1. Genotype-phenotype association with FaST-LMM in the DE Today we will work through example analyses and discuss our results.

2. overview Brief review of GWAS and what we will do in the tutorial Your team will select a data folder and run two of the analyses. We will compare results and discuss next steps.

3. Associate genetic differences in a population of individuals with phenotype differences, using a fast mixed model method that controls for potentially confounding genetic relatedness. Barley is one of the most highly adapted cereal grains with production occurring in climates ranging from sub-Arctic to subtropical. Because of its use in malt production, barley is grown in many areas of the world for cultural as well as economic reasons. Now let’s each find a fun fact about barley, pick one example from your team, and share your fun fact with the group.

4. How do association analyses work and what can you find out? One of the first pieces of information is the genetic architecture—how many important chromosomal loci there are for your measured phenotype. What do you need to do an association analysis? more than one individual…usually thousands DNA differences (alleles) throughout the chromosomes of those individuals (ie SNPs) and you need measured phenotypes, also called traits. The traits need to be measured in a way that we can fit them with our statistical methods e. g. for simple regression we need numerical phenotypes The identifier code for each individual links the many SNPs from that individual with the measured phenotype value. Discuss questions about association with your group, write down the key issues to discuss at the end of the module once you’ve seen more of this process.

5. Data from the USDA-funded BarleyCAP2 genotype panel, grain yield phenotype. There are four folders of data: Two are from the Montana State (MT) breeding program, one from an irrigated field and one from a dry (drought) field. The next two are from the Corvallis (C) breeding program, one from normal and the other from fungal-disease-affected plants. Please be respectful of these data sets, they are released prepublication. Full details of the agreement may be found at http://triticeaetoolbox.org/barley/toronto.php. For more information on barley data sets look around at T3T, http://triticeaetoolbox.org/barley/. http://triticeaetoolbox.org/barley/toronto.phphttp://triticeaetoolbox.org/barley/ Let’s get started by going to the tutorial page. Click on the link at https://pods.iplantcollaborative.org/wiki/display/eot/associat ion+FaSTLMM+workshop+materials or navigate through via the workshop schedule page.

6. please scroll down to the ‘step-by-step’ section when you are ready to get started Each team will analyze two data sets. Choose the MT or C data sets and decide who will have the control and treatment sets.

7. From looking at your plot, what can you conclude about genetic architecture of your trait in your barley population? Share your conclusions with your team-mate. Do you see the same significant markers in the two treatments within a breeding program? Compare your results with a your team-mate who did the other treatment to answer this question and share your answer.

8. This difference between two treatments/environments is plasticity, also called genotype by environment interaction. We’ve found this today by doing ‘map comparison’. It is more statistically sound to fit an explicit GxE interaction term in your analysis—this can be done using the QxPak app in iPlant. You can also determine the effect size of your SNP, in other words how much of the phenotype difference is explained by that allele, in FaST-LMM. Meet with Ann later today if you’d like to learn more or plan your own analysis!