Handling Immunogenetic Data Managing and Validating HLA Data

Transcription

1 Handling Immunogenetic Data Managing and Validating HLA Data Steven J. Mack PhD Children s Hospital Oakland Research Institute 16 th IHIW & Joint Conference Sunday 3 June, 2012

2 Overview 1. Master Analytical Dataset 2. The Perils of Using MS Excel 3. HLA Ambiguity 4. Internal Validation and Standardization of Datasets 5. Analytical Validation of HLA Population Data

3 Create a Master Dataset For Analysis/Sharing Create a single master data-file for research datasets Integrate demographic, phenotypic, and genotypic information Each analytical element (sample) is a row Each variable (genotype, phenotype, measurement) is a column Where possible, encode variable data numerically avoids errors, ambiguity of meaning, and saves space A data dictionary accompanies and explains the master data-file Describe the meaning and significance of each variable Define the numerical codes for each variable 1 = affected, 0 = control Define the code(s) for missing values -9 = unknown

4 Should I Store My Data in Excel? Microsoft Excel doesn t speak HLA IMGT/HLA db version Allele Name Excel Error 1.*.* *.* *.* 01:01:01 1:01:01 AM 3.*.* 01: Opening a text file by right-clicking and selecting Excel will result in Excel errors like these. Use Excel at your own risk; store data using a text format; consider using database software.

5 Consider Short-Term and Long-Term Needs for HLA Data Short-Term Need Interim Reporting, Submission Deadline, Data Analysis May often require an abstracted best guess to summarize the data. But that best guess may only be useful at the time it is made. Long-Term Need Archiving, Storage, Deposition into Public Domain Registry, Meta-Analysis, Multi-cycle projects Need to be able to make full use of these data in the future; The goal is to maximize available information, which often requires that primary or raw data be maintained. Should be storing both cleaned and ambiguous data.

6 HLA Data Ambiguity Allele ambiguity results when the polymorphisms that distinguish alleles fall outside of the regions assessed by the genotyping system. A*02:03:01/A*02:253/A*02:264 identical exon 2&3 sequence Genotype ambiguity results from an inability to establish chromosomal phase between identified polymorphisms. DRB1*04:01:01+DRB1*13:01:01 or DRB1*04:01:01+ DRB1*13:117 or DRB1*04:13+DRB1*13:02:01 or DRB1*04:14+DRB1*14:21 or DRB1*04:35+DRB1*13:40 or DRB1*04:38+DRB1*13:20 identical heterozygous exon 2 sequence An HLA genotype can include both allele and genotype ambiguity A*02:03:01/A*02:253/A*02:264+A*03:01:02 or A*02:171:01+A*03:50 or A*02:171:02+A*03:66

7 NMDP Allele Codes Coding Ambiguous HLA Data DRB1*04:02:01+DRB1*11:20 or DRB1*04:14+DRB1*11:16 Can be represented as DRB1*04:BK+DRB1*11:YR DRB1*04:BK represents DRB1*04:02/DRB1*04:14 DRB1*11:YR represents DRB1*11:20/DRB1*11:16 But these codes also represent excluded genotypes DRB1*04:02:01+DRB1*11:16 and DRB1*04:14+DRB1*11:20 Allele Codes increase ambiguity and omit information in the 3 rd & 4 th fields

8 Coding Ambiguous HLA Data Allele Groups G groups: Alleles with identical nucleotide sequence in the exons encoding the peptide binding domain (exon 2 for class II alleles, and exons 2 & 3 for class I alleles) A*02:03:01G = A*02:03:01/A*02:253/A*02:264 A*02:07:01G = A*02:07:01/A*02:07:02/A*02:15N/A*02:265 P groups: Alleles with identical peptide sequence in the peptide binding domain, with the exclusion of null alleles. A*02:03P = A*02:03:01/A*02:03:02/A*02:03:03/A*02:03:04/A*02:253/A*02:264 A*02:07P = A*02:07:01/A*02:07:02/A*02:265

9 Recording Ambiguous HLA Data Genotype Strings Genotype List String (GL String) Uses specific operators to describe the relationships between alleles, allowing genotype data to be recorded in a single line. Order of Precedence Data Delimiter Operator Description 1 ^ Gene/Locus 2 Genotype list 3 + Genotype 4 ~ Haplotype 5 / Alleles

10 GL String Representation of Ambiguous HLA Data Allelic ambiguity delimiter (forward slash) Defines an allele list / A*23:26/A*23:39 allele allele Possible alleles at locus A

11 GL String Representation of Ambiguous HLA Data ~ Haplotype delimiter (tilde) Applied in cis to identify a haplotype DRB5*01:02~DRB1*15:04 allele at locus DRB5 allele at locus DRB1 same chromosome

12 GL String Representation of Ambiguous HLA Data Genotype delimiter (plus sign) Identifies alleles on different chromosomes, genotype (trans) Delimits haplotype May also indicate gene duplication (ambiguous cis) + A*02:302+A*23:26/A*23:39 allele allele allele HLA-A allelic ambiguity genotype

13 GL String Representation of Ambiguous HLA Data Genotype list delimiter (pipe) Distinguishes ambiguous genotypes A*02:69+A*23:30 A*02:302+A*23:26/A*23:39 allele allele allele ambiguous allele list genotype genotype

14 GL String Representation of Ambiguous HLA Data Locus delimiter (carat) Distinguishes loci A*02:69+A*23:30 A*02:302+A*23:26/A*23:39^B*44:02:13+B*49:08 ^ allele allele allele ambiguous allele list allele allele possible genotype for A possible genotype for A Ambiguous genotype list for HLA-A HLA-B genotype

15 Alternative Format for Recording Ambiguous HLA Data UNIFORMAT Also allows ambiguous genotype data to be recorded in a single line, using different operators (colons, commas, spaces, tabs) than in GL String. identifier {tab mark} allele,allele [{space} allele,allele...][{tab mark} allele, allele...][{tab mark}#comments] sample identifier ambiguous alleles at one locus ambiguous alleles at additional loci comments

16 Know Your Nomenclature Version Identify the IMGT/HLA database release number applicable to your data The release number identifies which alleles should and should not be in your data IMGT/HLA db rel Allele 1.13 A* A* B* B* DPB1* DPB1*104:01 This information allows you to check your data for naming/recording errors

17 Dataset Validation The Allele Name Translation Tool (ANTT) can be used to validate/update the allele names in column-formatted datasets against/to any IMGT/HLA db release. Parses forward-slash (/) allele ambiguity delimiters; the next version will parse any delimiters (e.g. all GL String delimiters). Documents right-truncated allele names and unrecognized allele names. Identifies the id and row-column position of errors. DRB1*08:02:00 could not be found in the HLA-DRB1.upd translation file.[id = 003][Row = 4 Column = 2] DRB1*04:08 appears to be a truncated version of the DRB1*04:08:01 allele, and was translated to DRB1*0408. [id = 005][Row = 6 Column = 2]

18 Internal Standardization of Data Data Consistency Record data consistently across individuals (and datasets). For individuals typed Record homozygotes as diploid A*02:03:01/A*02:253/A*02:264+A*02:03:01/A*02:253/A*02:264 Use a code to identify missing data ****, -9, missing, etc. Use a code to identify absent loci when recording structural variants DRB1*01:01:01~DRB3*BLANK~DRB4*BLANK~DRB5*BLANK+DRB1*15:01:01~DRB3*BLANK~DRB4*BLANK~DRB5*01:01:01

19 Internal Standardization of Data Data Modification Document any post-typing modifications made to data. How did A*02:69+A*23:30 A*02:302+A*23:26/A*23:39 become A*02:302+A*23:39? For analysis across individuals and datasets Analyze allele names at the same level of polymorphism Avoid A*01:01:01:01, A*01:01:01, A*01:01, and A*01 in the same analysis You will have to throw out some data/information. Analyze alleles at the same sequence level For exons 2/2& 3 testing, don t analyze alleles in the same G group separately. Analyze DRB1*14:01:01 and DRB1*14:54 as DRB1*14:01:01G. Analyze allele names in the same nomenclature context Don t analyze A*2416 and A*3108; update to a single nomenclature.

20 Analytical Validation of HLA Population Data The Hardy-Weinberg (HW) model can give you insights into data-quality Hardy-Weinberg Equilibrium The frequency of the alleles should predict the frequency of the genotypes. If it does not (HW deviation), you may have problems with your data. Multi-locus HW deviation Sampling error Related individuals Populations mixed together (admixture) Solution: Review inclusion criteria and remove individuals Critical Typing problem (uncommon) Single-locus HW deviation Typing error Excess of homozygotes due to missed alleles Excess of heterozygotes due to poor assignment Solution: Review and redo typings Selection (unexpected in control-populations)

21 Example of HW Data Validation 13 DQB1 alleles in a population of n=109 Genotype Observed Count Expected Count p-value DQB1*03:03:02+DQB1*02:01: DQB1*03:03:02+DQB1*03:03: Chen s test of individual genotypes in PyPop ( HW deviation due to poor detection of DQB1*02:01:01 in the presence of DQB1*03:03:02, resulting from a SNP in DQB1*02:01:01 under a PCR primer. For population/control datasets, the first analysis done should be a HW test.

22 Much Thanks To Pierre-Antoine Gourraud Standard Methods for the Management Jill A. Hollenbach of Immunogenetic Data. Frank T. Christiansen Thomas Barnetche and Brian D. Tait (eds.), Immunogenetics: Richard Single Methods and Applications in Clinical Practice, Steven J. Mack Methods in Molecular Biology, vol pages doi: / _12 Children s Hospital Oakland Henry A. Erlich Janelle Noble Elizabeth Trachtenberg Immunogenetics Colleagues Glenys Thomson Alicia Sanchez-Mazas Owen D. Solberg Martin Maiers Carolyn Hurley Marcel Tilanus Christien Voorter Immunogenetics Community

23 Handling Immunogenetic Data Managing Highly Polymorphic Data for Disease Association Studies Jill A. Hollenbach, PhD, MPH Children s Hospital Oakland Research Institute 16 th IHIW & Joint Conference Sunday 3 June, 2012

24 Immunogenetic data require special handling in disease association studies

25 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci

26 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci Many rare alleles >>> sparse cells

27 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci Many rare alleles >>> sparse cells Need to identify all disease associated alleles

28 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci Many rare alleles >>> sparse cells Need to identify all disease associated alleles 2 Strong linkage disequilibrium

29 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci Many rare alleles >>> sparse cells Need to identify all disease associated alleles 2 Strong linkage disequilibrium Identify which loci are primary

30 Immunogenetic data require special handling in disease association studies 1 Highly polymorphic loci Many rare alleles >>> sparse cells Need to identify all disease associated alleles 2 Strong linkage disequilibrium Identify which loci are primary 3Immunogenetic loci show strong population structure

31 Case-Control Study

32 Case-Control Study Statistical tests

33 Case-Control Study Statistical tests First step: Population analyses

34 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP

35 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies

36 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies Association tests

37 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies Association tests Contingency tables /chi-squared test

38 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies Association tests Contingency tables /chi-squared test Logistic regression

39 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies Association tests Contingency tables /chi-squared test Logistic regression Other tests/special cases (Cochran-Armitage test for trend; survival analysis; etc)

40 Case-Control Study Statistical tests First step: Population analyses Tests for fit to HWEP Calculation of allele and haplotype frequencies Association tests Contingency tables /chi-squared test Logistic regression Other tests/special cases (Cochran-Armitage test for trend; survival analysis; etc)

41 Case-Control Study Contingency tables

42 Case-Control Study Contingency tables Test difference (independence) of frequency distributions for categorical variables between groups - 2 test

43 Case-Control Study Contingency tables Test difference (independence) of frequency distributions for categorical variables between groups - 2 test Always constructed with raw counts, not frequency data

44 Case-Control Study Contingency tables Test difference (independence) of frequency distributions for categorical variables between groups - 2 test Always constructed with raw counts, not frequency data Analyses can be performed at the allele, genotype, haplotype, amino acid or other levels

45 Sparse cells in contingency tables

46 Chi-squared Test Statistic: (O - E) 2 c 2 = E å all cells O is the observed cell counts E is the expected cell counts, where E = Sparse cells in contingency tables (row total column total) 2N

47 Chi-squared Test Statistic: (O - E) 2 c 2 = E å all cells O is the observed cell counts E is the expected cell counts, where E = Sparse cells in contingency tables (row total column total) 2N c 2 test is inappropriate if any expected count is less than 1 or if the expected count is less than five in more than 20% of all cells in a contingency table *** aka sparse cells

48 Sparse cells in contingency tables DRB1 case control n

49 Sparse cells in contingency tables DRB1 case control n

50 Sparse cells in contingency tables DRB1 case control binned n

51 Sparse cells in contingency tables DRB1 case control p-value binned

52 Sparse cells in contingency tables DRB1 case control p-value binned

53 Identifying all disease associated alleles

54 Identifying all disease associated alleles Relative predispositional effects method (RPE; Payami et al 1989)

55 Identifying all disease associated alleles Relative predispositional effects method (RPE; Payami et al 1989) Method to identify all heterogeneity in disease risk at locus of interest

56 Identifying all disease associated alleles Relative predispositional effects method (RPE; Payami et al 1989) Method to identify all heterogeneity in disease risk at locus of interest Contingency table testing reveals overall difference in allele frequency distributions at a locus

57 Identifying all disease associated alleles Relative predispositional effects method (RPE; Payami et al 1989) Method to identify all heterogeneity in disease risk at locus of interest Contingency table testing reveals overall difference in allele frequency distributions at a locus But we want to identify all alleles that contribute significantly

58 Identifying all disease associated alleles Relative predispositional effects method (RPE; Payami et al 1989) Method to identify all heterogeneity in disease risk at locus of interest Contingency table testing reveals overall difference in allele frequency distributions at a locus But we want to identify all alleles that contribute significantly Alleles with the strongest predisposing or protective effects sequentially removed from analysis until no further heterogeneity in risk effects is seen

59 Identifying all disease associated alleles DRB1 case control p-value binned

60 Identifying all disease associated alleles DRB1 case control p-value binned

61 Identifying all disease associated alleles

62 Identifying all disease associated alleles

63 Identifying the primary locus

64 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls

65 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03:

66 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved.

67 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03)

68 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) (0.10)/(0.04)=

69 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) (0.10)/(0.04)=2.5

70 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.10)/(0.04)=2.5

71 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.10)/(0.04)=2.5 (0.05)/(0.02)=

72 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.10)/(0.04)=2.5 (0.05)/(0.02)=2.5

73 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.10)/(0.04)=2.5 (0.05)/(0.02)=2.5

74 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved.

75 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03)

76 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) (0.12)/(0.02)=

77 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) (0.12)/(0.02)=6

78 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.12)/(0.02)=6

79 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.12)/(0.02)=6 (0.05)/(0.02)=

80 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.12)/(0.02)=6 (0.05)/(0.02)=2.5

81 Identifying the primary locus The frequencies of two haplotypes of an allele at the predisposing locus may differ between patients and controls DRB1~DQB1 Haplotype Case Control 07:01~02: :01~03: HOWEVER: the relative frequency of their ratios will be the same if the second locus is not involved. f case (07:01~02:01)/f case (07:01~03:03) f cont (07:01~02:01)/f cont (07:01~03:03) (0.12)/(0.02)=6 (0.05)/(0.02)=2.5

82 Population substructure in disease association studies

83 Population substructure in disease association studies (f) PopX (f) LocusA PopY

84 Population substructure in disease association studies (f) PopX (f) LocusA PopY

85 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY cases controls

86 Population substructure in disease association studies (f) PopX cases controls LocusA PopY No association (f) cases controls

87 Population substructure in disease association studies (f) PopX cases controls LocusA PopY No association (f) cases controls

88 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls

89 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls cases controls

90 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls cases controls

91 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls p<.05 cases controls

92 Population substructure in disease association studies cases controls p<.05

93 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls p<.05 cases controls

94 Population substructure in disease association studies (f) PopX cases controls (f) LocusA PopY No association PopXY cases controls p<.05 cases controls

95 Population substructure in disease association studies cases controls p<.05

96 Immunogenetic data require special handling in disease association studies

97 Immunogenetic data require special handling in disease association studies Highly polymorphic loci

98 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles

99 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning

100 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles

101 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles Relative predispositional effects

102 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles Relative predispositional effects Strong linkage disequilibrium

103 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles Relative predispositional effects Strong linkage disequilibrium Identify which loci are primary

104 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles Relative predispositional effects Strong linkage disequilibrium Identify which loci are primary Condition within haplotypes

105 Immunogenetic data require special handling in disease association studies Highly polymorphic loci Combine low frequency alleles Binning Need to identify all associated alleles Relative predispositional effects Strong linkage disequilibrium Identify which loci are primary Condition within haplotypes Control for population substructure

106 For further discussion see: Hollenbach JA, Mack SJ, Thomson G, Gourraud PA. Analytical methods for disease association studies with immunogenetic data. Methods Mol Biol. 2012;882: DOI: / _14

107 Thank you!