Changes

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.  

* 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.

* 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 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.

* 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?