Imputation. Genetics of Human Complex Traits

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Genetics of Human Complex Traits GWAS results Manhattan plot x-axis: chromosomal position y-axis: -log 10 (p-value), so p = 1 x 10-8 is plotted at y = 8 p = 5 x 10-8 is plotted at y = 7.

1 Genetics of Human Complex Traits GWAS results Manhattan plot x-axis: chromosomal position y-axis: -log 10 (p-value), so p = 1 x 10-8 is plotted at y = 8 p = 5 x 10-8 is plotted at y = 7.3 Advanced Genetics, Spring 2013 Human Genetics Series Thursday 4/04/13 Nancy L. Saccone, nlims@genetics.wustl.edu Agrawal A et al., Addiction Biol 2011 GWAS results Q-Q plot : Quantile-quantile plot Idea: Rank tested SNPs by association evidence; compare number of observed vs expected associations under the null at a given significance level GWAS results Q-Q plot : Quantile-quantile plot Helps detect systematic bias in data: Most datapoints t should be close to the y=x line. JAMA 299: (2008) Next steps from here (post-gwas)? Narrow down to "true" (biologically causal) variants in the associated region Determine how/why these variants alter disease risk How? Look across multiple diverse populations, leverage LD differences Bioinformatic prioritization, genotyping, sequencing to query the regions of association more thoroughly (1000 Genomes Project) Meta-analysis for larger, more powerful samples Functional follow-up Model organisms Method(s) for "in silico genotyping" - estimating genotype status at un-typed variants Benefits: Fine-mapping: Potential for improved detection of a signal at an imputed SNP Large-scale meta-analysis: Ease of combining i data/results across studies that have differing SNP coverage Cautions: Accuracy: Can be difficult to assess without genotyping 1

Green = genotyped, Red = not genotyped, Blue = imputed Your data: Reference data: Imputed Data: SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 Input data:

2 Green = genotyped, Red = not genotyped, Blue = imputed Your data: Reference data: Imputed Data: SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 SNP 1 SNP 2 SNP 3 SNP 4 SNP 5 Input data: Genotyped sample Selected "reference haplotype panel" (e.g Genomes) Typical output data Genotype probabilities Subject ID AA AG GG Measures of imputation confidence (not based on comparison with true genotype) Assessing imputation quality Gold standard is to compare with true genotypes It matters how the comparison is done a. Data for samples.? = untyped SNPs bt b. Testing typed dsnps How it works c. Phase each sample; model haplos as mosaic of those in reference d. Reference haplotypes e. Impute alleles for the samples (orange) f. Testing typed and imputed SNPs (adapted from Marchini & Howie, Nat Rev Genet 2010) Hidden Markov models(hmms) used (e.g. IMPUTE program, Marchini et al., 2007) Suppose have (unobserved/hidden) process that can be observed through another process that produces sequence of observable values. Goal is to characterize that process. N possible states for the system (unobserved), and transition probabilities between the states. Tasks: - Determine number of states (if not pre-specified - can be difficult) - Determine state transition probabilities. Example: suppose someone is behind a curtain, tossing coin(s). Don't know what kind of coins (fair or biased), how many coins (number of states). But you're able to observe the result of each toss: e.g. O 1, O 2, O 3, O 4, O 5,, O N, H, H, T, H, H,, T HMM example Some possible models: - 1 fair coin model: (2 states, one uniquely associated with H, one with T). NOT a hidden model (because of unique association between coin toss and H or T) 2

3 HMM example Some possible models: - 2 fair coins model: 2 states corresponding to 2 coins: in state 1, use coin 1; in state 2, use coin 2. (2 (unobserved) states, but each associated with 0.5 prob H, 0.5 prob T). An independent fair coin is used to decide which of the 2 coins to use at each trial. Now there is a non-unique, probabilistic bili association between toss and H or T outcome Model is hidden. HMM example - "2 biased coins model": In state 1, use coin 1 (P(H) = 0.75, P(T) = 0.25); in state 2, use coin 2 (P(H) = 0.25, P(T) = 0.75). But an independent fair coin is used to decide which of the other 2 coins is used at each trial. (Model is hidden: don't know which state (which biased coin) led to the observed H/T.) Figure from Rabiner and Juang, 1986 HMM example - Tasks: - Determine number of states (can be difficult) - Determine state transition probabilities. N possible states for the system (unobserved) the possible haplotypes from reference panel. (Note here, # of states is set by the ref panel.) K individuals in "your dataset" G = {G 1, G K }. Each G i corresponds to the set of genotypes G ij (some observed, some not), for ith person in your dataset. (j = 1, M, M= # markers). Want to model each individual's genotype probability vector Pr(G i H). Hidden states are sequence of pairs of the N known haplotypes in the set H = {H 1, H N }, which are being selected to form the genotype vector G i. Assessing imputation reliability/quality Genotype the variant to confirm genotype and test association. Concordance (but can be misleading for rare SNPs) Quality Score (IQS) (Lin P,, Rice JP, PLoS One, 2010): concordance adjusted for chance agreement ("kappa statistic" for rater agreement"). Useful for comparing with true genotype (when available) In absence of "true genotype": Could compare genotype probabilities and the "most likely genotype" e.g. compare 0.6, 0.2, 0.2 with and 0.97, 0.02, 0.01 with 1, 0, 0 1, 0, 0 etc. Caution: Concordance can be high "by chance" for rare SNPs Imputed genotype True genotype CC CG GG total CC CG GG total Suppose "imputation algorithm": Assign everyone 2 copies of the major allele (!) True allele frequency for G allele: 1/200 = = 0.5% Genotype Concordance: 99% (!) 3

4 Caution: Concordance can be high when IQS is low Imputed genotype Concordance: 0.73 IQS: 0.27 True genotype CC CG GG total CC CG GG total IQS takes into account chance agreement (e.g. for rare SNPs)/ Shown to filter out false positive association signals (Lin P et al., 2010) software packages IMPUTE2 (Howie, Marchini, et al.) Mach (Li, Abecasis et al.) n.html Beagle (Browning and Browning) BimBam (Stephens et al.) Thanks to Shelina Ramnarine Combining data through sharing/collaboration For example, meta-analysis and/or combined ("mega") analysis Benefits: Improved power Extends value of existing data (often costly to collect) Challenges: 1.Harmonizing phenotypes 2.Harmonizing genotypes: imputation helps with this Meta analysis: statistically combines summary statistics across multiple datasets Thus meta analysis can be applied to published data/results Collaborative meta analyses goes further: not just retrospective e literature e review, but carrying out new, coordinated analyses across multiple datasets Not limited to the ʺpublished analysesʺ for a given dataset Can include unpublished datasets "Collaborative de novo meta-analyses" Item level data retained by contributing sites. Summary results/statistics are shared, combined Often all sites follow an agreed upon ʺanalysis planʺ My group does even more: we write the analysis code and distribute, to ensure uniform analyses and ease of participation Targeted smoking meta-analysis (Saccone et al., PLoS Genetics 2010) 34 datasets, 17 sites rs on chr 15 genotyped in most but not all datasets Used proxy "tag" SNPs (rather than imputation): Locus Target SNP position (bp) Proxy SNP position (bp) LD (r 2 ) 1 rs rs rs rs rs

Heavy / light smoking at locus 1 (rs16969968) Requires "big science"

y-axis: -log(p-value), so p = 10-8 is plotted at y = 8 More and more

to make their data available through the Database of Genotypes and

5 Heavy / light smoking at locus 1 (rs ) Requires "big science" collaboration with great colleagues OR P-value Summary 1.33 ( ) 5.96 x Saccone et al., PLoS Genetics 2010 Recall Manhattan plot x-axis: chromosomal position y-axis: -log(p-value), so p = 10-8 is plotted at y = 8 More and more data are available by request All NIH funded GWAS studies are required to make their data available through the Database of Genotypes and Phenotypes (dbgap) Not just results also individual-level genotype/phenotype data. (Open-access: for non-sensitive data) Controlled-access: Data are available by request; access granted after panel review - e.g. dbgap DAC: Data Access Committee Tobacco and Genetics Consortium, Nat Genet 2010 dbgap homepage: Note the column labeled "Embargo Release" Note the column labeled "Embargo Release" 5

Old version: dbgap homepage: http://www.ncbi.nlm.nih.gov/sites/entrez?

analyses / manuscripts for publication before the embargo date This gives the original PI a proprietary period, often 6-12 months.

6 Old version: dbgap homepage: Ethical use of "available" or shared data NIH GWAS Policy for Data Sharing Investigators who request the data sign an agreement not to submit analyses / manuscripts for publication before the embargo date This gives the original PI a proprietary period, often 6-12 months. Important because data often are posted on dbgap at the same time it is available to the PI. Data Use Agreement also includes requirements for citing original study/funding. Remember there can still be benefits to collaborating directly with the data originators. 6

Human Genetics and Gene Mapping of Complex Traits

Human Genetics and Gene Mapping of Complex Traits Advanced Genetics, Spring 2015 Human Genetics Series Thursday 4/02/15 Nancy L. Saccone, nlims@genetics.wustl.edu ancestral chromosome present day chromosomes: