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DTSTAMP:20260616T135810Z
UID:bb9d08af-b3ea-43bd-a863-680ed42bed54
DTSTART:20240227T000000Z
DTEND:20240228T050000Z
DESCRIPTION:There are many interesting patterns that you can extract from g
 enetic variant data. This can include patterns of linkage\, balancing sele
 ction\, or even inbreeding signals. One of the most common approaches is t
 o find sites on the genome that are under selection. \n\nThis workshop int
 roduces the basics of genetic selection analysis. It will step you through
  the process of identifying signals of selection in an example genomic dat
 aset using the outlier analysis method. \n\n**Lead Trainer**: Dr Katarina 
 Stuart\, Research Fellow\, University of Auckland.\n\n**Date/Time:** 27 -2
 8 February 2024\, 1pm-6pm NZDT / 11am - 4pm AEDT / 10am - 3pm AEST / 10:30
 am - 3:30pm ACST / 8am - 1pm AWST ([check in your timezone](https://www.ti
 meanddate.com/worldclock/fixedtime.html?ah=5&amp\;iso=20240227T11&amp\;msg
 =Genetic%20Outlier%20Analysis&amp\;p1=152)) \n\n**Format:**\n\nThis online
  workshop will take place over two five-hour sessions. You must attend bot
 h sessions as they build on one another. Expert trainers will introduce ne
 w topics and guide you through hands-on activities to help you put your ne
 w skills into action.\n\n**Learning outcomes:**\n\nBy the end of the works
 hop you should be able to:\n\n1. Download example genomic data\n2. Use the
  PCAdapt tool to identify outlier loci within a genome.\n3. Use VCFtools t
 o identify outlier SNPs in pairwise population comparisons.\n4. Use Bayesc
 an and Baypass to identify outlier SNPs based on allele frequencies across
  multiple populations.\n5. Use Baypass to identify SNPs that are related t
 o phenotype (GWAS)\n6. Compare the results of the different methods and di
 scuss the results.\n\n**Who the workshop is for:**\n\nThis workshop is for
  researchers associated with an Australian organisation and/or members of 
 the Genetics Society of AustralAsia who will use genetic selection analysi
 s as part of their projects. \n\nThis workshop requires you to have:\n\n1.
  Some familiarity with biological concepts\, including the concept of sele
 ction. \n2. Basic command line (bash) knowledge. You must know how to navi
 gate the directory structure and copy files between the computers. If you 
 need a refresher on Unix/Linux try[ this online tutorial](https://linuxjou
 rney.com/lesson/the-shell).\n3. Basic knowledge of R. You must know how to
  set up directories\, run commands\, reading in and outputting files.  If 
 you need a refresher on R try the[ Introduction to R and RStudio section](
 https://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro.html) of
  this online tutorial.\n\n**How to apply:**\n\n**_[Apply here](https://gen
 etic-outliers.eventbrite.com.au/)_**\n\nThis workshop is free but particip
 ation is subject to application with selection. \n\nApplications close at 
 11:59pm AEDT\, Friday 9 February 2024.\n\nApplications will be reviewed by
  the organising committee and all applicants will be informed of the statu
 s of their application (successful\, waiting list\, unsuccessful) after th
 e closing date. More information on the selection process is provided in o
 ur[ advice on applying for Australian BioCommons workshops.](https://www.b
 iocommons.org.au/workshop-applications)\n\n_This workshop is presented by 
 the[ Australian BioCommons](https://www.biocommons.org.au/) and the[ Genet
 ics Society of AustralAsia](https://genetics.org.au/) with the assistance 
 of a network of facilitators from the national[ Bioinformatics Training Co
 operative](https://www.biocommons.org.au/training-cooperative)._\n\n_This 
 event is part of a series of[ bioinformatics training events](https://www.
 biocommons.org.au/events). If you'd like to hear when registrations open f
 or other events\, please[ subscribe](https://www.biocommons.org.au/subscri
 be) to Australian BioCommons._
SUMMARY:WORKSHOP: Genetic outlier analysis
URL;VALUE=URI:https://www.biocommons.org.au/events/genetic-outlier
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