Genome Wide Association Studies Zhiwu Zhang Washington State

Genome Wide Association Studies Zhiwu Zhang Washington State University

Outline Administration Why this course Overview

Administration No recording (video or audio) http: //ZZLab. Net (link on the left) http: //zzlab. net/GWAS 2016 WUHAN/ We. Chat

We. Chat Label account with real name Use portrait starting from shoulders No politics and religions

Hosts: 25 Guests: 25 Online: ?

Teaching team Xiaolei Liu Guanghui Hu Jiabo Wang Meijing Liang You Tang

Thanks to organizers Shuhong Zhao Mei Yu

Attendants 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

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

Percentage 93%-100% 90%-93% 87%-90% 83%-87% 80%-83% 77%-80% 73%-77% 70%-73% 66%-70% 60%-66% 0%-60% Letter A AB+ B BC+ C CD+ D F Certificate Grade

Participation Score No question, no discussion 0% Question or discussion occasionally 25% Question actively 50% Discussion actively 75% Question AND discussion actively 100%

Homework Assignments: five in total Due 5: 00 PM, Monday to Thursday Submit by email 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 components each takes 20% 1. 2. 3. 4. 5. Hypotheses/statement What did you observed (Results) How to replicate your findings (Method) Presentation: Description, figures and tables R source code

Hypothesis (demo)

Result (demo)

Method (demo)

Presentation (demo)

Text book http: //link. springer. com/book/10. 1 007%2 F 978 -1 -62703 -447 -0

Human genome 2 nd Generation Sequencing http: //luckyrobot. com/wp-content/uploads/2013/04/nih-cost-genome. jpg

More Research on GWAS and GS By May 31, 2013

As fast as one season 50~300 kb resolution

Problems in GWAS 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

Associations on flowering time

2/3 of Statistical Genomics at WSU

Schedule (L##: CROPS 545 lecture number) Lecture Section Title 1 7/4/16 Fundamental Syllabus, introduction, and R (L 01, L 02) 2 7/4/16 Random variables and distribution (L 03) 3 7/5/16 Statistical inference (L 04) 4 7/5/16 Linear algebra (L 05) 5 7/6/16 Genetic architecture and simulation of phenotype (L 08) 6 7/6/16 GWAS Mechanism of GWAS (L 09, L 10) 7 7/7/16 Power, type I error and False Discovery Rate (L 11) 8 7/7/16 General Linear Model (GLM) (L 13) 9 7/8/16 Structure and Kinship (L 12, L 14) 10 7/8/16 Mixed Linear Model (MLM) and Compression (L 15, L 16) 11 7/9/16 SUPER GWAS method (L 19) 12 7/9/16 Farm. CPU (L 21) Remark HW 1 HW 2 HW 3 HW 4 HW 5 Exam

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

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