1. Genome Wide Association Studies Zhiwu Zhang Washington State University

2.  Administration  Why this course  Overview Outline

3.  No recording (video or audio)  http://ZZLab.Net (link on the left) http://ZZLab.Net  http://zzlab.net/GWAS2016WUHAN/ http://zzlab.net/GWAS2016WUHAN/  WeChat Administration

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

5. Hosts: 25 Guests: 25 Online: ?

6. Teaching team Xiaolei Liu Guanghui HuJiabo WangMeijing LiangYou Tang

7. Thanks to organizers Shuhong ZhaoMei Yu

8.  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

9.  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

10. 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

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

12.  Assignments: five in total  Due 5:00PM, 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

13. 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%

14. Hypothesis (demo)

15. Result (demo)

16. Method (demo)

17. Presentation (demo)

20. http://link.springer.com/book/10.1 007%2F978-1-62703-447-0 Text book

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

22. More Research on GWAS and GS By May 31, 2013

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

24.  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

25. Associations on flowering time

26. 2/3 of Statistical Genomics at WSU

27. 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)

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

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