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Tom Price MRC SGDP Centre, Institute of Psychiatry Linkage analysis and eQTL studies Systems Biomedicine Graduate Programme 2008/9.

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Presentation on theme: "Tom Price MRC SGDP Centre, Institute of Psychiatry Linkage analysis and eQTL studies Systems Biomedicine Graduate Programme 2008/9."— Presentation transcript:

1 Tom Price MRC SGDP Centre, Institute of Psychiatry Linkage analysis and eQTL studies Systems Biomedicine Graduate Programme 2008/9

2 Genetic Linkage Studies Use the inheritance of markers within families to identify chromosomal regions where disease genes may lie Disease susceptibility gene Genetic markers M7 M1 M2 M3 M4 M5 M6

3 Linkage Pedigree Random chance? Or linkage between marker and disease locus? 1 2 2 13 1 3 2 3 Disease cases Genotype

4 The Possibilities Mendelian One gene = one trait Cystic fibrosis Non-Mendelian Multiple genes and environment Epilepsy, liability to stroke Quantitative traits Multiple genes and environment Height SIMPLE COMPLEX CONTINUOUS DISCRETE Multiple alleles of a single gene Different alleles different effects Trinucleotide repeat diseases

5 Laws of heredity discovered by Mendel 1865 –Three laws of heredity One Gene, One Trait?

6 Mendels Laws 1.Dominance When two contrasting characters are crossed only one appears in the next generation 2.Segregation For each trait, a gamete carries only one of the two parental alleles 3.Independent assortment Alleles for different traits are inherited independently of each other

7 Dominance for Hair Colour

8 Mendels Laws 1.Dominance When two contrasting characters are crossed only one appears in the next generation 2.Segregation For each trait, a gamete carries only one of the two parental alleles 3.Independent assortment Alleles for different traits are inherited independently of each other

9 Segregation ABCD A C D D C D C C Parental Genotypes

10 Mendels Laws 1.Dominance When two contrasting characters are crossed only one appears in the next generation 2.Segregation For each trait, a gamete carries only one of the two parental alleles 3.Independent assortment Alleles for different traits are inherited independently of each other

11 Independent Assortment Eye colour IS NOT predictable from hair colour –Blonde hair and brown or blue eyes –Brown hair and blue or brown eyes

12 Mendels Laws 1.Dominance When two contrasting characters are crossed only one appears in the next generation 2.Segregation For each trait, a gamete carries only one of the two parental alleles 3.Independent assortment Alleles for different traits are inherited independently of each other

13 Independent Assortment Eye colour IS often predictable from hair colour –Blonde hair and blue eyes –Brown hair and dark eyes

14 What is Linkage? A method to map the relative positions of two or more loci using genetic markers –Occurs because loci do not obey Mendels third law

15 Breaking the Third Law A, B, O = blood group genes affected, unaffected Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

16 Breaking the Third Law A, B, O = blood group alleles affected, unaffected Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

17 Breaking the Third Law A, B, O = blood group alleles affected, unaffected ABO locus predicts D locus Adapted from Phillip McLean http://www.ndsu.nodak.edu/instruct/mcclean/plsc431/linkage/

18 Genetics for Card Players We can think of genetic information as a deck of cards. The closer 2 cards are, the less likely it is that they will separate during shuffling. If not much shuffling has occurred, more distant cards can act as markers.

19 Linkage Groups If inheritance of two loci is independent –They are unlinked If inheritance of two loci is dependent –They are in the same linkage group –Linkage groups correspond to the physical structures called chromosomes

20 Chromosomes Chromosomes are NOT inherited as a single block Recombination occurs at meiosis –Affects co-inheritance of alleles

21 Recombination and Meiosis Nearby loci A and B are likely to co- segregate during meiosis. Distant loci B and C are less likely to co- segregate during meiosis.

22 Recombination For any pair of markers –Parental pattern = NR –Mixed pattern = R AaBbAaBb cc dd AcBdAcBd AcBdAcBd AcbdAcbd acBdacBd NR NR R R ABAB abab Non-recombinant gametes

23 Recombination For any pair of markers –Parental pattern = NR –Mixed pattern = R AaBbAaBb cc dd AcBdAcBd AcBdAcBd AcbdAcbd acBdacBd NR NR R R AbAb aBaB Recombinant gametes

24 Recombination For any pair of markers –Parental pattern = NR –Mixed pattern = R AaBbAaBb cc dd AcBdAcBd AcBdAcBd AcbdAcbd acBdacBd NR NR R R

25 Recombination Fraction = The proportion of offspring that are recombinant between two loci RF = 0.5 between unlinked loci (e.g. different chromosomes)

26 Parametric Linkage Analysis Uses pedigree information to estimate recombination fraction between markers and disease Assumes a particular model of inheritance (additive, dominant, recessive) Useful for Mendelian disorders (single gene)

27 Allele Sharing People with rare diseases are more highly related to each other near the disease-causing gene than you would typically expect. This is because nearby markers tend to be inherited together with the disease locus. We can look for excess allele sharing as a signal that a disease locus is nearby.

28 Identity By State When two individuals possess the same alleles at a locus, they are said to be identical by state (IBS). For example, these affected sibs share one allele IBS, the allele a. adac

29 Identity By State But if the parental genotypes are unknown, we do not know whether the offspring have inherited the a allele from the same parent or from different parents. We cant established shared inheritance, so IBS allele sharing is useless for linkage analysis. adac ??

30 Identity By Descent Individuals who share copies of a common ancestral allele are said to be identical by descent (IBD). For example, these affected sibs share one allele IBD. The paternal allele a has been transmitted to both offspring. abcd adac

31 Allele Sharing in Affected Sib Pair abcd ??ac Sibling genotypes Alleles shared IBD Expected Probability ac 2¼ ac ad1½ ac bc1½ ac bd0¼

32 Allele Sharing in Affected Sib Pair abcd ??ac Sibling genotypes Alleles shared IBD Expected Probability ac 2¼ ac ad1½ ac bc1½ ac bd0¼ Probability under random transmission of marker alleles.

33 Allele Sharing in Affected Sib Pair abcd ??ac Sibling genotypes Alleles shared IBD Expected Probability ac 2¼ ac ad1½ ac bc1½ ac bd0¼ Probability under random transmission of marker alleles. But what if the marker lies near a disease gene? Affected siblings are more likely to share marker alleles IBD.

34 Non-parametric Linkage Analysis Uses information on IBD allele sharing –Usually between affected sibs Do not need to specify the model of inheritance at any locus Useful for complex traits (multiple genes, different modes of inheritance)

35 Linkage Statistic for Affected Sib Pairs Alleles IBD012 Expect0.250.500.25 ObservedZ 0 Z 1 Z 2 Under linkage

36 Linkage Statistic for Affected Sib Pairs Alleles IBD 012 Expect0.250.500.25 ObservedZ 0 Z 1 Z 2 Under linkage Suppose x families share 0 alleles IBD, y families share 1 allele IBD, z families share 2 alleles IBD. Under a multinomial model, the expected probability of the marker data Z 0, Z 1, Z 2 assuming no linkage is P( Z 0, Z 1, Z 2 ) = x! y! z! 0.25 x 0.5 y 0.25 z (x+y+z)!

37 Linkage Statistic for Affected Sib Pairs Alleles IBD012 Expect0.250.500.25 ObservedZ 0 Z 1 Z 2 Under linkage Suppose x families share 0 alleles IBD, y families share 1 allele IBD, z families share 2 alleles IBD. LOD = log 10 P(marker data given estimated sharing Z 0, Z 1, Z 2 ) P(marker data given sharing 0.25, 0.5, 0.25) = log 10 Z 0 x Z 1 y Z 2 z 0.25 x 0.5 y 0.25 z

38 Example: 200 ASPs Sharing among 200 affected sibling pairs 012 Observed sharing369074 Expected sharing 5010050 Z 0 = 36/200 = 0.18 Z 1 = 90/200 = 0.45 Z 2 = 74/200 = 0.37 Recall: baseline values 0.25 0.5 0.25

39 Example: 200 ASPs Sharing among 200 affected sibling pairs 012 Observed sharing369074 Expected sharing 5010050 Z 0 = 36/200 = 0.18 Z 1 = 90/200 = 0.45 Z 2 = 74/200 = 0.37 LOD = log 10 0.18 36 0.45 90 0.37 74 0.25 36 0.5 90 0.25 74 = 3.35STRONG EVIDENCE FOR LINKAGE Recall: baseline values 0.25 0.5 0.25

40 Complications of Linkage Analysis With unknown parental genotypes, allele sharing must be estimated using population allele frequencies Families with less than four alleles may give unclear sharing Multipoint linkage analysis, using information from adjacent markers, will increase power to detect genes Computationally intensive: use computer programs to calculate LOD scores Other problems due to non-paternity, genotyping errors, sample mix-ups, poor phenotype definition ?? abcd

41 Software Several programs are available, including: –Parametric: LINKAGE MLINK –Non-parametric: MERLIN GENEHUNTER

42 Linkage Study Design Candidate gene search: dense marker genotyping within a region of positional or functional interest Genome search: -Aim to identify several susceptibility genes Families are genotyped on polymorphic markers across all chromosomes 300-400 microsatellite markers across genome, separated by 10cM (or, more recently, 10,000 SNP markers) –tighter marker spacing gives more information –few markers makes it difficult to reconstruct haplotypes, particularly without parental genotypes

43 Significance Level Lander and Kruglyak (1994) suggested criteria for affected sibling pair studies in complex diseases LOD score > 2.2suggestive linkage LOD score > 3.6significant linkage These LOD scores are expected to occur by chance in 1 and 1/20 times in a genome search, respectively Many studies of complex disease do not reach these cut-offs Another approach is to report highest LOD scores even if they are below these thresholds and look for replication across studies

44 Does It Work? Very powerful for mapping single gene disorders, e.g. early-onset Alzheimers Disease, many forms of mental retardation…

45 Does It Work? Very powerful for mapping single gene disorders, e.g. early-onset Alzheimers Disease, many forms of mental retardation… …but many non-replications for complex traits

46 Linkage v Association LinkageAssociation Usual sample FamiliesUnrelated individuals (e.g. case control) Good for finding Rare variants with large effects Common variants with small effects IdentifiesBroad chromosomal region Narrow region usually within a single gene

47 Break Next up: application of linkage analysis to gene expression phenotypes.

48 Central Dogma DNA mRNA protein

49 Finding Disease Pathways 1.Conduct linkage/association study to find candidate 2.Determine candidate gene function experimentally Problems: Markers only give regional information, the identity of the causal variations remains obscure Many GWAS hits are nowhere near any genes Reliance on animal and in vitro models to probe function

50 Genetics of Gene Expression Linkage study or GWAS using mRNA abundance as the phenotype Motivation: mRNA abundance as endophenotype –Lies on causal path between genetic variation and disease Hits (eQTLs) may have less complex inheritance –Larger effect sizes, fewer causal variants? We may already know which transcripts are dysregulated in diseased tissues –eQTLs can provide a link to finding susceptibility genes

51 The First eQTL Study

52 Cis Regulation Genetic variation near the gene locus that influence its expression What we think of as functional polymorphisms fall into this category

53 Trans Regulation Genetic variation away from the gene locus that influence its expression e.g. polymorphisms in hub genes that act as master regulators C A B E D

54 Microarray Experiment

55 Human eQTL Studies 1 st AuthorYearJournalPopulationSampleTissueMeasureGenotyping Morley2004NatureCEPH14 pedigreesLCL8K Affy Linkage scan Monks2004AJHGCEPH15 pedigreesLCL25K oligo Linkage scan Dixon2007Nat GenMRC-A206 familiesLCL54K Affy Linkage scan Goring2007Nat GenSAFHS1240 individualsLympho- cytes 47K Illumina Illumina 100K Stranger2007ScienceHapMap270 individualsLCL47K Illumina 2M SNPs + 7K CNVs Emilsson2008NatureIFB/IFA1002/673 individuals Blood/ Adipose 25K oligo Illumina 370K + Linkage scan Selected list

56 Largest human eQTL study to date 1,240 subjects from extended pedigrees Blood lymphocytes, not lymphocyte cell lines 47K Illumina WG-6 Series I microarray Expression adjusted for age, sex

57 Heritability 85% of 19,648 transcripts detected were heritable (FDR 5%)

58 Cis Regulation Single LOD score calculated at gene locus to identify cis-regulated transcripts 1,345 (6.8%) cis-regulated transcripts detected (FDR 5%) eQTL effect size overall: median 1.8%, mean 5.0% eQTL effect size in significant loci: median 24.6%, mean 29.1%

59 Trans Regulation Much lower power No evidence of master regulators: only 58 transcripts had 2+ peaks with LOD > 3

60 Gene Discovery Using eQTLs Promoter variants in VNN1 are associated with transcript abundance and HDL-C concentration

61 Consistency of Results Morley cis eQTLs confirmed by Göring, but not trans eQTLs. This is consistent with tissue specificity of trans regulation, but also with lower power to detect trans effects.

62 Linkage & association study Icelandic subjects Blood and adipose tissue samples Expression adjusted for age, sex, BMI

63 Tissue Specificity Linkage eQTLs (FDR 5%) for 20,877 expression traits, 10,364 of them heritable (FDR 5%) CisTrans Blood2,52952 Adipose1,48925 Both762?

64 Proximity of Cis Acting Variants Association eSNPs were within 100kb of the probe for 96% of expression traits with strong cis-acting effects

65 Potential Problem Microarray probes overlapping SNPs can give rise to spurious cis eQTLs Older studies did not have so much resequencing data available to identify probes containing SNPs

66 Further Directions Animal models (e.g. mouse F2 crosses) Other tissues (e.g. mouse brain) Evoked phenotypes –genetics of expression response to e.g. ionizing radiation, drug/hormone treatment Causal modelling –Genotype data can establish whether expression changes cause disease or are a consequence of it Schadt et al. (2005) Nature Genetics 37: 710-717.

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