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stata commands for moving data between phase and haploview stata conference dc 09 july 30 31 2009 n.
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John Charles “Chuck” Huber Jr, PhD Assistant Professor of Biostatistics PowerPoint Presentation
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John Charles “Chuck” Huber Jr, PhD Assistant Professor of Biostatistics

John Charles “Chuck” Huber Jr, PhD Assistant Professor of Biostatistics

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John Charles “Chuck” Huber Jr, PhD Assistant Professor of Biostatistics

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  1. Stata commands for moving data between PHASE and HaploViewStata Conference DC ‘09July 30-31, 2009 John Charles “Chuck” Huber Jr, PhD Assistant Professor of Biostatistics Department of Epidemiology and Biostatistics School of Rural Public Health Texas A&M Health Science Center jchuber@tamu.edu

  2. Motivation Many rapidly growing areas of research utilize multiple specialty “boutique” computer programs to conduct highly specialized analyses. The Stata user is faced with two choices: • Write new Stata commands that do the same analyses • Write Stata commands that efficiently export and import data for these “boutique” programs

  3. Stata for Genetic Data Analysis Outline • Genetic Data Analysis using Stata • Genetics Background • The “file” commands in Stata • The phasein and phaseout commands • The HaploView program • The haploviewout command • Summary

  4. Stata for Genetic Data Analysis 2007 UK Stata Users Group meeting: http://www.stata.com/meeting/13uk/ A brief introduction to genetic epidemiology using Stata Neil Shephard, University of Sheffield An overview of using Stata to perform candidate gene association analysis will be presented. Areas covered will include data manipulation, Hardy–Weinberg equilibrium, calculating and plotting linkage disequilibrium, estimating haplotypes, and interfacing with external programs.

  5. User Written Genetics Commands Programs written by David Clayton • ginsheet- Read genotype data from text files. • gloci - Make a list of loci. • greshape - Reshape a file containing genotypes to a file of alleles. • gtab - Tabulate allele frequencies within genotypes and generate indicators (performs Hardy-Weinberg Equilibrium testing). • gtype - Create a single genotype variable from two allele variables. • htype - Create a haplotype variable from allele variables. • mltdt - Multiple locus TDT for haplotype tagging SNPs (htSNPs). • origin - Analysis of parental origin effect in TDT trios. • pseudocc - Create a pseudo-case-control study from case-parent trios. • pscc - Experimental version of pseudocc in which there may be several groups of linked loci. • pwld - Pairwise linkage disequilibrium measures. • rclogit - Conditional logistic regression with robust standard errors. • snp2hap - Infer haplotypes of 2-locus SNP markers. • tdt - Classical TDT test. • trios - Tabulate genotypes of parent-offspring trios.

  6. User Written Genetics Commands Programs written by Adrian Mander • gipf - Graphical representation of log-linear models. • hapipf - Haplotype frequency estimation using an EM algorithm and log-linear modelling. • pedread - Read's pedigree data file (in pre-Makeped LINKAGE format), similar to ginsheet • pedsumm - Summarises a pre-Makeped LINKAGE file that is currently in Stata's memory. • pedraw - Draws one pedigree in the graphics window • plotmatrix - Produces LD heatmaps displaying graphically the strength of LD between markers. • profhap - Calculates profile likelihood confidence intervals for results from hapipf • swblock - A step-wise hapipf routine to identify the parsimonious model to describe the Haplotype block pattern. • qhapipf - Analysis of quantitative traits using regression and log-linear modelling when phase is unknown. • hapblock - attempts to find the edge of areas containing high LD within a set of loci

  7. User Written Genetics Commands Programs written by Mario Cleves • gencc - Genetic case-control tests • genhw - Hardy-Weinberg Equilibrium tests • qtlsnp - A program for testng associations between SNPs an a quantitative trait. Programs written by Catherine Saunders • co_power - Power calculations for Case-only study designs. • gei_matching - • geipower - Power calculations for Gene-Environment interactions. • ggipower - Power calculations for Gene-Gene interactions. • tdt_geipower - Power calculations for Gene-Environment interactions via TDT analysis. • tdt_ggipower - Power calculations for Gene-Gene interactions via TDT analysis. Programs written by Neil Shephard • genass- Performs a number of statistical tests on your genotypic data and collates the results into a Stata formatted data set for browsing.

  8. The Post-Genome Era February 15, 2001 February 16, 2001

  9. Scientific Method: Observe Hartl & Jones (1998) pg 18, Figure 1.13

  10. Scientific Method: Predict Watson et al. (2004) pg 29, Box 2-2

  11. Scientific Method: Manipulate

  12. The Structure of DNA Hartl & Jones (1998) pg 9, Figure 1.5

  13. The Structure of DNA Watson et al. (2004) pg 23, Figure 2.5

  14. What is a SNP? • A SNP is a single nucleotide polymorphism (the individual nucleotides are called alleles) ataagtcgatactgatgcatagctagctgactgacgcgatataagtccatactgatgcatagctagctgactgaagcgat ataagtccatactgatgcatagctagctgactgacgcgat ataagtcgatactgatgcatagctagctgactgaagcgat Person 1 – Chromosome 1 Person 1 – Chromosome 2 Person 2 – Chromosome 1 Person 2 – Chromosome 2 SNP1 SNP2

  15. Allelic Association • Simple 2x2 table • One table per SNP • Compute a simple chi-squared statistic or odds ratio for each SNP

  16. Genotypic Association • Compute chi-squared tests • Allows testing of various disease models (dominant, recessive, additivity)

  17. What is a Haplotype? • A haplotype is the combination of one or more alleles found on the same chromosome • Person 1 has a “gc” haplotype and a “ca” haplotype • Person 2 has a “cc” haplotype and a “ga” haplotype ataagtcgatactgatgcatagctagctgactgacgcgatataagtccatactgatgcatagctagctgactgaagcgat ataagtccatactgatgcatagctagctgactgacgcgat ataagtcgatactgatgcatagctagctgactgaagcgat Person 1 – Chromosome 1 Person 1 – Chromosome 2 Person 2 – Chromosome 1 Person 2 – Chromosome 2 SNP1 SNP2

  18. Haplotypic Association • Compute chi-squared tests • Two SNPs with genotypes a/g and c/t respectively

  19. Why are haplotypes important? 2009 Oxford and Cambridge Boat Race http://www.theboatrace.org/gallery/2009?page=7#

  20. Why are haplotypes important? SNP1 SNP2 SNP3 SNP4 SNP5 Chromosome R Chromosome D President VP State Defense Treasury

  21. Why are haplotypes important? SNP1 SNP2 SNP3 SNP4 SNP5 Chromosome R Chromosome D President VP State Defense Treasury Rearranging the members of each “chromosome” could have a profound effect!

  22. Why are haplotypes important? Hartl & Jones (1998) pg 18, Figure 1.13

  23. Hartl & Jones (1998) pg 18, Figure 1.13

  24. Why are haplotypes important? Watson et al. (2004) pg 29, Box 2-2

  25. The PHASE Program • Unfortunately, haplotypes are not observed directly using modern, high-throughput lab techniques • We observe genotypes and must infer the haplotype structure using algorithms • PHASE is a very popular program for inferring haplotypes from many SNPs simultaneously (Stephens, Smith & Donnelly, 2001)

  26. The phaseout Command Raw Genotype Data in Stata

  27. The phaseout Command Input file format for PHASE

  28. The phaseout Command I need to get my data from here: to here:

  29. The “file” commands in Stata Using “file open”, “file write” and “file close” file open Example1 using "ExampleFile.txt", write replace file write Example1 "Hello World" _newline(1) file write Example1 "Why so blue?" _newline(1) file close Example1

  30. The “file” commands in Stata Using “file open”, “file read” and “file close” . file open Example2 using "ExampleFile.txt", read . file read Example2 Line1 . file read Example2 Line2 . file close Example2 . disp "Line1: `Line1'" Line1: Hello World . disp "Line2: `Line2'" Line2: Why so blue?

  31. The phaseout Command Syntax for phaseout phaseout SNPlist , idvariable(string) filename(string) [missing(string) separator(string) positions(string)] Example local SNPList "rs1413711 rs3024987 rs3024989" local PositionsList "674 836 1955“ phaseout `SNPList' , idvariable("id") filename("VEGF.inp") missing("X/X 9/9") positions(`PositionsList') separator("/")

  32. The phaseout Command Example local SNPList "rs1413711 rs3024987 rs3024989" local PositionsList "674 836 1955“ phaseout `SNPList' , idvariable("id") filename("VEGF.inp") missing("X/X 9/9") positions(`PositionsList') separator("/")

  33. The phaseout Command Example local SNPList "rs1413711 rs3024987 rs3024989" local PositionsList "674 836 1955“ phaseout `SNPList' , idvariable("id") filename("VEGF.inp") missing("X/X 9/9") positions(`PositionsList') separator("/")

  34. The phasein Command Syntax for phasein phasein PhaseOutputFile [, markers(string) positions(string)] Example phasein VEGF.out, markers("MarkerList.txt") positions("PositionList.txt")

  35. The phasein Command Output file format from PHASE

  36. The phasein Command Example phasein VEGF.out, markers("MarkerList.txt") positions("PositionList.txt")

  37. The phasein Command Example phasein VEGF.out, markers("MarkerList.txt") positions("PositionList.txt")

  38. The HaploView Program • Once we have inferred our haplotypes, we can conduct further association analyses using the full complement of Stata commands. • We might also want to explore our data in the popular program HaploView (Barrett et al, 2005)

  39. The haploviewout Command Syntax for haploviewout haploviewoutSNPlist, idvariable(string) filename(string) [positions(string)] [familyid(string)] [poslabel] Example local MarkerList "rs1413711 rs3024987 rs3024989“ haploviewout `MarkerList', idvariable(id) filename("VEGF") poslabel

  40. The haploviewout Command Example local SNPList "rs1413711 rs3024987 rs3024989“ haploviewout `MarkerList', idvariable(id) filename("VEGF") poslabel

  41. The haploviewout Command Example local SNPList "rs1413711 rs3024987 rs3024989“ haploviewout `MarkerList', idvariable(id) filename("VEGF") poslabel

  42. The haploviewout Command

  43. The haploviewout Command

  44. The haploviewout Command

  45. Summary Compared to recreating “boutique” programs in Stata, it is relatively easy to create programs for exporting and importing data.

  46. Acknowledgements • Grant 1-R01DK073618-02 from the National Institute of Diabetes and Digestive and Kidney Diseases • Grant 2006-35205-16715 from the United States Department of Agriculture. • Drs. Loren Skow, Krista Fritz, Candice Brinkmeyer-Langford of the Texas A&M College of Veterinary Medicine • Roger Newson of the Imperial College London

  47. References • Barrett, J., Fry, B., Maller, J., & Daly, M. (2005). Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 21, 263-265. • Hartl, D.L., Jones, E.W. (1998) Genetics: Principles and Analysis, 4th Ed. Jones & Bartlett Publishers • Stephens, M., & Donnelly, P. (2003). A Comparison of Bayesian Methods for Haplotype Reconstruction from Population Genotype Data. American Journal of Human Genetics, 73, 1162–1169. • Stephens, M., Smith, N. J., & Donnelly, P. (2001). A New Statistical Method for Haplotype Reconstruction from Population Data. American Journal of Human Genetics, 68, 978–989. • Watson, J.D., Baker, T.A., Bell, S.P., Gann, A., Levine, M., Losick, R. (2004) Molecular Biology of the Gene, 5th Ed. Benjamin Cummings