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Detecting selection using genome scans Roger Butlin University of Sheffield

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Nielsen R (2005) Molecular signatures of natural selection. Annu. Rev. Genet. 39, 197–218. What signatures does selection leave in the genome? 1.Population differentiation – today’s focus! 2.Frequency spectrum, e.g. Tajima’s D 3.Selective sweeps 4.Haplotype structure (linkage disequilibrium) 5.MacDonald-Kreitman tests (or PAML over long time-scales)

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From Nielsen (2005): frequency of derived allele in a sample of 20 alleles. Tajima’s D = (π-S)/sd, summarises excess of rare variants Frequency distribution:

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Selective sweep:

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Extended haplotype homozygosity (Sabeti et al. 2002)

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MacDonald-Kreitman and related tests dN = replacement changes per replacement site dS = silent changes per silent site dN/dS = 1 - neutral dN/dS < 1 - conserved (purifying selection) dN/dS > 1 - adaptive evolution (positive selection)

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Selection on phenotypic traits: QTL Association analysis Candidate genes

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Genome scans (aka ‘Outlier analysis’)

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‘H’ ‘M’ Thornwick Bay Littorina saxatilis – locally adapted morphs What signatures of selection might we look for?

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Signatures of selection: Departure from HWE Low diversity (selective sweep) Frequency spectrum tests High divergence Elevated proportion of non-synonymous substitutions LD

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Neutral loci

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Stabilizing selection

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Local adaptation

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Charlesworth et al (from Nosil et al. 2009)

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A concrete example: adaptation to altitude in Rana temporaria (Bonin et al. 2006) High – 2000m Intermediate – 1000m Low – 400m 190 individuals 392 AFLP bands

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Generating the expected distribution NeNe DetSel – Vitalis et al N0N0 N1N1 N2N2 t μ NeNe toto F 1,2 – measure of divergence of population 1,2 from population 2,1 Dfdist – Beaumont & Nichols 1996 N N N N N N N m F ST – symmetrical population differentiation, as a function of heterozygosity Does the structure/history matter?

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DetSelDfdist ‘Low 1’ vs ‘High 1’ 95% CI 95% 50 % 5%

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DetSelDfdistBothInterpretation Monomorphic in one population 35N/A Unreliable outliers Significant in one comparison 1429 False positives Significant in comparisons involving one population 311 Local effects Significant in at least 2 comparisons 231 Adaptation to altitude Significant in global comparison across altitudes 6 (2 at 99%) Adaptation to altitude 392 AFLPs, 12 pairwise comparisons across altitude or 3 altitude categories, 95% cut off

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8 loci343 loci

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Outliers and selected traits Coregonus clupeaformis (lake whitefish) Rogers and Bernatchez (2007): Dwarf x Normal cross both backcrosses Measure ‘adaptive’ traits (9) QTL map (>400 AFLP plus microsatellites) Homologous AFLP in 4 natural sympatric population pairs Outlier analysis (forward simulation based on Winkle) Homologous AFLP Outlier AFLP in homologous set* Outlier within QTL (based on 1.5 LOD support) Hybrid x Dwarf (3.6 expected, P=0.0015) Hybrid x Normal (0.5 expected, P=0.0002) *Only 3 outliers shared between lakes

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Roger Butlin - Genome scans21

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Nosil et al review of 14 studies: – 26% outliers, most studies 5-10% % outliers replicated in pair-wise comparisons % of outliers specific to habitat comparisons 4.No consistent pattern for EST-associated loci 5.LD among outliers typically low But many methodological differences between studies Population sampling Marker type Analysis type and options Statistical cut-offs

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Environmental correlations SAM – Joost et al IBA – Nosil et al F ST for each locus correlated with ‘adaptive distance’, controlling for geographic distance (partial Mantel test)

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Methodological improvements – Bayesian approaches BayesFst – Beaumont & Balding 2004 Bayescan – Foll & Gaggiotti 2008 Ancestral For each locus i and population j we have an F ST measure, relative to the ‘ancestral’ population, F ij Then decompose into locus and population components, Log(F ij /(1-F ij ) = α i + β j α i is the locus-effect – 0 neutral, +ve divergence selection, -ve balancing selection β j is the population effect Assuming Dirichlet distribution of allele frequencies among subpopulations, can estimate α i + β j by MCMC In Bayescan, also explicitly test α i = 0

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Apparently much greater power to detect balancing selection than FDIST Lower false positive rate Wider applicability

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Methodological improvements – hierarchical structure Arlequin – Excoffier et al. 2009

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Circles – simulated STR data, grey – null distribution

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Bayenv – Coop et al Estimates variance-covariance matrix of allele frequencies then tests for correlations with environmental variables (or categories). Software available at: Multiple analyses? Candidate vs control? E.g. Shimada et al. 2010

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Hohenlohe et al. 2010

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Mäkinen et al populations 3 marine, 4 freshwater 103 STR loci Analysed by BayesFst (and LnRH) 5 under directional selection (3 in Eda locus) 15 under balancing selection Used as a test case by Excoffier et al 2 directional 3 balancing

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Can we replicate these results? Bayescan Stickleback_allele.txt – input file Output_fst.txt – view with R routine plot_Bayescan Arlequin Stickleback_data_standard.arp – IAM Stickleback_data_repeat.arp – SMM Run using Arlequin3.5 Try hierarchical and island models, maybe different hierarchies

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Sympatric speciation? F ST distribution as evidence of speciation with gene flow Savolainen et al (2006) Howea - palms Cf. Gavrilets and Vose (2007) few loci underlying key traits intermediate selection initial environmental effect on phenology

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