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Efficient Tag SNP Selection for Genome-Wide Association Studies

This joint work presents the Fast Tagger tool for rapid and memory-efficient tag SNP selection, crucial for genome-wide association studies. The work outlines previous methods, introduces the Fast Tagger algorithm, presents experimental results, and discusses tag SNP applications and future research directions. Fast Tagger outperforms existing algorithms by reducing memory consumption and runtime, enabling efficient analysis of extensive SNP datasets. Additionally, the tool's effectiveness in merging equivalent SNPs, skipping rules, and pruning redundant correlations is highlighted. The study's conclusions emphasize Fast Tagger's speed, memory efficiency, and scalability for large SNP datasets, making it a valuable tool for personalized medicine and breeding programs. Experiment results demonstrating the inference of extra SNPs from existing datasets further showcase the tool's practical application.

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Efficient Tag SNP Selection for Genome-Wide Association Studies

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  1. Fast Tag SNP Selection Wang Yue Joint work with Postdoc Guimei Liu and Prof Limsoon Wong

  2. Outline Some preliminary definitions Previous Work Our work- Fast Tagger Experiment Result Tag SNP Application Future Work

  3. SNP(Single-nucleotide polymorphism)

  4. Why research on SNP • Variation among human beings can affect how human develop certain diseases and respond to pathogens, chemicals, drugs, vaccines, and other agents eg:Researchers found that persons with the specific alterations (SNPs) have a 50% higher relative risk of developing glioblastoma, a type of Brain Cancer. • A promising area to realize the "Personalized medicine" • Important in crop and livestock breeding programs

  5. Tag SNP A tag SNP is a SNP in a region of the genome with high linkage disequilibrium Possible to identify genetic variation without genotyping every SNP in a chromosomal region. Tag SNPs are useful in whole-genome SNP association studies in which hundreds of thousands of SNPs across the entire genome are genotyped.

  6. Tag SNP-linkage disequilibrium • In population genetics, linkage disequilibrium is the non-random association of alleles at two or more loci, not necessarily on the same chromosome. • Usually use r2to measure • where P(XY), P(Xy), P(xY), P(xy) are freq of possible alleles; P(X) =P(XY)+P(Xy), P(x)=P(xY)+P(xy),

  7. Tag SNP selection Given dataset, we can find a huge number of tag snp relation among SNPs as long as we can enumerate the possible r2 value between SNPs The reality is We desire to select a smallest set of high quality SNPs which can tag the rest SNPs, in other words, if we understand this smallest set of SNPs, we can refer the rest based on the r2 value.

  8. Tag SNP Selection-- More formal description • Given a set S of SNPs, find the smallest set of tag SNPs Stagsuch that for every SNPj ∈ S − Stag, thereis at least one SNP set Sj⊆ Stag such that • – r2 (Sj, SNPj) ≥ min_r2 • – |Sj| ≤ max_size • – Distance between every pair of SNPs in Sj ∪ {SNPj} is no larger than max_dist

  9. Previous Work • Step 1: Correlations between SNPs within certaindistance are calculated • Step 2: Find smallest setof tag SNPs using correlations calculated inStep 1 • Most algo use greedyapproach to find a near optimal set of tag SNPs inStep 2 Earlier tag SNP selectionmethods rely on pairwisecorrelations • MultiTag & MMTagger findmultimarker rules– {SNP1, SNP2, SNP3} ->SNPx • Cannot handle >100k SNP • MultiTag takes hundreds of hours for 30k SNP • MMTagger takes hours & 1GB memory for 30k SNP

  10. FastTagger • Similar two major steps • first step: borrow the typical data mining techniques to mine tagging rules based on r2 value • Second step：Use a greedy algorithm to select the small set of tag SNPs from the tagging rules generated in first step

  11. Why beat the previous work? • Previous work like MMtagger will generate a lot of redundant tagging relations • Ours can avoid this by • 1. Merge nearby equivalent SNPs • 2. Prune redundant correlation rules • 3. Skip the rules if RHS has been covered many times • 4. If total size of rules exceeds memory, divide • chromosome into blocks, and then find tag SNPs • within each block

  12. Experiment Setting Japanese and Han in HapMap release 21 – 45 unrelated individuals – 6 chromosomes

  13. Experiment Result—running time and # tag SNPs Comparison with state-of-the-art work: MMTagger

  14. Experiment Result-memory consumption MMTagger consumes much more memory Failed on large chromosomes when max_size = 3 Step 2 of FastTagger consumes much more memory than Step 1 because this step needs to store rules generated in the memory

  15. Effectiveness ofMerging Nearby Equiv SNPs # of rules, tag SNPs, and runtime are significantly reduced

  16. Effectiveness of Skipping Rules Memory usage and runtime are significantly reduced, while # of tag SNPs is marginally increased

  17. Effectiveness ofPruning Redundant Rules Memory usage and # rules are significantly reduced

  18. Conclusions • Compared to existing genome-wide tag SNP selection algorithm using multi-marker correlations, • FastTagger is – Many times faster – Consumes much less memory – Can work on chromosomes with > 100k SNPs • Merging equiv SNPs together is most effective technique in reducing running time and memory consumption

  19. Tag SNP Application • Using the tagging rules generated by our data mining technique to infer extra SNPs from existing SNP list • We obtained two SNP list from two major SNP chip company: • IIiuminia ,1145784 SNPs • Affimetric,927654 SNPs • How many extra SNPs we can infer?

  20. Experiment Setting • Our rules are generated from Data set Japanese and Han in HapMap release 21,contrary to previous experiment, we use 22 chromosomes • In this experiment, two factors will determine how many extra SNPs we can infer 1. r2 threshold: empirical set 0.8, we set 0.80, 0.85, 0.90, 0.95 2. Rule size: we set 1,2

  21. r2 : 0.80 r2 : 0.85 r2 : 0.90 r2 : 0.95

  22. Future Work • Test the accuracy of our selected SNPs with state-of-the-art work • Support adaptive user requirement to select the SNPs, such as I have only 1 million, just give me 1000 most informative SNPs • How the division of the chromosomes influence the # of tag SNPs • More to explore

  23. Many thanks to • My supervisor : Prof Limsoon Wong • My senior: Guimei Liu • Some slides are adapted from Prof Wong's notes and Wikipedia • Thank you for listening • Q&A

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