And so as a use case for genomic prediction, we might be calculating breeding values to say, select for larger cows, for example. /CS /DeviceRGB /Resources << Our software has been cited in 1,000s of peer-reviewed publications such as Science and Nature and has been very well received by the industry. So the data that I'm going to be using today is from the Bovine HapMap project with the Illumina 50k genotypes with simulated phenotypes. Before we begin, I would just like to remind our audience of our Q&A process. So if I go ahead and open up our genotypes spreadsheet. And this can be explained by our exponential growth in the worldwide population due to advances in both science and medicine. >> OK. And so take a look at that. So if I go back to our genotype menu, we can find an analysis for our identity by descent, by going to quality assurance and utilities, and we can see here there's that IBD option here. So we're gonna go ahead and select that one, since we definitely want to include that in our prediction. 5 265 0 R 6 265 0 R 7 266 0 R 8 267 0 R 9 268 0 R

So our goal is when creating SVS was to have high performance but easy to use software with rich visualization tools that would make SVS an obvious and popular choice for genetic researchers. Check out, please ⇒ www.HelpWriting.net ⇐, Very nice tips on this. /Lang (en-US) We'll use those observed P values. And so here in the spreadsheet, we can see in which sample and then which fold of these predictions were made. 9 0 obj 234 0 R 235 0 R 236 0 R 237 0 R 238 0 R 239 0 R 240 0 R 241 0 R 242 0 R 244 0 R 5 0 obj • Genotype once and sequence repeatedly. (GWAS) have already been conducted in wheat. Structure of this lecture • Recap some concepts (SAS tutorial later) • Discuss GWAS • Look at the steps in running & analyzing results GWAS • Lab – analyze a GWAS • SAS tutorial 4. And then our columns are the list of all of our SNPs that were important to project as well. If you happen to be performing NGS based CNV analysis, Golden Helix is the market leader. Looking at our R-squared value to see if that's true. /ColorTransform 0 So it does look similar to the run that we performed with gBLUP. Users can integrate all of these features into a standardized workflow which can be automated even more with VSPipeline. And so we can go ahead and open that up. And we'll also go ahead and adjust these bin sizes to sort of give us a better representation of our call rates across our samples. And then comparing this to what is observed from sample to sample. /F6 48 0 R << /F2 44 0 R Advantages of Genome-wide association study (GWAS): The genome-wide association study will help to find out the genes associated with particular complex disease. Hello and welcome to Golden Helix's final webcast for 2019. >> And in SVS there are a few ways to calculate a genomic relationship matrix to help us answer that question. 1 0 obj And I'll give you a couple seconds to answer that one as well.

We may still have some inflation, but otherwise this all looks pretty good and then we can also see our significant SNPs up here. 160 0 R 161 0 R 162 0 R 163 0 R 164 0 R 165 0 R 210 0 R 211 0 R 212 0 R 213 0 R 253 0 R 254 0 R 255 0 R 256 0 R 257 0 R 257 0 R 257 0 R 257 0 R 257 0 R 257 0 R Learn more. endobj And so we can see that we have our biggest difference between our top two here, but maybe also this third one. 131 0 R 132 0 R 133 0 R 275 0 R 152 0 R 153 0 R 154 0 R 155 0 R 156 0 R 276 0 R We need to go perform some quality control procedures on our samples and markers. But you do also get this concise list of principal components and their corresponding eigenvalues. /Width 50 And it will scroll down to our principal component analysis. Sr.MSc, PALB 2235 And so to set the stage for what we need to know and what our goals are in performing genomic prediction, I'm gonna go ahead and switch gears and go back to our slides. GWAS based on Linkage Disequilibrium (LD) • LD is the non-random correlation or association of alleles at two loci • D, D′ (normalized), and r2 are commonly used summary statistics to estimate pairwise LD • r2 is preferred in association studies because it is more indicative of … Panels (A) and (B) were adapted with permission from Gibb, B., Silverstein, T. D., Finkelstein, I. J., & Greene, E. C. (2012). And so we won't have to run our IBD on those.

>> But all of these methodologies available in SVS are accompanied with an incredible and intuitive visualization powered by GenomeBrowse. 155 0 R 156 0 R 157 0 R 157 0 R 157 0 R 157 0 R 157 0 R 157 0 R 158 0 R 158 0 R This is achievable through a partnership with Sentieon who provides the alignment and variant calling steps to produce VCF and BAM files. And so that's in this line is represented as each sample compared to itself. /Group << /S /Transparency For today, since we're going to be looking at GWAS GenomeBrowser will produce a publication ready Manhattan plots, but also linkage disequilibrium plots as well. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. And so I have merged our genotype and our phenotype spreadsheet here for our association test. And so any changes that you make to elements in the project here in the navigator window, they're going to be reported here and it's going to log what that change was, who made the change and when. How SVS can be used to perform GWAS and genomic prediction on a cattle dataset; Analyze high-quality SNPs by performing the association test with and without quality control filters; Quality control metrics, including evaluating sample statistics to check for outliers, identifying samples that are poor in quality, identifying SNPs that are in linkage disequilibrium, and identifying samples that depart from expected population stratification; Genomic prediction with K-Fold for both gBLUP and Bayes C-pi to estimate which genotypes best predict our desired phenotype. 3 0 obj All right. It's wonderful to have you with us today.

<< endobj

And we can take a closer look at this in a minute. And it was a little bit more scattered. /Parent 2 0 R 118 0 R 119 0 R 120 0 R 121 0 R 274 0 R 126 0 R 127 0 R 128 0 R 129 0 R 130 0 R Can we still perform association tests in SVS.

of Genetics & Plant Breeding /F4 46 0 R So but our FAS team is always available to help troubleshoot the import process with you. SNP discovery and selection of a subset of highly designable markers. 245 0 R 246 0 R 247 0 R 248 0 R 249 0 R 250 0 R 251 0 R 252 0 R 253 0 R 254 0 R There we go. But it is standard practice before running your identity by descent estimation to go ahead and do some LD pruning as well. /Contents [65 0 R 66 0 R] You can change your ad preferences anytime. 6 0 obj endobj I've compared our top two principal components on our X and Y axis. And so we'll go to our genotype menu again. /X10 16 0 R To maximize the identification of relevant polymorphic SNPs, four genetically distant P. sativum genotypes were selected for genomic DNA preparation and HiSeq sequencing. Additionally, users have access to automated AMP or ACMG variant guidelines as well as the capability to detect copy number variations. And so we have that additive model that we want to make sure that we select based on our principal component analysis. When we're working with a dataset for the first time, we might want to, it's helpful to look at our sample statistics to determine any information about our samples, in particular, if we have any outliers. /Resources << We can go ahead and move into the Q&A if you're ready. /F9 62 0 R But let's say we did have some lower call rates just so I can show you guys this. So we have a lot of users that are handling thousands of samples and millions of markers for association test, large end studies and also even imputation. But at this point we have covered which methods are available in SVS for genomic prediction and highlighted some of the similarities and differences between them. All rights reserved | Looking for Golden Helix Symposia?

>> ★★★ http://t.cn/A6vI6Tyi. So we might be looking at correcting our data for the first two or maybe three principal components. And so let's go ahead and return back to our cattle data set and take a look at some of the results from our genomic prediction.

GWAS is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. /F8 50 0 R /Creator >> You can still perform basic association tests to identify any of those significant markers, even without application of a marker map. And so a better way to really look at this is to plot our expected predicted phenotype with our actual phenotype. It looks like our lowest one is around .98. June 13th 2013 3. An example of that might be a sample eight here. 7 0 obj And it's going to contain all of our mapping information for each SNP. B., Kozlov, A. G., Schroeder, C. M., Lohman, T. M., & Ha, T. (2014). So today what I'll be covering is I'll be going through a GWAS workflow, including some various quality control metrics. %PDF-1.5 So let's go ahead and take a look at those now. University of Agricultural Sciences And so we all go ahead and take a look at those Manhattan plots now. Well, unfortunately, we are running out of time or at the top of our hour, so we will not be able to get to everyone's questions today.

/F1 43 0 R >> So let's go ahead and open that up. Although there also is some limited RNA-seq capabilities as well. /Type /Page 98 0 R 99 0 R 100 0 R 101 0 R 102 0 R 103 0 R 104 0 R] /ColorSpace /DeviceRGB And another difference is that gBLUP computes results a bit faster than Bayes C-Pi because generally Bayes C-Pi is more computationally demanding. And the FAS team here at Golden Helix is here to help you with this import process. It really makes sure that any future statistical tests that you run will be on quality samples and markers. >> /Contents 61 0 R >> /Subtype /Image GKVK, Bangalore-65 OK. First and foremost, again, I would like to acknowledge that we are very grateful and appreciate the NIH grant funding that we received. And so from this spreadsheet, I have produced a heat map that makes visualizing this data a little bit nicer. From there, you can clip your favorite slides or download the entire deck to your computer. Now I will turn things back over to Delaina and she will provide some more details on PAG as well as some other housekeeping items. And the dataset consists of 472 Bos Taurus samples that we will first use in our GWAS workflow. stream >>

/Producer << /X7 15 0 R But really what we're looking at is we want to make sure that this relationship would be linear. And something that we might want to look at here is any clustering or outliers in that, it's also helpful by coloring by fold. /S /Transparency So why is this relevant to you? The variations in unzipping force have been proposed to be used to analyze DNA sequence. Thank you, Julia. So once we have our data imported into SVS, we want to make sure that we have adequately prepared our data because we don't want anything influencing the result of our association test. 171 0 R 172 0 R 173 0 R 174 0 R 175 0 R 176 0 R 177 0 R 178 0 R 179 0 R 180 0 R

258 0 R 259 0 R 260 0 R 261 0 R 262 0 R 263 0 R] And so you'd want to subset this. And so then we would go ahead and click OK to run this.