linkstream2 microblog

Pooled association tests for rare variants in exon-resequencing http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073 Simulation shows advantage of mult. rarity thresholds

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ,
Sunyaev SR. Pooled association tests for rare variants in
exon-resequencing studies. American Journal of Human Genetics (2010)
86: 832-838.

SUMMARY

Multiple studies indicate strong association between rare variants and
resulting phenotype. This paper describes a population-genetics
simulation framework to study the influence of variant allele
frequency on the corresponding phenotype. In a prior study, causal
relationship between variants and phenotype was resolved by performing
association test on set of variants having allele frequency below a
fixed threshold. However, here it is observed that simulation
frameworks based on a variable allele frequency threshold provide
higher accuracy in association test compared to the fixed allele
frequency model. In addition, inclusion of predicted functional
effects of variants (Polyphen-2 scores) increases the accuracy of the
variable frequency threshold model. Overall, this paper describes a novel methodology, which can be
used to explore the association between rare variants and various
diseases.

Server for predicting damaging missense #mutations
http://www.nature.com/nmeth/journal/v7/n4/full/nmeth0410-248.html Polyphen2 uses both structure & sequence (eg ASA & conservation)

http://www.ncbi.nlm.nih.gov/pubmed/20354512

Polyphen2 includes both structural and sequence features to predict the effect of nonsynonymous substitutions on protein function. Similar to many other methods, Polyphen2 uses evolutionary conservation as one of the features to identify functionally important residues. Integration of 3D-structure, membrane-specific features (PHAT matrix for TM regions) and other features such as protein-domain and active-site are the strengths of Polyphen2 compared to other sequence-based software making it a good tool for prediction.

#Exome sequencing & #genetic basis of complex traits
http://www.nature.com/ng/journal/v44/n6/full/ng.2303.html Key pt: amt of rare variants exceeds that from neutral model

Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, McLaren PJ, Gupta N, Sklar P, Sullivan PF, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Lehner T, Shugart YY, Price AL, de Bakker PI, Purcell SM, Sunyaev SR. Exome sequencing and the genetic basis of complex traits. Nature Genetics (2012) 44: 623-630

SUMMARY

This article serves as part review, and part research article, focusing on using exome sequencing to detect associations between variants and complex traits.

An important fact they point out, with a wide range of implications for studying disease, is that the number of rare variants exceeds the number predicted by the neutral model. Figure 1 illustrates nicely this excess of rare variants.

I agree with their statement that the majority of these mutations are not “neutral”. They attribute this excess to population expansion or purifying selection, but a plausible explanation that explains this excess, which is found in all organisms regardless of demographic history, is linked selection.

The authors compare statistics derived before and after filtering exome sequencing data of 438 individuals (HIV and Scizophrenia data-sets), illustrating the importance of filtering in obtaining high quality calls. WGS (CGI data on 37 individuals) was used as a benchmark for the number of called SNP counts of different categories (silent, missense, nonsense).

They then proceed to analyze the affect of population stratification on significance values by combining different ratios of individuals from the European-American HIV cohort and the Swedish schizophrenia cohort. (Theory predicts that older populations should have more rare variants because recombination has had more time to break up linkage blocks, and because newer populations have most likely gone through homogenizing bottlenecks.) They find that calculating p-values using a permutation test provides fewer type I errors (false positives), and that this technique can competently deal with population
stratification when conducting association studies.