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Simon Myers Department of Statistics, Oxford Recombination and genetic variation – models and inference.

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Presentation on theme: "Simon Myers Department of Statistics, Oxford Recombination and genetic variation – models and inference."— Presentation transcript:

1 Simon Myers Department of Statistics, Oxford Recombination and genetic variation – models and inference

2 What does recombination do to genetic variation? Informally, recombination shuffles up genetic diversity We can see the effect of recombination in how ‘structured’ genetic variation is Lipoprotein Lipase: 10kb 48 African Americans Chromosome 22: 1Mb 57 Europeans Xq13: 10kb 69 worldwide Chromosomes Sites

3 Human pairwise association revisited What is going on? Recombination causes the association breakdown Does the uneven pattern reflect –Chance? –Real strong differences in the underlying recombination rate in meiosis We will explore two approaches to find out Data for ENR131, Chromosome 2q, Chinese and Japanese population sample (The International HapMap Consortium, Nature 2005) LD

4 Recombination and genealogical history Forwards in time Backwards in time TCAGGCATGGATCAGGGAGCTTCACGCATGGAACAGGGAGCT Grandpaternal sequence Grandmaternal sequence TCAGGCATGGAACAGGGAGCT x GAGA GA Non-ancestral genetic material

5 The ancestral recombination graph The combined history of recombination, mutation and coalescence is described by the ancestral recombination graph Mutation Event Recombination Coalescence

6 Deconstructing the ARG

7 Time T(0)T(0)T(0.5)T(1)T(1)

8 Learning about recombination Just like there is a true genealogy underlying a sample of sequences without recombination, there is a true ARG underlying samples of sequences with recombination We can consider nonparametric and parametric ways of learning about recombination There are several useful nonparametric ways of learning about recombination which we will consider first –These really only apply to species, such as humans, where we can be fairly sure that most SNPs are the result of a single ancestral mutation event –This is formally called the infinite sites model

9 Why use a non-parametric approach? Non-parametric approaches require few assumptions about evolution The infinite sites model, and that’s it! We can attempt to learn features of the history of a sample based only on this assumption –Robust inference –Identify – “detect” the recombination events that shaped our sample –Clustering of multiple events in a region could signal a high underlying rate Some drawbacks to this approach

10 The signal of recombination? Recurrent mutation Recombination Ancestral chromosome recombines

11 Practical: detecting recombination from DNA sequence data Look for all pairs of “incompatible” sites Combine information across the pairs Find minimum number of intervals in which recombination events must have occurred (Hudson and Kaplan 1985): R m

12 Simon Myers Department of Statistics, Oxford Recombination and genetic variation – models and inference, part II

13 Example: 7q31 These results are based on a non-parametric minimum number of recombination intervals (events) R h Myers and Griffiths (2003) – improvement over R m but identical assumptions Results strongly suggest recombination “hotspots”

14 Example: humans vs. chimpanzees Winckler et al. (2005)

15 Why use parametric approaches? The infinite-sites model is not applicable to all species There are many more recombination events in the history of the sample than the non-parametric methods can ever detect –Lack of mutations in the right places –Recombination events completely undetectable HIV Subtype B (2kb segment)HIV Subtype C (2kb segment)

16 Modelling recombination Model-based approaches to learning about recombination allow us to ask more detailed questions than nonparametric approaches –What is the rate of recombination (as opposed to just the number of events) –Is the rate of recombination across a region constant? –Does gene A have a higher recombination rate than gene B? –What patterns of genetic diversity might I expect to see in other samples from the same (or different) population? We need a model!

17 Adding recombination to the coalescent Each generation, the probability of recombination between two loci is r, working in scaled time, this means that recombination occurs at rate  /2 per sequence where  = 4N e r Recombination, mutation and coalescence occur independently: –Coalescence occurs as a Poisson process with rate n(n-1)/2 –Recombination occurs as a Poisson process with rate n  /2 –Mutations on edges added as a Poisson process with rate n  /2 The time until the next recombination or coalescence event is also a Poisson process with rate n  /2+ n(n-1)/2, and the probability that this next event is a recombination is

18 Recombination in non-ancestral material Once a region has recombined, further recombination can occur in both ancestral lineages However, recombination in non-ancestral DNA cannot in anyway influence patterns of diversity (under a neutral model) We usually ignore such recombination events in the coalescent X X

19 Simulating histories with recombination www.coalescent.dk

20 Properties of the ARG Unlike the basic coalescent, there are few results about the effects of recombination on gene genealogies that we can derive analytically For example, we cannot even calculate the expected number of recombination events in the history of a sequence –Though we can show it is less than infinity! There are some useful results about how many recombination events we can see –The key is that only a small minority of recombination events that occur in the history of the sample can ever be directly detected by nonparametric methods  =10,  =10 against log log sample size

21 Estimating the population recombination rate The ideal inference procedure would calculate the likelihood of the data –Need to allow recombination rate to vary ….but full-likelihood inference is effectively impossible for anything but the simplest data sets (and models) We need alternatives –Calculate the probability of some summary of the data (like ABC) –Approximate the coalescent model –Approximate the likelihood The composite likelihood of Hudson (2001) approximates the likelihood of the full data by the product of the likelihoods for pairs of sites –Not the real likelihood! –Fast to calculate –Allows a variable recombination rate

22 Composite likelihood estimation of 4N e r: Hudson (2001) 15 7 1 2 7 2 4 2 3 1 Full likelihood Composite- likelihood approximation R  lnL R R

23 Fitting a variable recombination rate Use a reversible-jump MCMC approach (Green 1995) Merge blocksChange block size Change block rate Cold Hot SNP positions Split blocks

24 Composite likelihood ratio Hastings ratio Ratio of priors Jacobian of partial derivatives relating changes in parameters to sampled random numbers Acceptance rates Include a prior on the number of change points that encourages smoothing

25 Broad scale validation: strong concordance between rates estimated from genetic variation and pedigrees 2Mb correlation between “Perlegen” and deCODE rates (Myers et al. 2005)

26 Fine-scale validation: strong concordance between fine-scale rate estimates from sperm and genetic variation Rates estimated from sperm Jeffreys et al (2001) Rates estimated from genetic variation McVean et al (2004) 200kb region of human HLA In this region at least, human recombination clusters into 1-2kb wide hotspots >90% of recombination in 6 hotspots We have also developed a specific test for hotspots, based on the same composite likelihood (likelihood ratio test)

27 Fine-scale rates across the human genome Throughout the genome, human recombination clusters into narrow hotspots These explain LD breakdown sites Across chromosome12 Myers et al. (2005)

28 Data for ENR131, Chromosome 2q, Chinese and Japanese population sample (The International HapMap Consortium, Nature 2005)

29 Summary Both non-parametric and model based approaches allow us to ask detailed questions about recombination from population genetic data Recombination can be incorporated within the coalescent framework The population recombination rate,  =4N e r, is the key quantity in determining the effect of recombination on genetic variation Efficiently estimating recombination rates within a coalescent framework is difficult, but approximate methods have proved a powerful approach Such methods have allowed us to successfully learn about recombination rates in humans and other species, and reveal “hotspots” across genomes


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