Bar Harbor Statistical Genetics Workshop - Revision history

Goncalo: /* Web Resources */

2011-07-21T18:54:00Z

Web Resources

← Older revision		Revision as of 14:54, 21 July 2011
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	* the UCSC genome browser at http://genome.ucsc.edu		* the UCSC genome browser at http://genome.ucsc.edu
	* the NCBI database collection at http://www.ncbi.nlm.nih.gov		* the NCBI database collection at http://www.ncbi.nlm.nih.gov
	* the swiss army knive of the early 21st ~~centry~~ at http://www.google.com		* the swiss army knive of the early 21st century at http://www.google.com

	To learn about genes that flank variants with tentative evidence of association in each of the three regions we are investigating, select the SNP showing the strongest evidence of association in each region and explore what you can learn from each of the resources above.		To learn about genes that flank variants with tentative evidence of association in each of the three regions we are investigating, select the SNP showing the strongest evidence of association in each region and explore what you can learn from each of the resources above.

Goncalo: /* Web Resources */

2011-07-21T18:53:47Z

Web Resources

← Older revision		Revision as of 14:53, 21 July 2011
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	* the UCSC genome browser at http://genome.ucsc.edu		* the UCSC genome browser at http://genome.ucsc.edu
	* the NCBI database collection at http://www.ncbi.nlm.nih.gov		* the NCBI database collection at http://www.ncbi.nlm.nih.gov
	* the swiss army knive of the early ~~20th~~ centry at http://www.google.com		* the swiss army knive of the early 21st centry at http://www.google.com

	To learn about genes that flank variants with tentative evidence of association in each of the three regions we are investigating, select the SNP showing the strongest evidence of association in each region and explore what you can learn from each of the resources above.		To learn about genes that flank variants with tentative evidence of association in each of the three regions we are investigating, select the SNP showing the strongest evidence of association in each region and explore what you can learn from each of the resources above.

Goncalo: /* Questions to Consider */

2011-07-21T17:47:32Z

Questions to Consider

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	* Do the boundaries of the association signal match the recombination map in each region?		* Do the boundaries of the association signal match the recombination map in each region?
	* Do the association signals clearly point to one gene?		* Do the association signals clearly point to one gene?

			== Are the Glucose Associated Variants Also Associated with Other Traits? ==

			If you have gotten this far and don't feel like twiddling your thumbs, you could proceed to investigate another question of interest. Specifically, do you have evidence that these same variants / loci are associated with other traits?

			One place to start would be by examining the results of genomewide association studies for related traits, such as available [http://statgen.sph.umich.edu/locuszoom/genform.php?type=ourdata in LocusZoom] or the [http://www.genome.gov/gwastudies/ NHGRI GWAS Catalog].

Goncalo: /* Glusose Data Set */

2011-07-21T17:36:04Z

Glusose Data Set

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	This workshop was prepared for the 51st Mammalian Genetics Short Course, at the Jackson Laboratory in Bar Harbor. It illustrates several simple analyses of genetic association studies (we are necessarily limited by time!).		This workshop was prepared for the 51st Mammalian Genetics Short Course, at the Jackson Laboratory in Bar Harbor. It illustrates several simple analyses of genetic association studies (we are necessarily limited by time!).

	= ~~Glusose~~ Data Set =		= Glucose Data Set =

	This example examines evidence for association between fasting glucose levels and genetic markers in the [http://www.sph.umich.edu/csg/abecasis/publications/18521185.html G6PC2] (chr. 2), GCK (chr. 7) and [http://www.sph.umich.edu/csg/abecasis/publications/19060907.html MTNR1B] (chr. 11) regions. It uses results from 3 genomewide association studies: [http://www.sph.umich.edu/csg/abecasis/publications/17463248.html FUSION], [http://www.sph.umich.edu/csg/abecasis/publications/PLOS-Obesity-Scan.html SardiNIA] and [http://www.ncbi.nlm.nih.gov/pubmed/17463246 DGI]. Genetic variants in the three loci impact fasting glucose levels and, in the case of MTRN1B, also impact the risk of type 2 diabetes.		This example examines evidence for association between fasting glucose levels and genetic markers in the [http://www.sph.umich.edu/csg/abecasis/publications/18521185.html G6PC2] (chr. 2), GCK (chr. 7) and [http://www.sph.umich.edu/csg/abecasis/publications/19060907.html MTNR1B] (chr. 11) regions. It uses results from 3 genomewide association studies: [http://www.sph.umich.edu/csg/abecasis/publications/17463248.html FUSION], [http://www.sph.umich.edu/csg/abecasis/publications/PLOS-Obesity-Scan.html SardiNIA] and [http://www.ncbi.nlm.nih.gov/pubmed/17463246 DGI]. Genetic variants in the three loci impact fasting glucose levels and, in the case of MTRN1B, also impact the risk of type 2 diabetes.

Goncalo: /* Your Tasks */

2010-07-22T18:58:02Z

Your Tasks

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	* Count the number of markers being examined. Let's call this number M.		* Count the number of markers being examined. Let's call this number M.
	* Sort observed p-values, from smallest to largest.		* Sort observed p-values, from smallest to largest.
	* Match each ~~expected~~ p-value with an expected p-value, which is simply its rank divided by M+1.		* Match each observed p-value with an expected p-value, which is simply its rank divided by M+1.
	* Because we are most interested in the smallest p-value, transform observed and expected p-values using the -log() function.		* Because we are most interested in the smallest p-value, transform observed and expected p-values using the -log() function.
	* Plot expected p-values along the X axis and actual p-values along the X-axis.		* Plot expected p-values along the X axis and actual p-values along the X-axis.

Goncalo: /* Your Tasks */

2010-07-22T17:47:19Z

Your Tasks

← Older revision		Revision as of 13:47, 22 July 2010
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	* Count the number of markers being examined. Let's call this number M.		* Count the number of markers being examined. Let's call this number M.
	* Sort observed p-values, from smallest to largest.		* Sort observed p-values, from smallest to largest.
	* Match each expected p-value with an expected p-value, which is simply its rank divided by M.		* Match each expected p-value with an expected p-value, which is simply its rank divided by M+1.
			* Because we are most interested in the smallest p-value, transform observed and expected p-values using the -log() function.
	* Plot expected p-values along the X axis and actual p-values along the X-axis.		* Plot expected p-values along the X axis and actual p-values along the X-axis.

Goncalo: /* Questions to Consider */

2010-07-22T15:35:01Z

Questions to Consider

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	=== Questions to Consider ===		=== Questions to Consider ===

			* What did you learn from each of the web resources?
			* What are the limitations of using web resources to study overlap between results?
			* Do the boundaries of the association signal match the recombination map in each region?
			* Do the association signals clearly point to one gene?

Goncalo: /* Plotting Regional Association Results */

2010-07-22T15:31:36Z

Plotting Regional Association Results

← Older revision		Revision as of 11:31, 22 July 2010
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	=== Plotting Regional Association Results ===		=== Plotting Regional Association Results ===

	The ability to display association study results in a visual manner can also be extremely useful.		The ability to display association study results in a visual manner can also be extremely useful. There are now several automated tools that facilitate the process of generating high quality visual displays of association study results and regional linkage disequilibrium patterns, including SNAP [http://www.broadinstitute.org/mpg/snap/], CandiSNPer [http://www2.hu-berlin.de/wikizbnutztier/software/CandiSNPer/] and LocusZoom [http://csg.sph.umich.edu/locuszoom/].

			Here, we will use LocusZoom to generate displays of association study results.

			To do this, you should:

			# Go to the LocusZoom website at http://csg.sph.umich.edu/locuszoom/ and select '''Plot Using Your Data'''
			# Upload your data (using the '''Browse ...''' button)
			# Provide key descriptors in your data, including header information for:
			## P-value column (should be PVALUE)
			## Marker name column (should be SNP)
			## Column delimiter (should be WhiteSpace)
			# Select a region to plot (for example, you could request plots in the regions surrounding G6PC2, MTNR1B or GCK)

			=== Questions to Consider ===

Goncalo: /* Plotting Regional Association Results */

2010-07-22T15:18:58Z

Plotting Regional Association Results

@@ Line 51: / Line 51: @@
 * Can you propose possible explanations for the patterns you observe in the QQ plot?
-== Plotting Regional Association Results ==
+== Interpreting Regional Association Results ==
-Another common task that geneticists encounter is the interpretation of study results in the context of nearby genes.
+=== Web Resources ===

Goncalo: /* Plotting Regional Association Results */

2010-07-22T13:24:34Z

Plotting Regional Association Results

← Older revision		Revision as of 09:24, 22 July 2010
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	== Plotting Regional Association Results ==		== Plotting Regional Association Results ==

			Another common task that geneticists encounter is the interpretation of study results in the context of nearby genes.