossein Moeinkhah Assistant Professor
CEN 4010 First Lecture January 9, 2006 CEN 4010 Introduction to Software Engineering Spring 2006 Instructor: Masoud Sadjadi
Quantitative Methods in Geography Geography 391. Introductions and Questions What (and when) was the last math class you had? Have you had statistics.
Object Oriented Programming (OOP) Design Lecture 1 : Course Overview Bong-Soo Sohn Associate Professor School of Computer Science and Engineering Chung-Ang.
GenABEL: an R package for Genome Wide Association Analysis
Washington State University
Statistical Genomics Zhiwu Zhang Washington State University Lecture 20: MLMM.
Statistical Genomics Zhiwu Zhang Washington State University Lecture 11: Power, type I error and FDR.
EGR 115 Introduction to Computing for Engineers Course Overview and Introduction Monday 29 Aug EGR 115 Introduction to Computing for Engineers Slide 1.
Audit Analytics --An innovative course at Rutgers Qi Liu Roman Chinchila.
Welcome to Physics 2215! Physics Lab for Scientist & Engineers 1 Spring 2013.
School of Mechanical, Industrial & Aeronautical Engineering
MSE 440: Processing of Metallic Materials
Object Oriented Programming (OOP) Design Lecture 1 : Course Overview
An Introduction to Measurement and Evaluation
Computer Network Fundamentals CNT4007C
Washington State University
電腦圖學 Computer Graphic with Programming
Advanced Accounting Information Systems Summer 2011
Probabilistic Analysis of Computer Systems
EGR 115 Introduction to Computing for Engineers
ECE 533 Digital Image Processing
Computer Networks CNT5106C
Usability Testing 3 CPSC 481: HCI I Fall 2014 Anthony Tang.
Welcome to Physics 2015! (General Physics Lab 1 – Spring 2013)
Telerik School Academy
CS5040: Data Structures and Algorithms
Washington State University
JavaScript Frameworks & AngularJS
Do Now: WELCOME to Algebra 1! Ms. LaVeglio and Mrs. Thompson
Object Oriented Programming (OOP) Design Lecture 1 : Course Overview
Washington State University
Psychological Science I: Statistics
Course Information and Introductions
Washington State University
Computer Networks CNT5106C
Washington State University
Andy Wang Operating Systems COP 4610 / CGS 5765
Washington State University
Washington State University
Lecture 23: Cross validation
Lecture 23: Cross validation
Andy Wang Operating Systems COP 4610 / CGS 5765
Andy Wang Operating Systems COP 4610 / CGS 5765
Washington State University
Project Management - ime380
Lecture 11: Power, type I error and FDR
C Programming Lecture 1 : Introduction
Course Outline Highlights
Lecture 11: Power, type I error and FDR
C Programming Lecture 1 : Introduction
WELCOME TO EE457 COMPUTER SYSTEMS ORGANIZATION
Washington State University
Computer Networks CNT5106C
Washington State University
Lecture 17: Likelihood and estimates of variances
Washington State University
Course Overview CSE5319/7319 Software Architecture and Design
STT215 Course Overview Collecting Data Exploring Data
Washington State University
CS 232 Geometric Algorithms: Lecture 1
Mastery Assessment in Teaching Statistics
Presentation transcript:

Genome Wide Association Studies Zhiwu Zhang Washington State University

 Administration  Why this course  Overview Outline

 No recording (video or audio)  (link on the left)   WeChat Administration

 Label account with real name  Use portrait starting from shoulders  No politics and religions WeChat

Hosts: 25 Guests: 25 Online: ?

Teaching team Xiaolei Liu Guanghui HuJiabo WangMeijing LiangYou Tang

Thanks to organizers Shuhong ZhaoMei Yu

 Beginners: Data process and tool selection  Experienced: Method selection and result interpretation  Advanced: Modeling and maximization of data values  Developers: genetic models, statistical models, coding and software engineering Attendants

 Mechanism of GWAS, pros and cons  Experiment design: false discoveries, power and accuracy  Analyses: methods and tools  Reasoning and critical thinking  Motivated through reinventing Objectives

Grade PercentageLetter 93%-100%A 90%-93%A- 87%-90%B+ 83%-87%B 80%-83%B- 77%-80%C+ 73%-77%C 70%-73%C- 66%-70%D+ 60%-66%D 0%-60%F Certificate

Participation Score No question, no discussion0% Question or discussion occasionally25% Question actively50% Discussion actively75% Question AND discussion actively100%

 Assignments: five in total  Due 5:00PM, Monday to Thursday  Submit by  PDF Report and R source code separately  PDF report is limited to five pages.  R source code should set seed for replicate of report  No late submission accepted. Answers are given on next day  Your homework may be selected for demonstration Homework

1.Hypotheses/statement 2.What did you observed (Results) 3.How to replicate your findings (Method) 4.Presentation: Description, figures and tables 5.R source code Homework components each takes 20%

Hypothesis (demo)

Result (demo)

Method (demo)

Presentation (demo)

007%2F Text book

Human genome Human genome 2 nd Generation Sequencing 2 nd Generation Sequencing

More Research on GWAS and GS By May 31, 2013

 As fast as one season  50~300 kb resolution

 Computing difficulties: millions of markers, individuals, and traits  False positives, ex: “Amgen scientists tried to replicate 53 high-profile cancer research findings, but could only replicate 6”, Nature, 2012, 483: 531  False negatives Problems in GWAS

Associations on flowering time

2/3 of Statistical Genomics at WSU

Schedule Lecture SectionTitleRemark 17/4/16FundamentalSyllabus, introduction, and R (L01, L02) 27/4/16 Random variables and distribution (L03)HW1 37/5/16 Statistical inference (L04) 47/5/16 Linear algebra (L05)HW2 57/6/16 Genetic architecture and simulation of phenotype (L08) 67/6/16GWASMechanism of GWAS (L09, L10)HW3 77/7/16 Power, type I error and False Discovery Rate (L11) 87/7/16 General Linear Model (GLM) (L13)HW4 97/8/16 Structure and Kinship (L12, L14) 107/8/16 Mixed Linear Model (MLM) and Compression (L15, L16)HW5 117/9/16 SUPER GWAS method (L19) 127/9/16 FarmCPU (L21)Exam (L##: CROPS545 lecture number)

 Morning: Theory  Afternoon: Practice and homework  Evening: Preparation Phases

 Active participation + HWs + Exam  GWAS: Very active for research and application  Rapid development Highlight