07 - January 29, 2013.docx

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University of Toronto St. George
Molecular Genetics and Microbiology
Johanna Rommens

MGY470 – lecture 07 – January 29, 2013 Polymorphism – understand genetic contributors to disease Used in human genetic studies – using phenotype data – Mental retardation more common in males than females – suggested some genes involved are on the X chromosome – why not Y??? Differences in DNA sequence between individuals Classical definition of polymorphism – population geneticists define it as greater than 1% Molecular biologists define it differently – refer to DNA sequence variation with NO FUNCTIONAL EFFECT ENCODE project – 80% of human genome has functional consequences – most of genome is Allele frequency less than one percent – ex. Common mutation in cystic fibrosis gene delta F508 – one called mutation, others call polymorphism Classify genetic variation/markers – based on how many alleles Microsatellite, minisattellite, CNV – from left to right increasing in size Classification not entirely correct – some SNP have two alleles – in tehroy, for single base pair, four alleles – most SNPs biallelic CNV – in both categories SNPs – very old – rare de novo mutations – inherited from parents mostly Database of SNPs – dbSNP – part of NCBI – dbSNP – collect subissions – identify the ones that are identical so all given same name Human genome diversity project Genotype and allele frequencies – Observe unordered genotype – cannot distinguish AS from SA – do not know parental origin of S and A Genotype data given can be used to describe allele – A allele is twice of 189 and 89 Diploid individuals Minor allele is S – minor allele frequency is the 0.19 number Allele frequency of bialleic marker Samples from populations illustrate variation in allele frequency at particular site of particular populations Ethiopians – minor alle is the plus allele In P Indians – plus allele has frequency of 82% - variation in allele frequency causes major problems in genetic studies – type 2 diabetes – selected cases from Pimas where prevalence of this is high and compared to Ethiopians without diabetes – see massive difference in alelel frequency at marker not because it’s associated with diabetes, but because the two populations have differences in the marker – in population studies, need controls that are ethnically a match Haplotype map 15 million common SNPs in human genome – which snp associated with particular disease – select subset of markers with genetic variation – put on microarray chips – used for genotyping large samples of controls – idea behind haplotype map QC is quality control criteria Phases 1 and 2 Samples are trios (two parents, 1 child) Phase three Exapand number of individuals, diversity Twenty to twenty-fivea djacent snips – four different haplotypes – formed by DNA To distinguish these four haplotypes, do not genotype the SNPS – genotype just select SNPS First SNP can distinguish haplotype three from the other three haplotypes Second SNP – distinguish two and four from one and three Using these three SNPs – can distinguish the four haplotypes SNPs – DNA sequence data from one particular gene – polymorphisms Gene is roughly 15kb in length, 20-25 SNPs within the gene Colour indicates genotype at each SNP Blue is homozygous for common allele Yellow is homozygous for rare allele Genetic variation that are close together tend to be correlated in the population – in this figure, correlations difficult to see Four to seven from the right – pattern Cluster the SNPs in bins – shift the columns – five bins within the data and within the bin, genotypes across SNPs are very similar – person is heterozygous at one SNP in a bin, can predict person is heterozygous at the other SNPs within the same bin Hap map project – select SNps for genotype that determines bins – so genotype ONE SNPs per bin – test for association – Uses of Hapmap Goal to identify causal genes Association Compare genotypes or al
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