Northfield Undergraduate Mathematics Symposium October 7 2014 A method for combining familybased rare variant tests of association Genetic information stored in DNA Coded as a sequence of A denines ID: 1047680
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1. Kaitlyn CookCarleton CollegeNorthfield Undergraduate Mathematics SymposiumOctober 7, 2014A method for combining family-based rare variant tests of association
2. Genetic information stored in DNACoded as a sequence of Adenines, Thymines, Guanines, and CytosinesOrganized into chromosomesGene…a collection of nucleotidesAllele…a version of a geneAn Introduction to GeneticsA A T C G G A T T C T G G A C C C C G C G C G A T TA A T C G G A T T C T G G A C A C C T C G C G A T TGene 1Gene 2
3. Genetic information stored in DNACoded as a sequence of Adenines, Thymines, Guanines, and CytosinesOrganized into chromosomesHomozygous…having two copies of the same alleleHeterozygous…having copies of two different allelesAn Introduction to GeneticsA A T C G G A T T C T G G A C C C C G C G C G A T TA A T C G G A T T C T G G A C A C C T C G C G A T THomozygousHeterozygous
4. Genetic information stored in DNACoded as a sequence of Adenines, Thymines, Guanines, and CytosinesOrganized into chromosomesSingle Nucleotide Polymorphism (SNP)Minor alleleSingle Nucleotide Variant (SNV)An Introduction to GeneticsA A T C G G A T T C T G G A C C C C G C G C G A T TA A T C G G A T T C T G G A C A C C T C G C G A T T
5. Possible that these rare variants are associated with complex diseasesEither risk-increasing or risk-reducingWant to be able to detect these sorts of associationsOne problem: POWERTwo solutions:Gene-based tests of associationUse family-based study designsRare variants
6. Rather than analyzing individual SNVs, we can collapse them on a gene levelInstead of testing whether one SNV is associated with a disease phenotype, we test whether at least one SNV in a given gene is associatedAggregates the signal and increases powerTwo ways of collapsingBurden testsVariance component testsGene-based association tests
7. Tests perform optimally in different circumstancesBurden tests: all the SNVs are risk-increasing or risk-reducingVariance component tests: mixture of risk-increasing or risk-reducingPower depends on type of test and genetic architectureDrawback: picking the “right” test requires knowing genetic architecture beforehandPicking the “wrong” test could lead to substantial drop in powerGene-based association tests
8. Recruit families into the studyGenotype all possible members of each familyConstruct pedigreesGenetic inheritance means that rare variants are aggregated in the familiesIncreased prevalence means increased powerFamily-based study design
9. If different tests perform better under different scenarios, can we somehow combine information from these tests to improve power?Develop a flexible method that:Can combine any number and type of family-based tests of association Increase statistical power and robustnessMaintains type I errorOur focus
10. Generate a vector of test statistics, Q(0) = (Q1,…, Qk)Permute the phenotype m times, finding Q(1), …, Q(m)Convert test statistic vectors Q(i) into p-value vectors p(i) by determining each test statistic’s relative rank among the m permutations Combine p-values within each permutation into a single summary statistic S(i)Compute the significance level of S(0) by finding the percentage of S(i) which are greater than S(0), out of m.Combination method
11. Typical permutation approaches do not work in family settingFit the data to a random effects modelFixed effects for covariatesRandom effects for kinshipPermuted the residualsPermutation strategy
12. Genetic Analysis Workshop 19Human sequence dataSequence information for 959 individuals in 20 familiesPhenotype of interest: Systolic and diastolic blood pressureReal and simulated phenotypes provided200 replicates of the simulated phenotypeUsed to calculate power/type I errorData used
13. Statistical tests used
14. Type I error conserved across both individual and combined family-based testsPower was generally low for all tests usedCombined test had empirical power in the same range as the individual testsChoice of variant weighting system had substantial impact on empirical powerCombined test more robust to differences in the characteristics of individual genesResults
15. resultsWarm colors: SKAT weightsCool colors: WSS weightsBlack: combined test approach
16. Working to determine the optimal type or number of tests to include in the combined test statisticReducing correlation between the tests being combinedIncorporation of further biological information into the test statisticNext steps
17. NIH/NHGRI: R15HG006915NSF/MCB: 1330813Derkach A, Lawless J, Sun L (2012) Robust and Powerful Tests of Rare Variants Using Fisher’s Method to Combine Evidence of Association From Two or More Complementary Tests. Genet Epidemiol 00:1-12.Greco B, Hainline A, Liu K, Zawistowski M, Tintle N General approaches for combining multiple rare variant association tests provide improved power across a wider range of genetic architecture (in progress)Acknowledgements & References