Measurable Analysis in Case-Control examines Summer International Workshop Aug, 09, Beijing Liu Tian Genome Institute of Singapore email@example.comSlide 2
Outline Introduction Basic Statistical Methods of Case-control Study GWAS A Novel Epistasis-testing ProcedureSlide 3
Aim of Genetic Studies Dramatic variety do exist inside a same zest Almost every natural wonder includes a hereditary segment There is dependably a sharp requirement for us to look for the hereditary variety identifies with complex qualities.Slide 4
Different Design Strategies Intervention considers Clinic trials Observational reviews Case-control thinks about Cohort contemplatesSlide 5
uncovered non-uncovered populace Disease +/ - Cohort Studies An accomplice study is a review where a gathering of people are taken after. Accomplice studies can be either imminent or reviewSlide 6
Case-Control Studies Case-control studies are utilized to distinguish components that may add to a therapeutic condition by looking at subjects who have that condition (the \'cases\') with patients who don\'t have the condition however are generally comparable (the \'controls\') Case-control studies are review and non-randomizedSlide 7
uncovered non-uncovered non uncovered populace Case-Control Studies Disease - Disease +Slide 8
Selection of Cases Population-based cases: incorporate all subjects or an irregular example of all subjects with the illness at a solitary point or amid a given timeframe in the characterized populace. Healing center based cases: All patients in a clinic office at a given timeSlide 9
Selection of Controls Principles of Control Selection: Study base: Controls can be utilized to describe the appropriation of presentation Comparable-precision: Equal dependability in the data acquired from cases and controls (to keep away from methodical misclassification) Overcome frustrating: Elimination of bewildering through control determination ( coordinating or stratified testing)Slide 10
Selection of Controls General populace controls: registries, family units, phone examining exorbitant and tedious review inclination inevitably high non-reaction rate Hospitalized controls: Patients at an indistinguishable doctor\'s facility from the cases Easy to recognize; less review predisposition; higher reaction rateSlide 11
Case-Control Studies versus Associate Studies Cohort concentrate Rare presentation Examine various impacts of a solitary introduction Minimizes inclination in the in presentation assurance Direct estimations of rate of the infection Case-control concentrate Quick, economical Well-suited to the assessment of maladies with long dormancy period Rare illnesses Examine different etiologic variables for a solitary ailmentSlide 12
Case-Control Studies versus Companion Studies Case-control concentrate Not uncommon introduction Incidence rates can\'t be assessed unless the review is populace based review, non-randomized nature restrains the conclusions that can be drawn from them. Associate review Not uncommon sicknesses Prospective: Expensive and tedious Retrospective: in satisfactory records Validity can be influenced by misfortunes to developmentSlide 13
test id case/control genotypes Data Structure of Case-control thinks aboutSlide 14
Outline Introduction Basic Statistical Methods of Case-control Study GWAS A Novel Epistasis-testing ProcedureSlide 15
Population-Based Case-Control Study Individuals are irrelevant To test if marker genotypes convey distinctively between the cases and controls By contrasting inside cases and controls, we recognize those hereditary variables corresponded with a pre-characterized phenotypeSlide 16
Coding Genotypes For one marker with two alleles, there can be three conceivable genotypes:Slide 17
Genetic Models and Underlining Hypotheses Genotypic Model Hypothesis: every one of the 3 unique genotypes have diverse impacts Genotypic esteem is the normal phenotypic estimation of a specific genotype AA versus Aa versus aaSlide 18
Genetic Models and Underlining Hypotheses Dominant Model Hypothesis: the hereditary impacts of AA and Aa are the same (expecting An is the minor allele) AA and Aa versus aaSlide 19
Genetic Models and Underlining Hypotheses Recessive Model Hypothesis: the hereditary impacts of Aa and aa are the same (An is the minor allele) AA versus Aa and aaSlide 20
Genetic Models and Underlining Hypotheses Allelic Model Hypothesis: the hereditary impacts of allele An and allele an are distinctive A versus aSlide 21
Pearson\'s Chi-squared Test Genotypic Model: Null Hypothesis: Independence df = 2Slide 22
Pearson\'s Chi-squared Test Dominant Model: Null Hypothesis: Independence df = 1Slide 23
Pearson\'s Chi-squared Test Recessive Model: Null Hypothesis: Independence df = 1Slide 24
Pearson\'s Chi-squared Test Allelic Model: Null Hypothesis: Independence df = 1Slide 25
Test Statistic Chi-squared Test Statistic: O is the watched cell numbers E is the normal cell checks, under invalid speculation of freedomSlide 26
Example The accompanying table condense the genotype tallies of marker M : Different tests can be performed: - Allelic test - Dominant quality activity - Recessive quality activity - Genotypic testSlide 27
Example ( Dominant Gene Action ) Using R: dominant_table <- matrix(c(80,90,20,10), ncol = 2) print(dominant_table ) chisq.test(dominant_table ,correct=FALSE)Slide 28
Example ( Recessive Gene Action ) Using R: recessive_table <- matrix(c(36,18,164,182), ncol = 2) print(recessive_table) chisq.test(recessive_table,correct=FALSE)Slide 29
Example (Genotypic Test) Using R: genotypic_table <- matrix(c(36,18,100,84,64,98), ncol = 3) print(genotypic_table) chisq.test(genotypic_table,correct=FALSE)Slide 30
Example (Allelic Test) Using R: allelic_table <- matrix(c(172,120,228,280), ncol = 2) print(allelic _table) chisq.test(allelic_table,correct=FALSE)Slide 31
Logistic Regression Analysis A General Model : Where: p infection is the likelihood that an individual has a specific sickness. β 0 is the block β 1 , β 2 … β J are the impacts of hereditary components X 1 , X 2 … X J are the spurious factors of hereditary elementsSlide 32
Logistic Regression Analysis Logistic relapse portrays the relationship between a dichotomous reaction variable and an arrangement of informative factors. Logit model is the main model under which β , the impact parameter, can be assessed in review considers as same as in imminent reviews. On the off chance that the examining rate for cases is 10 times that for controls, the catch evaluated is log(10) =2.3 than the one assessed with a planned review.Slide 33
Inference and Interpretation Significant test concentrate on : Estimator is the evaluated chances proportion for hereditary element i . The indication of figures out if is expanding or diminishing when the impact of hereditary component i exists.Slide 34
An Example of R yieldSlide 35
Other Options Fisher\'s Exact Test: When test size is little, the asymptotic guess of invalid appropriation is no more drawn out legitimate. By playing out Fisher\'s correct test, correct centrality of the deviation from an invalid theory can be ascertained. For a 2 by 2 table, the correct p-esteem can be figured as:Slide 36
Other Options Cochram-Armitage Trend Test - favorable position of the Cochran-Armitage test is that it doesn\'t expect Hardy-Weinberg harmony - Typically used to test a 2 × k possibility table, when the impacts of AA, Aa, and aa are thought to be requested. - In extensive affiliation contemplates, the added substance (or codominant) rendition of the test is frequently utilized.Slide 37
Outline Introduction Basic Statistical Methods of Case-control Study GWAS A Novel Epistasis-testing ProcedureSlide 38
expansive Association Study In hereditary the study of disease transmission, an all inclusive affiliation consider (GWAS) - otherwise called entire genome affiliation think about (WGA contemplate) - is an examination of hereditary variety over a given genome, intended to distinguish hereditary relationship with discernible characteristics. In human reviews, this may incorporate qualities, for example, circulatory strain or weight, or why a few people get an infection or condition. From: http://en.wikipedia.orgSlide 39
extensive Association Study Technology makes it practical - Affymetrix: 500K; 1M chip arrives before the actual arranged time 2007. (Arbitrarily appropriated) - Illumina: 550K chip costs (quality based) Requires little on test, Case-control information, case-guardians trio information are sufficient. Useful for direct impact sizes ( chances proportion < 1.5). Especially helpful in finding hereditary varieties that add to regular, complex illnesses.Slide 40
expansive Association StudySlide 41
What Is A SNP? TTCAGTCAGATCC T AGCCC Chromosome 1 AAGTCAGTCTAGG A TCGGG TTCAGTCAGATCC C AGCCC Chromosome 2 AAGTCAGTCTAGG G TCGGG Single Nucleotide PolymorphismSlide 42
Handling GWAS Storing and changing over a lot of genotype information Quality control Generating introductory affiliation investigation Specialized examinationSlide 43
Quality Control Of SNPs Exclude SNPs that disappointment the Hardy-Weinberg test - Expected extents of genotypes are not reliable with watched allele recurrence - HWE p-esteem < 10 - 4 to 10 - 6 Genotyping achievement rate < 95% Differential missingness in cases and controlsSlide 44
Quality Control Of Samples Poor quality examples - Sample genotype achievement rate < 95 to 97.5% - Greater extent of heterozygous genotypes than anticipated Related people (if autonomous specimens) - Based on combine insightful correlations of closeness of genotypes Samples with miss indicated sexSlide 45
Genetic Stratification Assess populace structure Adjust both phenotypes and genotypes for conceivable stratification utilizing - main part examination (Price\'s strategy) - bunch investigations (Plink) Genomic Control .:t
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