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人类群体遗传学 基本原理和分析方法

中国科学院上海生命科学研究院研究生课程 人类群体遗传学. 人类群体遗传学 基本原理和分析方法. 中科院 - 马普学会计算生物学伙伴研究所. 徐书华 金 力. 2008 － 2009 学年第二学期 《 人类群体遗传学分析方法 》 课程表 上课时间：每周四上午 10:00-11:50 上课地点：中科大厦 4 楼 403 室第 7 教室. 第八讲. 基因定位中的关联分析 II. 第八讲. 基因定位常见策略 连锁分析 关联分析 关联分析 关联分析的群体遗传学基础 关联分析的统计学基础 关联分析实验设计 关联分析中的常见分析方法

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人类群体遗传学 基本原理和分析方法

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  1. 中国科学院上海生命科学研究院研究生课程人类群体遗传学中国科学院上海生命科学研究院研究生课程人类群体遗传学 人类群体遗传学基本原理和分析方法 中科院-马普学会计算生物学伙伴研究所 徐书华 金 力

  2. 2008－2009学年第二学期《人类群体遗传学分析方法》课程表2008－2009学年第二学期《人类群体遗传学分析方法》课程表 上课时间：每周四上午10:00-11:50 上课地点：中科大厦4楼403室第7教室

  3. 第八讲 基因定位中的关联分析II

  4. 第八讲 • 基因定位常见策略 • 连锁分析 • 关联分析 • 关联分析 • 关联分析的群体遗传学基础 • 关联分析的统计学基础 • 关联分析实验设计 • 关联分析中的常见分析方法 • 关联分析中存在的问题 • 隐藏的群体结构问题 • 多重检验问题 • …

  5. GENEPHENOTYPE / DISEASEENVIRONMENT

  6. Association analysis Gene Mapping • Linkage analysis Two strategies

  7. Gene Mapping • Linkage analysis • Pedigree data • Localize chromosomal regions where disease gene might be found. • Low resolution (10s cM ≈ 107-108 bp in Human). • Association analysis • Population data • Further localize the region where the disease gene is located. • High resolution (10s - 100s kb).

  8. Principle always same Correlate phenotypic and genotypic variability

  9. 3/5 2/6 3/6 5/6 Association AND Linkage 3/6 2/4 4/6 2/6 3/2 6/2 6/6 6/6 All families are ‘linked’ with the marker Allele 6 is ‘associated’ with disease

  10. Allelic Association Controls Cases 6/6 6/2 3/5 3/4 3/6 5/6 2/4 3/2 3/6 6/6 4/6 2/6 2/6 5/2 Allele 6 is ‘associated’ with disease

  11. Linkage vs Association

  12. Population-based association studies Optimal mapping strategies Family-based linkage studies Unlikely exist Magnitude of effect No good strategy Frequency in population

  13. Association Study Designs • Designs • Family-based • Trio (TDT), twins/sib-pairs/extended families (QTDT) • Case-control • Collections of individuals with disease, matched with sample w/o disease • Some ‘case only’ designs

  14. Family-based Designs for Association Studies Advantages: • Not susceptible to confounding due to population substructure • Tests for linkage and association • Can test for parent-of-origin effects Disadvantages: • Inefficient recruitment, only heterozygous parents informative • Often cannot test for environmental main-effects • Family members often not available (eg, late-onset diseases)

  15. A,a A,a A,A For each individual, have 2x2 table of 0s, 1s, or 2s • Use all such tables to get a matched chi-square test for excess occurrence in cells b and c [McNemar’s test] TDT (transmission-disequilibrium test) • Basic idea of TDT • Disease alleles are transmitted from parents to offspring • Marker alleles in LD with these alleles will also be transmitted preferentially to affected offspring • Test if heterozygous parents transmit a particular marker allele to affected offspring more frequently than expected • Looks for excess transmission of particular alleles from parents to affected children • Controls are ‘non-transmitted alleles’ A-Not transmitted a – Not transmitted A - Transmitted 0 2 a - Transmitted 0 0

  16. Why Case/Control? • Limitations • 1. Possible Population Stratification • 2. Need for highly dense marker sets (capture LD) • Lack of phase information • Lack of consistency of results Advantages • Methodology is well-known • Convenient to collect • Common • Verylarge samples • More efficient recruitment than family-based sampling • Simultaneous assessment of disease allele frequency, penetrance, and AR • Unrelated controls can provide increased power These can be overcome! 1. Assessment and ‘genomic control’ of stratification 2. SNP maps 3. Imputed haplotypes

  17. Statistical basis • The p-value • Under the null hypothesis the probability that you observe your data or something more extreme • Distribution of the test statistic under the null hypothesis (integrates to 1) • F • t • Chi-Square

  18. The Decision • Reject the null - fail to reject the null • Truth versus decision • H0 = no change • H1 = difference The Decision H0 H1 Significance (no diff) (diff) level H0 (no diff) a The truth H1 (diff) b (1-b) Power

  19. Distribution of the test statistic under the alternative hypothesis Null distribution Alternative distribution a B

  20. Chi-square (X2)test observed expected

  21. Statistical Power • Null hypothesis: all alleles are equal risk • Given that a risk allele exists, how likely is a study to reject the null? • Are you ready to genotype?

  22. Power Analysis • Statistical significance • Significance = p(false positive) • Traditional threshold 5% • Statistical power • Power = 1- p(false negative) • Traditional threshold 80% • Traditional thresholds balance confidence in results against reasonable sample size

  23. True Distribution Distribution under H0 95% c.i. under H0 Small sample: 50% Power

  24. Maximizing Power • Effect size • Larger relative risk = greater difference between means • Sample size • Larger sample = smaller SEM • Measurement error • Less error = smaller SEM

  25. Large sample: 97.5% Power

  26. Genetic Relative Risk

  27. Power to Detect RR=2N Cases, N Controls

  28. Power to Detect RR=2N Cases, N Controls

  29. Power to Detect RR=2N Cases, N Controls

  30. Power to Detect RR=2N Cases, N Controls

  31. Power to Detect SNP Risk200 Cases, 200 Controls

  32. Power Analysis Summary • For common disease, relative risk of common alleles is probably less than 4 • Maximize number of samples for maximal power • For RR < 4, measurement error of more than 1% can significantly decrease power, even in large samples

  33. Sample size requirements for case-control analyses of SNPs (2 controls per case; detectable difference of OR 1.5; power=80%). Statistical power: an increasing concern Palmer, L. J. and W. O. C. M. Cookson (2001). “Using Single Nucleotide Polymorphisms (SNPs) as a means to understanding the pathophysiology of asthma.” Respiratory Research 2: 102-112.

  34. Focus on Common Variants - Haplotype Patterns SNPs > 10% MAF All Gene SNPs

  35. Why Common Variants? • Rare alleles with large effect (RR > 4) should already be identified from linkage studies • Association studies have low power to detect rare alleles with small effect (RR < 4) • Rare alleles with small effect are not important, unless there are a lot of them • Theory suggests that it is unlikely that many rare alleles with small effect exist (Reich and Lander 2001).

  36. CD/CVHypothesis • Common Disease-Common Variant hypothesis: Common diseases have been around for a long time. Alleles require a long time to become common (frequent) in the population. Common diseases are influenced by frequent alleles.

  37. D r r M D M Pedigree Analysis & Association Mapping Association Mapping: Pedigree Analysis: 2N generations Pedigree known Few meiosis (max 100s) Resolution: cMorgans (Mbases) Pedigree unknown Many meiosis (>104) Resolution: 10-5 Morgans (Kbases) Adapted from McVean and others

  38. Example of Linkage Disequilibrium through generations

  39. 4 maps for gene localization • Gene localization or gene mapping is based on four maps, each with additive distances. • Two of these maps are physical: • the high-resolution genome map in base pairs (bp) • the low-resolution cytogenetic map in chromosome bands of estimated physical lengths. • The other two maps are purely genetic: • the linkage map in Morgans or centimorgans (cM) • the map of linkage disequilibrium (LD) in LD units (LDU)

  40. LD map • Genetic maps in linkage disequilibrium (LD) units play the same role for association mapping as maps in centimorgans provide at much lower resolution for linkage mapping. • Association mapping of genes determining disease susceptibility and other phenotypes is based on the theory of LD.

  41. Graphic representation of LD r2 D’ GOLD

  42. LD based association studies • The paradigm underlying association studies is that linkage disequilibrium can be used to capture associations between markers and nearby untyped SNPs. In strong LD marker untyped SNP

  43. Marker Selection for Association Studies Direct: Catalog and test all functional variants for association Indirect: Use dense SNP map and select based on LD Collins, Guyer, Chakravarti (1997). Science 278:1580-81

  44. Parameters for SNP Selection • Allele Frequency • Putative Function (cSNPs) • Genomic Context (Unique vs. Repeat) • Patterns of Linkage Disequilibrium

  45. Association studies • Association between risk factor and disease: risk factor is significantly more frequent among affected than among unaffected individuals • In genetic epidemiology: • Risk factors = alleles/genotypes/haplotypes

  46. Association studies • Candidate genes (functional or positional) • Fine mapping in linkage regions • Genome wide screen

  47. Candidate gene analysis • Direct analysis: • Association studies between disease and functional SNPs (causative of disease) of candidate gene

  48. TagSNP Candidate gene analysis • Indirect analysis: • Association studies between disease and “random” SNPs within or near candidate gene • Linkage Disequilibrium mapping

  49. Yes No Cases n11 n12 n1. Controls n21 n22 n2. n.1 n.2 n.. Case-control studies: 2test Risk factor contingency table Test of independence: 2=  (O-E)2 / E with 1 df

  50. Case-control studies: 2test 2x3 contingency table Genotypes AA Aa aa Cases nAA nAa naa N Controls mAA mAa maa M tAA tAa taa N+M Test of independence: 2=  (O-E)2 / E with 2 df

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