glimmix models with permutation

Assume a factor coded under a genotypic model as the main effect without interaction

Estimate ‘dominant’  geno  -1 0.5 0.5  ‘recessive’ geno  -0.5 -0.5 1  ‘additive’   geno   0  0.3333333 0.6666667/adj=simulate;

The function may be available in other SAS Proc

Prioritizing GWAS Results

This review paper mentioned 3 analytic methods to prioritizing GWAS results (for follow-up studies):

• Meta-analysis seeks to pool information from multiple GWAS to increase the chances of finding true positives among the false positives and provides a way to combine associations across GWAS, even when the original data are unavailable.

• Testing for epistasis within a single GWAS study can identify the stronger results that are revealed when genes interact.

• Pathway analysis of GWAS results is used to prioritize genes and pathways within a biological context. Following a GWAS, association results can be assigned to pathways and tested in aggregate with computational tools and pathway databases.

HapMap 3: more people ~ more genetic variation

from here.
Centre d’Etude du Polymorphisme Humain collected in Utah, USA, with ancestry from northern and western Europe (CEU)
Han Chinese in Beijing, China (CHB)
Japanese in Tokyo, Japan (JPT)
Yoruba in Ibadan, Nigeria (YRI)
African ancestry in the southwestern USA (ASW)
Chinese in metropolitan Denver, Colorado, USA (CHD)
Gujarati Indians in Houston, Texas, USA (GIH)
Luhya in Webuye, Kenya (LWK)
Maasai in Kinyawa, Kenya (MKK)
Mexican ancestry in Los Angeles, California, USA (MXL)
Tuscans in Italy (Toscani in Italia, TSI)
So memorize some of those abbreviations! One particular difference across these populations is that some are parent-offspring trios, and some are not. So the CEU sample are trios, while the TSI are not.

IBS alternative

The publicly available IBS (Identity by State) macro in SAS is quite slow, and has a bug that IBS is not adjusted for call rate. I made an alternative implementation which is about 1/2 or 1/3 faster. However, SAS apparently handles the standard correlation calculations much much faster, and resulting Pearson correlation, Kendall correlation and Spearman correlation may give similar measurements to IBS (code is here, data include about 1000 markers clustered in candidate genes in 400 subjects, primary Caucasians). Therefor I guess in certain Kernel application, we can use usual correlation to replace IBS.

Personalized Medicine: A Discussion

http://blogs.forbes.com/matthewherper/2011/02/25/personalized-medicine-a-discussion/

average heterzygosity

from Ascertainment bias in studies of human genome-wide polymorphism
A simple comparison of the HapMap and Perlegen genotype data was done by considering the 5682 windows of 500 kb across the entire genome and, for each window, tallying the SFS and calculating summary statistics such as average heterozygosity for each population and FST for each population pair and for the trio of samples.

The average uncorrected heterozygosity within the three population groups for the HapMap data were 0.281, 0.247, and 0.268 for the Yoruban, Chinese, and European samples. The corresponding figures for the uncorrected Perlegen data are 0.251, 0.211, and 0.229 for the African American, Chinese, and European samples.

histograms are like this.

Pseudoautosomal regions gene nomenclature

http://www.genenames.org/genefamily/par.php