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Genome wide association studies seminar Prepared by Ms Varsha Gaitonde.
 

Genome wide association studies seminar Prepared by Ms Varsha Gaitonde.

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This ppt is related to the new era of association mapping,GWAP which is an emerging bud in mapping field.

This ppt is related to the new era of association mapping,GWAP which is an emerging bud in mapping field.

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  • An inefficient design, the one-stage design, genotypes all M SNPs on all available samples in a single stageTwo-stage designs substantially reduce the genotyping burdenIn a two-stage GWA study, all the SNPs are genotyped on a proportion of cases and controls. In stage 2, a proportion, pi markers, of the SNPs are followed up using the remaining samples.In replication-based analysis, the genotype data collected in stage 2 is viewed independently of the data collected in stage 1, allowing any significant stage 2 result to be deemed a replications.In joint analysis, the test statistics for stage 1 and 2 are combined to assess evidence
  • Risk measurement
  • Null hypo-fair coinAlternative- biased coin Binomial distribution Statistical regression
  • LD influenced by recombination, gene conversion, mutation, selection, etc.If causal variant is at A and we tested B, B would be associated with BThe first SNP perfectly “tags” second SNP
  • Nested association mappingHigh power ,high resolution,analysis of many alleles
  • "GWA studies have been very successful in allowing us to home in on the biologically relevant parts of the genome, but we need to build functional data sets in all human cell types to convert initial findings into biological mechanisms," said DrDeloukas. "This study is one such example and shows the power of integrating genomic and biological data."

Genome wide association studies seminar Prepared by Ms Varsha Gaitonde. Genome wide association studies seminar Prepared by Ms Varsha Gaitonde. Presentation Transcript

  • Seminar on By:Varsha Gaitonde ID:PALB2235 1
  •  Introduction  Terminologies  Comparison of AM v/s BM GWAS Introduction  Methodology  Challenges –Conducting GWAS Case studies Advantages Disadvantages Future of GWAS Revision Conclusion 2
  • • False negative: the declaration of an outcome as statistically non-significant, when the effect is actually genuine. • False positive: the declaration of an outcome as statistically significant, when there is no true effect. • Linkage: refers to coinheritance of different loci within a genetic distance on the chromosome. • Linkage equilibrium: LE is a random association of alleles at different loci and equals the product of allele frequencies within haplotypes. 3
  • • Linkage disequilibrium: LD is a non-random association of alleles at different loci, describing the condition with non-equal frequency of haplotypes in a population. • Minor allele Frequency(MAF):The frequency of the less common alleles of a polymorphic locus. Its value lies between 0 to 0.5,and can be vary between populations. • Odd ratio: Measurement of association that is commonly used in case control studies. Defined as odd of exposure to the susceptible genetic variant in case compared with that in controls. If OR significantly greater than 1,then the genetic variant is associated with a disease. 4
  • • It is the representation of information using special relationships • Mapping methods Linkage mapping: measures recombination between markers and the unknown gene (linkage) • Association mapping: measure correlation between marker alleles and allele in a population (linkage disequilibrium) 5
  • Bi-parental mapping Association mapping • No cross required, works with existing germplasm. • Phenotypic data can be Phenotypes to be collected. already available. Limited mapping resolution. • High resolution. Essentially 2 alleles are tested • More than 2 alleles are tested. Constraints to segregating loci • Many loci for a single trait are concurrently analyzed. between parental lines. • Comparatively low. High detection power • Experimental cross required. • • • • • 6
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  • • Aim to identify which regions (or SNPs) in the genome are associated with disease or certain phenotype. • Design: – Identify population structure – Select case subjects (those with disease) – Select control subjects (healthy) – Genotype a million SNPs for each subject – Determine which SNP is associated. • Encoded data • Ranking SNPs 8
  • Linkage vs Association Linkage Association • Successful study published in 2005,with investigating patients age related molecular degeneration. 1. Family-based Matching/ethnicity generally • 2.Prior to GWAS in 2000 unimportant Inheritance studies of 3. Few markers for genome coverage (300-400 linkage families. STRs) 4. Can be weak design • 5.HapMap2003 detection; poor Good for initial 6. for fine-mapping Powerful for rare variants 1. Families or unrelateds 2. Matching/ethnicity crucial 3. Many markers req for genome coverage (105 – 106 SNPs) Powerful design 4. 5. 6. Poor for initial detection; good for fine-mapping Powerful for common variants; rare variants generally impossible 9
  • 1. Human Genome Project  Good for consensus, not good for individual differences Sept 01 Feb 02 April 04 Oct 04 2. Identify genetic variants  Anonymous with respect to traits. April 1999 – Dec 01 3. Assay genetic variants  Verify polymorphisms, catalogue correlations amongst sites  Anonymous with respect to traits Oct 2002 - present 10
  • • Multi-country effort to identify, catalog common human genetic variants. • Developed to better understand and catalogue LD patterns across the genome in several populations. • Genotyped ~4 million SNPs on samples of African, east Asian, European ancestry. • All genotype data in a publicly available data base. • Can download the genotype data – Able to examine LD patterns across genome – Can estimate approximate coverage of a given SNP chip • Can represent 80-90% of common SNPs with ~300,000 tag SNPs for European or Asian samples ~500,000 tag SNPs for African samples 11
  • Linkage vs Association Linkage Association 1. Family-based 1. Families or unrelateds 2. Matching/ethnicity generally unimportant Few markers for genome coverage (300-400 STRs) Can be weak design 2. Matching/ethnicity crucial 3. Many markers req for genome coverage (105 – 106 SNPs) Powerful design Good for initial detection; poor for fine-mapping Powerful for rare variants 5. 3. 4. 5. 6. 4. 6. Poor for initial detection; good for fine-mapping Powerful for common variants; rare variants generally impossible 12
  • Linkage vs Association Linkage Association 1. Family-based 1. Families or unrelateds 2. Matching/ethnicity generally unimportant Few markers for genome coverage (300-400 STRs) Can be weak design 2. Matching/ethnicity crucial 3. Many markers req for genome coverage (105 – 106 SNPs) Powerful design Good for initial detection; poor for fine-mapping Powerful for rare variants 5. 3. 4. 5. 6. 4. 6. Poor for initial detection; good for fine-mapping Powerful for common variants; rare variants generally impossible 13
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  • Samples SNPs Joint analysis Replication-based analysis SNPs SNPs Stage 2 Samples Stage 1 Stage 2 Samples Stage 1 15 15
  • • Joint analysis has more power than replication. • p-value in Stage 1 must be liberal. • CaTs power calculator. • Here signals from an initial, First-stage GWAare used to define a subset of SNPsthat are retyped in additional second stage samples. • Lower cost—do not gain power. http://www.sph.umich.edu/csg/abecasis/CaTS/inde x.html 16
  • • Most common approach: look at each SNP one-at-a-time. • Possibly add in multi-marker information. • Further investigate / report top SNPs only. Or backwards replication…Most commonly trend test. • Log additive model, logistic regression. • Adjust for potential population stratification. 17
  • Calculate the odd ratio • If 2 events are considered • odds of A and B is OR = Odds(A)/Odds(B) = (A)/(~A) / (B)/(~B) • Symmetry in odds ratio OR = Odds(D|G=1)/Odds(D|G=0) = = Odds(G|D=1)/Odds(G|D=0). 18
  • • Significance? Chi- square test. Rank SNP by P-value. (Statistical test of association ) • Search for SNPs that deviate from the independence assumption. • Rank SNPs by p-values 19
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  • 22 (Abdurakhmonov and Abdukarimov, 2008)
  • • “Graphical overview of linkage disequilibrium” (GOLD) to depict the structure and pattern of LD. • “Trait Analysis by aSSociation, Evolution and Linkage” (TASSEL) and PowerMarker 23
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  • • Maize- (Zea mays spp mays )Studies conducted to investigate LD over a wide range of population and marker type. 25
  • The factors, which lead to an increase in LD, include • • • • • • • • Inbreeding, Small population size, Genetic isolation between lineages, Population subdivision, Low recombination rate, Population admixture, Natural and artificial selection, Balancing selection, etc. The factors, which lead to a decrease/disruption in LD, include • Outcrossing, • High recombination rate, • High mutation rate, etc. 26
  • Basic biology • Understand the makeup of molecular pathways. • Dissect the genetic component of molecular variation. • Genotype environmental interaction. Breeding • Mining of markers causal for phenotype. • to assist in breeding decisions. • Maximization of yield, pathogen resistance etc. Brown and Brown 2008 27
  • Genotype • Imputing of missing value. • Hidden Mankov models and related approaches. Beagle,IMPUTE • In GWAS based on full sequencing data some alleles may be rare or even private. Phenotype • Most parametric models are based on Gaussian assumptions. • Phenotypic residues are often non Gaussian. • Phenotypic transformation on suitable scale. • Use of prior knowledge. • eg.Growth rate , generation doubling time etc. • Variance stabiliztion. 29
  • Multiple hypothesis testing • In GWAS the number of statistical tests is commonly is on the order of 10⁶. • At significance level of 0.01we would expect 10,000 false positive. Thus individual p-value <0.01are not significant anymore. • Correction of multiple hypothesis testing is critical. Population structure • Confounding structure leads to false positive. Statistical power and resolution • Small samples, large number of hypothesis. • Increased power • Testing compound hypothesis. 30
  • • Compare the Quartiles of the empirical test statistic distribution to assume null distribution. • Sort test statistic. • Plot test statistic against (Y-axis) quartile of the theoretical null distribution. • If the plot is close to diagonal the distribution makeup. • Deviation from the diagonal indicates inflation or deflation of test statistics. • Repair the plot with HW equilibrium. 31
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  • 36 Gonçalo Abecasis
  • • Why in Arabidopsis? • Hermophodite.so large existence of spp. • Behaves as naturally existing inbred population • LD is more extensive. • False positive rates strongly differed between the traits . 37
  • • They considered 4 phenotypes for which the major loci were known. • Vernalization response locus-FRI. • 3 pathogen resistant loci-Rpm1,Rps2,Rps5. 38
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  • • Biparental and QTL approaches are not scalable to investigate genetic potential of 12000 accessions. • GWAS simultaneously screened large number of genetic accessions. • Genotype once and sequence repeatedly. • Took Global collection of 413 accessions of sativa. • Collected from 82 countries and designed 440000 oligonucleotides. Results • Correlation analysis between different phenotypes. • Correlation ranged from -0.41 to 0.9(seed width and length). 45
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  • Analysis of Naive and mixed model approaches 48
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  • • Smut (Sphacelotheca reiliana) study using Illumina maize SNP 50 array. • 45868 SNPs in the panel of 144 inbred lines. • Classified candidate genes as resistant genes, disease response genes and other genes. Outcome • 50K SNP offers highest array. • Chromosomal region and specific SNP that affect resistance level in commercial build up population. • Assessed the extent of LD in target population. • Identified genes or QTLs that significantly affect smut resistance. • Characterized those genes based on known function and colocation. 51
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  • • • • • • • Barley Lettuce Tomato Sorghum Wheat Foxtail millet 54
  • • Biological pathway of the trait does not have to be known. • Discovering novel candidate genes. • Encourages collaborative consortia. • Rules out specific genetic association. • Provides more robust data. • Identifies the mutations explaining few percent of phenotypic variant. 55
  • • Results need replication in independent samples in different population. • A large study of population is required, detects association not causation. • Identifies specific location not complete gene. • Focus on common variants and many associated variants are not causal. • Detect any variant(>5%) in a population. • Cost of each DNA sample and pooling them. • Unavailability of funding agencies. • Not predictive and explains less heritability. 56
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  • Candidate gene • Hypothesis-driven • Low-cost: small genotyping requirements • Multiple-testing less important – Possible many misses, fewer false positives 58
  • • The dropping genotyping costs. It involves whole genome. • Resequencing of all the individuals in a population, large structure variation such as copy number variation. • Eg.Resequencing of Arabidopsis lyrata. • In future this will help in RNA-seq data to include in e-QTL mapping in GWAS studies. • Population choice will no longer restricted to model organisms will slowly become more focused on the spp which are more relevant in answering biological questions. • The accuracy depends on 1 time genotyping and repeated phenotyping. 59
  • • All phenotype and genotype data should to be made public and be deposited in public databases. • As such file format and minimum information standards should to be established. • Priority to storage and dissemination of phenotypic and genotypic data. 60
  • Systems Biology Moving beyond Genomics 61
  • The more we find, the more we see, the more we come to learn. The more that we explore, the more we shall return.” Sir Tim Rice, Aida, 2000 62
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