Identifying targets of positive selection in non-equilibrium populations: The population genetics of adaptation

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1 Identifying targets of positive selection in non-equilibrium populations: The population genetics of adaptation Jeffrey D. Jensen September 08, 2009

2 From Popgen to Function As we develop increasingly sophisticated sequencing technology, as well as statistical methodology - a clear direction/challenge for computational/ theoretical popgen will be connecting predictions with function agronomic improvements medical genetics characterizing natural selection

3 Over view Understanding the genetic basis of cryptic coat color Characterizing the evolution of sex chromosomes Can we do better? METHODOLOGY

4 Adaptation in natural populations Is positive selection an important force in the evolution of natural populations? Is adaptive evolution characterized by the fixation of many weak or few strong beneficial mutations? Does selection commonly act on standing variation, or is there a distinct mutational limit on the rate? What phenotypic traits are targeted, and what is the mechanism of change?

5 Example #1: Peromyscus maniculatus, and the sand hills Deer mice have evolved a dorsal coat that closely matches the local habitat Previous results suggest that this is a likely adaptation to avian predation The Sand Hills are geologically young (10-15 kya), suggesting this may be a recent adaptation Linnen, Kingsley, Jensen, & Hoekstra, Science.

6 Its all about the band... Hairs from the dorsum of light mice have a wider band of pheomelanin than the wildtype Larger band area is significantly correlated with reflectance Because of its role in producing pheomelanin, Agouti is a strong candidate gene In Mus musculus, knockout results in dark-colored mice, and overexpression of Agouti result in light-colored mice Linnen, Kingsley, Jensen, & Hoekstra, Science.

7 Questions of interest Can we determine if selection has acted on this phenotype? And, if so: Can we identify the target of selection? Can we characterize the strength of selection? Can we determine the timing of selection, and relate it to the geographic history of the sand hills? Linnen, Kingsley, Jensen, & Hoekstra, Science.

8 Statistical tools These questions involve: determining between adaptive and non-adaptive forces shaping genomic variation quantifying the selection parameters: s (strength), X (target), and t (time) Linnen, Kingsley, Jensen, & Hoekstra, Science.

9 The Site Frequency Spectrum A tremendous amount of work has been focused on utilizing patterns in the SFS two common SFS-based tests of selection for single loci (CLRT & GOF), were utilized here: a SELECTION b MIGRATION P-value c = 0.1 t b = t b = t b = P-value d = 0.01 t b = t b = t b = P-value P-value Jensen, Kim, Bauer DuMont, Aquadro & Bustamante, Genetics.

10 Patterns of Linkage Disequilibrium More recent work has demonstrated that patterns of LD may hold greater promise for discriminating between models Building on the work of Stephan et al. (2006), we obtained some encouraging findings Kim & Nielsen (2004) and recently l + S l 1 r 2 2 ( ij + r ij 2 2 i, j L i, j R ) ω = 1/ l( S l) i L, j R ( ) r ij 2 Jensen, Thornton, Bustamante & Aquadro, Genetics.

11 Patterns of Linkage Disequilibrium (2) While we have good power to identify sweeps in equilibrium populations, others do as well What we find however, is power even to identify sweeps in severely bottlenecked populations - a very difficult task for SFS inference Jensen, Thornton, Bustamante & Aquadro, Genetics.

12 So, do we have evidence of selection? Comparing between the wildtype and light-colored allele classes: significant reduction in variation on light-colored allele significantly different SFS between classes significant SFS (CLR and GOF) and LD (omega) tests on light-colored allele (and not on the wildtype) Linnen, Kingsley, Jensen, & Hoekstra, Science.

13 Determining the target of selection A) correlation between genotype and phenotype B) SFS-based likelihood surface and arrow-indicated LD target prediction, relative to the AA deletion Both functional, as well as SFS and LD-based methods suggest a target at or near an exonic deletion Linnen, Kingsley, Jensen, & Hoekstra, Science.

14 From genotype to phenotype A) Quantative real-time PCR shows the light-colored allele expresses Agouti at a higher level than the wildtype B) Expression of Agouti transcript is significantly higher in light-colored mice from postnatal day 1 to 5 Linnen, Kingsley, Jensen, & Hoekstra, Science.

15 Characterizing the strength of selection Using our MLE s, 2Ns = 112. With an estimated Ne = 10,000, this corresponds to s = We also have (ill-advised) experiments with which to compare owl predation experiments in the 1940s, estimate that on light soil, light colored mice have a selective advantage of s = certainly an overestimate, given the enclosed arena tested... Linnen, Kingsley, Jensen, & Hoekstra, Science.

16 Characterizing the timing of selection And yet, one of the most interesting questions remains: did selection act on standing variation during the formation of the hills? did the hills form, and the species wait around for the right mutation? The Bayesian approach of Przeworski (2003), strongly suggests that the allele arose de novo Linnen, Kingsley, Jensen, & Hoekstra, Science.

17 Overview: coat color light coloration stems from a novel banding pattern on individual hairs produced by an increase in Agouti expression selection has acted on cis-acting mutation(s), which either is, or is closely linked to, a single amino acid deletion data suggests that this derived allele arose after the formation of the sand hills Linnen, Kingsley, Jensen, & Hoekstra, Science.

18 What have we learned from Peromyscus? Is positive selection an important force in the evolution of natural populations? Is adaptive evolution characterized by the fixation of many weak or few strong beneficial mutations? Does selection commonly act on standing variation, or is there a distinct mutational limit on the rate? What phenotypic traits are targeted, and what is the mechanism of change?

19 Example #2: The Evolution of Sex Chromosomes Y degeneration creates dosage problems for X- linked genes: mammals inactivate one X in females C. elegans halves the expression from each X male Drosophila increase transcription on the X two-fold, by recruiting dosage compensation enhancing proteins Bachtrog, Jensen & Zhang, PLoS Biology.

20 Drosophila miranda, and the neo-x chromosome X-L is part of the X in all Drosophila (60 mya) X-R is a fused former autosome (10 mya) the neo-sex chromosomes were formed by a very recent fusion of autosomes to the Y (1 mya) -thus, we may observe the active transition from autosome to sex chromosome Bachtrog, Jensen & Zhang, PLoS Biology.

21 Questions of Interest Given the recent fusion, can we identify selective pressures associated with this transition? More specifically, can we use popgen tools to quantify the predicted rapid acquisition of dosage compensation binding sites? Can we effectively utilize the X - neox comparison as a demographic control? Bachtrog, Jensen & Zhang, PLoS Biology.

22 Single locus results We can utilize the same single locus tools described in the Peromyscus analysis Summary and locus-by-locus statistics for the X and neo-x chromosomes of D. miranda. # loci Mean region length(bp) Mean sample size Number of invariant loci a Number of loci rejecting CLRT b Number of loci rejecting GOF c Number of loci rejecting max d X neo-x a excluded from further analysis b Kim and Stephan 2002 c Jensen et al d Kim and Nielsen 2004; Jensen et al Bachtrog, Jensen & Zhang, PLoS Biology.

23 From single sweeps to recurrent hitchhiking But here we are talking about recurrent beneficial mutations, and thus an important consideration is the timing of selective events: if rare, sweeps may be so old on average that they are not detectable from polymorphism data if common, there may be interference, or sweeps may be occurring on recently swept backgrounds - producing very different SFS patterns Hence, we want to be able to quantify the rate and strength of selection from poly data Jensen, Thornton & Andolfatto, PLoS Genetics.

24 SHH-RHH: the big picture Wiehe & Stephan (1993) predicted expected heterozygosity under a RHH model E(π ) = θr r +κγλ -thus, substitutions satisfying 2Nsλ = constant, have the same effect on variation Jensen, Thornton & Andolfatto, PLoS Genetics.

25 The prospect of RHH estimation Intuitively, scale may be key to solving this problem Jensen, Thornton & Andolfatto, PLoS Genetics.

26 An ABC based approach -With an ABC approach, it is computationally feasible to consider optimal combination of multiple summary stats -We found the means and SDs of π, S, θh and ZnS to result in highly accurate and robust estimation Jensen, Thornton & Andolfatto, PLoS Genetics.

27 Characterizing RHH in D. miranda X neo X log10(2nλ) log10(2nλ) log10(s) log10(s) strength of selection rate of sweeps density X neox density log10(s) log10(2nλ) Bachtrog, Jensen & Zhang, PLoS Biology.

28 Overview: sex chromosome evolution Selection has reduced diversity on the neox by 50% SHH analysis identifies many more targets on the neox RHH models estimate an order of magnitude stronger and more frequent selection on the neox Bachtrog, Jensen & Zhang, PLoS Biology.

29 What does this tell us about function? These results demonstrate an important principle of sex chromosome evolution: not only does the neoy degenerate, but the neox experiences pervasive adaptive evolution during the process of creating new genomic regions governing sexual differentiation Bachtrog, Jensen & Zhang, PLoS Biology.

30 What have we learned from D. miranda? Is positive selection an important force in the evolution of natural populations? Is adaptive evolution characterized by the fixation of many weak or few strong beneficial mutations? Does selection commonly act on standing variation, or is there a distinct mutational limit on the rate? What phenotypic traits are targeted, and what is the mechanism of change?

31 A caveat: SHH-RHH (2) Aquadro lab Harr et al Glinka et al Region size a # markers b Fraction swept c E(fraction) ~20% reduction d E(fraction) ~50% reduction e 256kb k kb l MB m Looking at published estimates, one unsettling pattern emerges - many more sweeps are being empirically identified than we would predict under existing RHH predictions Jensen, GBE.

32 Same biology, different results reduction in variation RHHbased estimation SHHbased estimation fraction of loci swept Jensen, GBE.

33 Possible explanations Reconciling these models, and understanding apparent discrepancies, is of paramount importance as we begin to analyze genomic polymorphism data: are recurrent hitchhiking estimators overly conservative? are most empirically identified sweeps false-positives? are these approaches capturing different tails of the true underlying genomic distribution? Jensen, GBE.

34 Previous results may be informative Thornton & Jensen, Genetics.

35 Demography & Ascertainment Type I = 63% CLRT GOF freq freq 0 p-value 1 0 p-value 1 But, we propose a relatively straightforward correction: Type I = 88% Type I = 4% freq freq 0 p-value 1 0 p-value 1 Thornton & Jensen, Genetics.

36 One obvious solution is better methodology methodology capable of dealing with demography and ascertainment while this is easy to say - one direction is incorporating multiple sweep predictions in to a single statistical framework Pavlidis, Jensen & Stephan. in review.

37 Equilibrium Selection weak selection strong selection selection bneck Both SFS and LD statistics have no problem at equilibrium Pavlidis, Jensen & Stephan. in review.

38 Equilibrium selection vs. non-equilibrium neutrality weak selection strong selection selection bneck And strong selection is readily identifiable from neutral bottlenecks Pavlidis, Jensen & Stephan. in review.

39 Non-Equilibrium selection Li&Stephan 2006 bottleneck Thornton&Andolfatto 2006 bottleneck selection bneck But selection remains difficult to identify within certain bottlenecked populations (but the situation is not hopeless!) Pavlidis, Jensen & Stephan. in review.

40 Collaborators & Funding Cornell Berkeley Harvard Munich UC Irvine Princeton C. Aquadro C. Bustamante D. Bachtrog R. Nielsen H. Hoekstra C. Linnen E. Kingsley W. Stephan P. Pavlidis K. Thornton P. Andolfatto Funding: National Science Foundation Biological Informatics Post-doctoral Fellowship

41 An advertisement The Jensen Lab will be founded at the UMass Medical School in January 2010! We will be making multiple faculty hires in the new Program in Bioinformatics & Integrative Biology, in: comparative genomics, statistical and population genetics

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