Encode variation analysis

Analysis goals Quantify genetic variation in ENCODE regions Detect selective constraint in ENCODE features Develop rules for interpretation of functional variation Motivate experiments to test functional variation

Data Encode SNPs (HapMap resequencing) 5kB HapMap SNPs DIPs Gene expression variation

Metrics of variation Derived allele frequency spectrum (Manolis) Diversity/Het (Ewan) SNP density (Ewan, others) DIP density (Jim, Taane) LD/Recombination (Daryl/Oxford) Regions of contiguous DNA without variation (Manolis) Accelerated (positively selected?) regions (Manolis) Standard tests of neutrality McDonald Kreitman/Tajima’s D etc (Mike, others) Other non-parametric tests of selection (Andy) Tagging (Paul)

Analysis plans Analysis wrt to genomic features Calculate variability in a large number of genomic features with all metrics Correlate variability metrics with “intensity” of feature (e.g. levels conservation with levels of variability) Variation, alternative spicing and expression Distance effects from genomic features Association of gene expression with SNPs (some is in UCSC and some will be provided by Manolis at the workshop) Analysis independent of genomic features (in principle) Tag SNPs and comparison of resequencing data to 5 Kb map. Here it will be a good idea to see how the 5 Kb map captures variation within genomic elements. If we really aim to capture variation mainly in functional genomic elements (e.g. known regulatory regions, or nonsym SNPs) how can we modify the tag algorithms? General description of levels of variation wrt to the functional content of the 44 ENCODE regions

av2pq/SNP av2pq/pos #snps Promoters : Region Rnd2 : Completely Rnd: Exons : RRnd Exons : Overall : Diversity in features Ewan Birney

Derived allele frequency spectrum CNS intersection P = 0.003

Derived allele frequency spectrum Transfrags union P = 0.204

Taane Clark Heterozygosity

Indels

Regions accelerated in humans

selective constrains differ for genes expressed in different tissues Nuria Lopez

Genes expressed in more tissues have more selective constrains (lower dN)

Tagging ENCODE is near-complete inventory of common (MAF≥5%) sites How well do tag SNPs picked from thinned versions of ENCODE (to mimic ascertainment of Phase I and II) capture: –all common variants –functional sites Paul de Baker

Coverage of common variants by tags picked from simulated Phase I and II HapMap