Update on the Genomics Data in the Health and Re4rement Study. Sharon Kardia Jennifer A. Smith University of Michigan April 2013

Transcription

1 Update on the Genomics Data in the Health and Re4rement Study Sharon Kardia Jennifer A. Smith University of Michigan April 2013

2 Genetic variation in SNPs (Single Nucleotide Polymorphisms) ATTGCAATCCGTGG...ATCGAGCCA.TACGATTGCACGCCG ATTGCAAGCCGTGG...ATCTAGCCA.TACGATTGCAAGCCG ATTGCAAGCCGTGG...ATCTAGCCA TACGATTGCAAGCCG ATTGCAATCCGTGG...ATCGAGCCA.TACGATTGCACGCCG ATTGCAAGCCGTGG...ATCTAGCCA.TACGATTGCAAGCCG

3 Genotypes are called with varying uncertainty Intensity of Allele A Intensity of Allele G

4 Two easy ways dealing with uncertain genotypes 1. Genotype Calling: Choose the most likely genotype and con4nue as if it is true (p 11 =10%, p 12 =20% p 22 =70% => G=2) 2. Mean genotype = Dosage Use the weighted average genotype (p 11 =10%, p 12 =20% p 22 =70% => G=1.6)

5 AFer Data Cleaning: Imputa4on HapMap Consor4um Reference (Completed) Used in first wave of GWAS Imputed ~ 2.4 million SNPs Only completed on whites and blacks Not posted 1000 Genomes Reference (Completed) Used in second wave of GWAS Imputed ~22 million SNPs Posted to dbgap in August

6 Different Genotyping Platforms measure different SNPs

7 Linkage Disequilibrium (LD) is the correla4on among muta4ons across SNPs SNP D n LD Markers close together on chromosomes are often transmitted together, creating a correlation between the mutation. LD also arises when populations mix (admixture).

8 Basic Concepts! Parent 1! Parent 2! A " "B "! a " "b! X! A " "B "! a " "b! A B! A B! a b! a b! OR! A b! a B! A B! A B! a b! A B! A B! a b! a B! A B! A b! A b! etc! High LD -> No Recombination! (r 2 = 1) SNP1 tags SNP2! Low LD -> Recombination! Many possibilities!

9 Key Terms! LD (linkage disequilibrium): For a pair of SNP alleles, it s a measure of deviation from random association. Measured by D, r 2 Phased haplotypes: Estimated distribution of SNP alleles in a genomic region on a chromosome. We have a pair of each chromosomes so we have pairs of haplotypes! Tag SNPs: Minimum set of SNPs needed to identify a haplotype. High LD (e.g. r 2 >0.8) indicates two SNPs are nearly redundant, so each one acts as a tag for the other.!

10 HapMap Project! Phase 1! Phase 2*! Phase 3! Samples & POP panels! Genotyping centers! Unique QC+ SNPs! 269 samples! (4 panels)! HapMap International Consortium! Reference! Nature (2005) 437:p1299! 270 samples! (4 panels)! Perlegen! 1.1 M! 3.8 M! (phase I+II)! Nature (2007) 449:p851 1,115 samples! (11 panels)! Broad & Sanger! 1.6 M (Affy 6.0 & Illumina 1M)! Draft Rel. 1! (May 2008)! *This is the version that most gene4cs consor4a are using

11 Haplotype Blocks are mapped across the genome (for each ethnicity)

12 Figure 2. A schema4c of SNP types as defined in the IMPUTE2 imputa4on algorithm. Each individual is represented by a unique color in the horizontal bar(s), and alternate alleles at each SNP are represented as A and B. Sec4on (A) represents phased reference haplotypes, where two samples (4 phased chromosomes) are shown. Sec4on (B) represents three study samples with SNP genotype calls, as would be observed in GWAS array experiment. Sec4on (C) iden4fies the SNP type of each posi4on shown. Type 2 SNPs have data in both the reference and the study samples: posi4ons 1, 4, 6, 8, and 11. Type 0 SNPs have data in the reference but not in the study samples: posi4ons 3, 5, 9 10, and 12. Thus, data at type 2 SNPs (imputa4on basis) are used to impute type 0 SNPs (imputa4on target) in the study samples. Type 3 SNPs are those in study samples but not in the reference; ul4mately, these SNPs are extraneous to the imputa4on, which is why they are shown in white text. This figure is a based off of IMPUTE2 background documenta4on (see Web Resources).

13 Observed genotypes Study Sample HapMap Gonçalo Abecasis

14 Iden<fy match among reference Gonçalo Abecasis

15 Phase chromosomes, impute missing genotypes Gonçalo Abecasis

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18 One more tricky piece: Haplotypes cross over (recombine)

19 Hidden Markov Model Hidden State S m : The pair of contributing reference haplotypes at marker m Data G m : Observed genotypes at marker m Goal: Infer S m

20 Algorithm Update one individual at a time: Construct haplotypes that match observed genotypes, from a pool of reference haplotypes. Forward: Calculate, cumulatively until the last marker, forward probabilities for observed genotypes and haplotype affiliation state S m Backward: Sample haplotype affiliation states (i.e., construct mosaic haplotypes) probabilistically according to forward probabilities and transition probabilities.

21 1000 Genomes Imputa4on Strategy Impute en4re sample Pre phase (es4mate haplotypes) Impute SNPs with > 4 copies of minor allele in any of the following HapMap racial/ethnic groups: African (AFR) Admixed American (AMR) East Asian (ASN) European (EUR)

22 The 1000 Genomes Reference sample Full Popula9on Name Abbrevia9on Number of Samples African Ancestry in Southwest US ASW 61 Luhya in Webuye, Kenya LWK 97 Yoruba in Ibadan, Nigeria YRI 88 Total African ancestry 246 Colombian in Medellin, Colombia CLM 60 Mexican Ancestry in Los Angeles, CA MXL 66 Puerto Rican in Puerto Rico PUR 55 Total American ancestry 181 Han Chinese in Beijing, China CHB 97 Han Chinese South, China CHS 100 Japanese in Tokyo, Japan JPT 89 Total Asian ancestry 286 Utah residents (CEPH) with Northern and Western European ancestry CEU 85 Toscani in Italia TSI 98 Bri4sh in England and Scotland GBR 89 Finnish in Finland FIN 93 Iberian popula4ons in Spain IBS 14 Total European ancestry 379 An overview of the 1,092 samples in the 1000 Genomes Project worldwide reference panel (phase I integrated variant set v3, March 2012), which was used to impute all study par4cipants. Each popula4on was assigned to one of four con4nental groupings: African (AFR), American (AMR), Asian (ASN), and European (EUR). All haplotypes in the phased reference panel are for unrelated, founder individuals only. This table is based on reference panel data downloaded from IMPUTE2 and the sample summary provided by the Project (see Web resources).

23 1000 Genomes Imputa4on Chromosome Study SNPs Imputa9on basis Imputa9on Output 1 171, ,997 1,639, , ,933 1,781, , ,934 1,501, , ,660 1,517, , ,185 1,378, , ,771 1,348, , ,601 1,228, , ,601 1,188, ,854 91, , , ,636 1,040, , ,929 1,038, ,377 97,955 1,006, ,080 72, , ,602 67, , ,755 63, , ,862 68, , ,966 59, , ,189 60, , ,210 42, , ,871 51, , ,884 28, , ,062 30, ,543 X 43,193 40, ,930 Totals 2,195,306 2,065,320 21,632,048 Study SNPs passing pre imputa4on filters (IMPUTE2 SNP types 2 and 3). Study SNPs passing pre imputa4on filters and overlapping with the reference panel (type 2). Imputa4on output is the sum of imputa4on basis (type 2) and imputa4on target (type 0) SNPs. Type 0 SNPs have been restricted to those with at least 4 copies of the minor allele in AFR, AMR, ASN, or EUR reference samples.

24 Quality control: Predic4on of Known Genotypes Minor Allele Frequency (in study samples) Number of SNPs Mean (Median) Overall Concordance Mean (Median) empirical dosage r2 < 0.1 1,059, (0.998) (0.919) 0.1 1,005, (0.994) (0.994) Quality metrics for all masked SNPs, dichotomized into groups of MAF < 0.1 vs. MAF 0.1. The second column shows the number of SNPs in each MAF group. Mean and median values are presented for overall genotype concordance and empirical dosage r 2 (in IMPUTE2 metrics files, labeled as concord_type0 and r2_type0, respec4vely). No info threshold has been applied here, such that all masked and imputed SNPs in each MAF category are included in these averages.

25 B ) Figure 3. Summaries of quality metrics at all imputed SNPs. Panel A shows the distribu4on of the info quality metric, with a dashed line indica4ng a poten4al 0.3 threshold value. Panel B is the distribu4on of certainty, the average certainty of best guess genotypes. Panel C summarizes the rela4onship between the info score and MAF. The secondary axis indicates the count of SNPs in each MAF bin (0.01 intervals).

26 Figure 4. A comparison of imputa4on quality metrics by chromosome for all imputed SNPs, info in panel A and certainty in panel B. Outlier values are not displayed in these box plots. On the x axis, 23 denotes the X chromosome. A) B)

27 Figure 5. Quality metrics for all masked SNPs, grouped into MAF bins at 0.01 intervals. Panel (A) shows the number of SNPs per MAF bin and, on the secondary y axis, the frac4on of SNPs in the bin passing an info filter threshold of 0.8. Panel (B) plots the average empirical dosage r2 metric per MAF bin, both before and afer filtering on the info score (black and gray data series, respec4vely). Similarly, panel (C) is the concordance between the observed and the most likely imputed genotype at masked SNPs within each MAF bin, with and without the info filter.

28 Apo E Imputa4on

29 Descrip9on Whites African Americans Hispanics Gene SNP Is the SNP genotyped on the Omni2.5? Call rate on Omni2.5 (from CIDR) APOE rs429358, rs7412 rs No rs7412 Yes rs N/A rs7412 0% from CIDR, 90% afer re clustering Imputa9on reference database (build and website) 1000 Genomes reference panels generated at University of Michigan (Interim Phase I, data freeze, haplotypes) hup:// PhaseI Interim.html Imputa9on reference panel The EUR reference panel (87 CEU +98 TSI +89 GBR +93 FIN +14 IBS) The EUR + AFR (88 YRI + 97 LWK +61 ASW ) reference panel s The EUR + AMR (60 CLM + 66 MXL + 55 PUR ) reference panels

30 Descrip9on Whites African Americans Hispanics Pre imputa9on quality control Region and number of SNPs in imputa9on Imputa9on program (including sepngs) Number of people included in imputa9on Imputa9on quality (Rsq) Exclude SNPs with MAF<0.01 or HWE< SNPs upstream and 1000 SNPs downstream on chromosome 19 MaCH , one step, 25 rounds Exclude SNPs with MAF<0.01 or HWE< SNPs upstream and 1000 SNPs downstream on chromosome 19 MaCH , one step, 50 rounds rs rs rs rs Exclude SNPs with MAF<0.01 or HWE< SNPs upstream and 1000 SNPs downstream on chromosome 19 MaCH , one step, 50 rounds Rs Rs

31 Descrip9on Whites African Americans Hispanics Valida9on dataset Number of par9cipants in valida9on dataset Concordance rate Quality control procedures Concordance rate aser quality control procedures ADAMS ADAMS ADAMS APOE genotype 113/117 (96.6%) Set genotype to missing if posterior probability <0.90 (N=2 for rs7412) (N=4 for rs429358) APOE genotype 111/114 (97.4%) APOE genotype 25/28 (89.3%) Set genotype to missing if posterior probability <0.90 (N=1 for rs7412) (N=2 for rs429358) APOE genotype 23/25 (92.0%) APOE genotype 15/15 (100%) Set genotype to missing if posterior probability <0.90 (N=1 for rs7412) (N=0 for rs429358) APOE genotype 14/14 (100%)

32 Algorithm to get APOE genotype from the best guess genotype of SNP rs7412 and rs Genotype Frequencies of APOE Apo E imputa4on in HRS rs7412 best guess rs best guess APOE genotype genotype genotype T/T T/T e2/e2 C/T T/T e2/e3 C/T C/T e2/e4 C/C T/T e3/e3 C/C C/T e3/e4 C/C C/C e4/e4 Whites (N=8652) AA (N=1519) HRS_all (N=12367) e2/e2, N (%) 48 (0.56) 20 (1.32) 78 (0.63) e2/e3, N (%) 1131 (13.07) 226 (14.88) 1555 (12.57) e2/e4, N (%) 199 (2.30) 73 (4.81) 301 (2.43) e3/e3, N (%) 5197 (60.07) 704 (46.35) 7415 (59.96) e3/e4, N (%) 1890 (21.84) 434 (28.57) 2751 (22.24) e4/e4, N (%) 187 (2.16) 62 (4.08) 267 (2.16)

33 Principal Components and Popula4on Stra4fica4on

34 Popula4on Stra4fica4on Problem: Diseases and muta4on frequencies are confounded by race Example: Imagine that Hypertension has a prevalence of 50% in blacks and 25% in whites. do a gene4c analysis in the HRS (20 million simple chisquare tests) the vast majority of the hits will be for the black/ white differences and not hypertension. Solu4on: Es4mate and adjust for gene4c variability using principal components

35 Analysis of Genotypes only Principle Component Analysis reveals SNP-vectors explaining largest variation in the data

36 PCA of POPRES cohort

37 The HRS Par4cipants + HapMap par4cipants Figure 1. Principal component analysis of 12,507 unique study par4cipants and 1,230 HapMap controls, using a set of 96,134 autosomal SNPs pruned for both long and short range linkage disequilibrium. For study samples, color coding is according to self iden4fied race while symbol denotes ethnicity (Hispanic or non Hispanic). HapMap samples are color coded by membership in 1 of 11 Phase 3 popula4ons: ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern and Western European ancestry from the CEPH collec4on; CHB: Han Chinese in Beijing, China; CHD: Chinese in Metropolitan Denver, Colorado; GIH: Gujara4 Indians in Houston, Texas; JPT: Japanese in Tokyo, Japan; LWK: Luhya in Webuye, Kenya; MEX: Mexican ancestry in Los Angeles, California; MKK: Maasai in Kinyawa, Kenya; TSI: Tuscan in Italy; and YRI: Yoruban in Ibadan, Nigeria. The percent variance explained by each of these first two components is noted on the axis labels. (Also Figure 11 from the genotype QC report.)

38 What have we done so far?

39 Completed GWAS Analysis on HRS Traits GWAS (29x2=58 total) Gait velocity (with and without osteoporosis, NHW and AA) 4 Longevity (autosomal plus X chromosome) 1 Hand grip strength (main effects and interac4on with sex) 2 Disability in Aging (sex stra4fied) 2 Educa4onal Auainment (quan4ta4ve and dichotomous, sex stra4fied) 4 Subjec4ve well being (3 well being outcomes) 3 Fer4lity (sex stra4fied) 2 Op4mism/Pessimism (sex stra4fied, sex adjusted, interac4on with sex) 5 Personality Traits (six traits) 6 GWAS Replica4on (selected SNPs or complex modeling) Body mass index and SNPs in the PCSK1 gene (NHW and AA) Systolic and diastolic blood pressure (NHW and AA) Hypertension (NHW and AA) Longevity and telomere SNPs CESD subscales (Depression)

40 GWAS of Personality Traits N = 8,113 European Americans Big Five personality traits Agreeableness Extraversion Conscien4ousness Neuro4cism Openness

41 GWAS of Personality Traits Modeling: linear model with age and sex included as covariates Personality Trait = α + β 1 (age) + β 2 (sex) + β 3 (SNP dosage) Analysis Program: PLINK

42 QQ Plots for Neuro4cism and Openness Neuro4cism Openness Observed log(p value) Observed log(p value) Expected log(p value) Expected log(p value)

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44 Norms for Gene4c Consor4a Large cohorts (>50,000) for discovery Equally large replica4on samples Modest harmoniza4on of traits Shared analysis plans Tons of conference calls Meta analysis of results followed by bioinforma4cs Wri4ng groups 1 4 Years to publica4on

45 Resources for Inves4gators

46 User friendly Resources dbgap Candidate gene list Results database

47 dbgap Website Gene4c Data Released April 03, users have been approved to download the data

48 Summary of Projects through dbgap GWAS of longevity, cholesterol levels, cogni4ve func4on, liver disease, leukemia, lupus, economic outcomes Gene environment interac4ons on cogni4ve func4on, mood disorders, body mass Mendelian Randomiza4on to es4mate effects of gene4c, social, and physical risk factors on long term health Control popula4on for GWAS and other studies Assorta4ve ma4ng with respect to alcohol use

49 Crea4ng a bridge to known candidate genes The list of SNPs/genes was compiled from two lists sent to us List 1: Popular established gene4c variants used most ofen by behavioral scien4sts Compiled by Terrie Moffiu and Avshalom Caspi, October 2011 Several genes were also added to the list because they were suggested by Robert Wallace, October 2011 List 2: Cogni4ve age related candidate genes A list provided by Carol Prescou and Jack McArdle

50 Example of Candidate Gene List

51 What s Next? Exome chip data (~16,300 samples from 2006, 2008 & 2010 collec4on waves.) Over 200,000 func4onal muta4ons measured at once Rare variants (needs a completely different analysis approach e.g. SKAT) Fancy mul4gene es4mates of risk (Risk Index) Measured gene4c heritability Gene environment interac4ons Many consor4a conference calls

52 Illumina Exome Chip SNP Set Number of Candidates Number of Successful Designs Coding Content 275, ,094 GWAS Tag SNPs 5,763 5,325 Grid of Common Variants 5,710 5,286 Randomly Selected Synonymous SNPs 5,000 4,651 AIM African Ancestry 3,388 3,241 Addi9onal Notes An addi4onal set of 8,242 SNPs that were unique to the 1000 Genomes Project and popula4ons under represented in the design was added. For 1,000 SNPs, assays were generated on both strands in order to facilitate QC efforts and future development of methods for genotyping of rare variants. AIM Na4ve American Ancestry 1, HLA Tags 2,536 2,459 ESP Requests 1, Fingerprint SNPs MicroRNA Target Sites Mitochondrial Variants Chromosome Y Indels

53 Es4ma4ng the gene4c rela4onship from genome wide SNPs A ijk is the gene4c rela4onship between individual j and k at SNP i N is the number of SNPs p i is the allele frequency at SNP i x ij is an indicator variable that takes value of 0,1 or 2 if the genotype of the j th individual at SNPi is bb, Bb or BB

54 An Example of The Gene4c Rela4onship Matrix Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6.. Subject Subject Subject Subject Subject Subject 6..

55 Histogram of the Gene4c Relatedness Among the HRS subjects (N=12367) Mean= 8.382e 05 Median=0.008 Min= Max=0.559

56 Histogram of the Gene4c Relatedness used in our first heritability study (N=4367) Mean=0.010 Median=0.011 Min= Max=0.025

57 Es4mate Heritability of BMI Using Unrelated Individuals Square of z score difference = α + βa jk where α = 2σ 2 p Β = 2σ 2 g Heritability = σ 2 g /σ2 p Gene4c rela4onship A jk

58 Summary New GWAS requests are slowing down Ongoing connec4on to 10 GWAS consor4a We have finished exome chip cleaning, working with dbgap for submission, and new consor4a analysis Candidate Gene List being reviewed We would like to find a way to offer the gene4c relatedness matrix to inves4gators Should we begin a series of gene4cs webinars? What would be helpful to you?