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Large-scale recombination rate patterns are conserved among human populations David Serre McGill University and Genome Quebec Innovation Center UQAM January 2006. Recombination rate patterns in Humans. Recombination? Why is recombination important? How do we measure recombination rates?
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Large-scale recombination rate patterns are conserved among human populations David Serre McGill University and Genome Quebec Innovation Center UQAM January 2006
Recombination rate patterns in Humans • Recombination? • Why is recombination important? • How do we measure recombination rates? • Large scale recombination rate differences among human populations
Why Studying Recombination ? Recombination separates history of two loci located on same chromosome.
Recombination and disease mapping n+1 generation
Recombination and natural selection Sabeti et al, 2002 Neutral locus Locus under selection Hitchhiking effect Low recombination rate Independent evolution High recombination rate
Understanding Recombination… Population history Genetic map Recombination Linkage Disequilibrium Sperm typing Direct Indirect
Genetic maps How often do we observe a recombination event between two markers ? Divide recombination fraction by physical distance (bp) recombination rate - Marshfield map Yu et al., 2001 - DeCode map Kong et al., 2002 Kong et al., 2004
Sperm typing A B a b How often do we observe a recombinant spermatozoid ? Divide recombination fraction by physical distance (bp) recombination rate Papers from Alec Jeffreys and Norm Arnheim
Linkage Disequilibrium (LD) “non-random association of alleles” If Pr(AB) ≠ P(A)*P(B) Then markers are in LD Random-association no linkage linkage “equilibrium” Non-random association linkage linkage disequilibrium
Measures of LD If Pr(AB) ≠ P(A)*P(B) then markers are in LD |D’|: D’= p(AB) – p(A)p(B) influenced by allele frequency R2: R2=(p(AB) – p(A)p(B))2 / (p(A)p(B)p(a)p(b)) normalized chi-squared distributed
Inferring Recombination from LD Polymorphism data Measure LD (Given a model of population history) Estimate recombination: ρ=4Nec
neutral growth structure Pritchard and Przeworski, 2001
From LD to Recombination rates LD reflects the number/location of historical recombination events. For a given population size, A given population history, What is the most likely recombination rate that would produce the observed LD pattern? Hudson 2001, McVean 2004
Recombination variations in Humans • Fine-scale variations (<100kb) • Individual variation in hotspot intensity (sperm typing) • (Jeffreys 1998, 2002, 2005) • Inter-species variation in hotspot intensity and/or location (LD) • (Wall 2003, Ptak 2004, 2005, Winckler 2005) • Population differences in hotspot location and/or intensity ? • (Crawford 2004)
Recombination variations in Humans • Fine-scale variations (<100kb) • Individual variation in hotspot intensity (sperm typing) • (Jeffreys 1998, 2002, 2005) • Inter-species variation in hotspot intensity and/or location (LD) • (Wall 2003, Ptak 2004, 2005, Winckler 2005) • Population differences in hotspot location and/or intensity ? • (Crawford 2004)
Recombination variations in Humans • Fine-scale variations (<100kb) • Individual variation in hotspot intensity (sperm typing) • (Jeffreys 1998, 2002, 2005) • Inter-species variation in hotspot intensity and/or location (LD) • (Wall 2003, Ptak 2004, 2005, Winckler 2005) • Population differences in hotspot location and/or intensity ? • (Crawford 2004)
Recombination variations in Humans • Large-scale variations (>1Mb) • Individual variation in number of recombination events per meiosis • (Yu 1996, Broman 1998, Kong 2002, 2004) • Inter-species variation in genetic map length • (Rogers 2000)
Recombination variations in Humans • Large-scale variations (>1Mb) • Individual variation in number of recombination events per meiosis • (Yu 1996, Broman 1998, Kong 2002, 2004) • Inter-species variation in genetic map length • (Rogers 2000) “The total centimorgan distances among homologous markers are 28.0% longer in the human genome than in the baboon, suggesting that rates of recombination may be higher in humans.” (Rogers et al. 2000)
Recombination variations in Humans • Fine-scale variations (<100kb) • Individual variation in hotspot intensity (sperm typing) • (Jeffreys 1998, 2002, 2005) • Inter-species variation in hotspot intensity and/or location (LD) • (Wall 2003, Ptak 2004, 2005, Winckler 2005) • Population differences in hotspot location and/or intensity ? • (Crawford 2004) • Large-scale variations (>1Mb) • Individual variation in number of recombination events per meiosis • (Yu 1996, Broman 1998, Kong 2002, 2004) • Inter-species variation in genetic map length • (Rogers 2000)
Recombination variations in Humans • Fine-scale variations (<100kb) • Individual variation in hotspot intensity (sperm typing) • (Jeffreys 1998, 2002, 2005) • Inter-species variation in hotspot intensity and/or location (LD) • (Wall 2003, Ptak 2004, 2005, Winckler 2005) • Population differences in hotspot location and/or intensity ? • (Crawford 2004) • Large-scale variations (>1Mb) • Individual variation in number of recombination events per meiosis • (Yu 1996, Broman 1998, Kong 2002, 2004) • Inter-species variation in genetic map length • (Rogers 2000) • Are large-scale recombination patterns conserved among human populations ?
Materiel & Methods • Used genome-wide polymorphism data from Perlegen Sciences (Hinds 2005) • re-sequencing followed by genotyping • >1,500,000 SNPs (average: one SNP per 1.8kb) • 23 individuals from 3 populations • - European-Americans (Utah residents with ancestry from northern and western Europe), • - African-Americans, • - and Han Chinese (from the Los Angeles area).
Materiel & Methods • For each population, • split genome in 1Mb-window • for each window, • if more than 100 SNPs, calculated ρcl=4Nec using Hudson 2001 • I considered only window for which I obtained ρcl in each population: • 2,613 non-overlapping windows of 1Mb covering the 22 autosomal chromosomes
Is ρ more strongly correlated with genetic map estimate in Eur.-Am.? • Kong (2002) estimated recombination rates using 146 Icelandic families. • If the recombination rate patterns differ between human populations, the DeCode estimates should predict better the population recombination rates in populations of European ancestry than they do in populations of non-European ancestry. • N.B. the slope of the correlation is proportional to Ne and will therefore differ for different populations but the strength of the correlation (i.e. Pearson’s r) will allow detecting differences in the recombination patterns. • Performed a linear regression analysis between: • the recombination rates estimated from polymorphism data (ρ) • and those obtained in the DeCode map (cmap) • for 2,609 non-overlapping 1Mb windows.
Is ρ more strongly correlated with genetic map estimate in Eur.-Am.?
Is the ratio ρ/ρconstant along the chromosomes? • Population recombination rate: ρ=4Nec • If c is identical in different populations, • ρ(pop1)/ρ(pop2) = Ne(pop1)/Ne(pop2) = constant • Thus, if the patterns of recombination rates are conserved, the ratios of the population recombination rate estimates should be constant along the chromosomes. • (this is true if the variations of Ne along the chromosomes are negligible at the scale considered)
Looking at the most extreme ρ/ρ ratios… • Looked at the 2.5% highest-2.5% lowest ratios of each comparison • - no significant deviation from a uniform distribution across chromosomes • - distribution in centromeric, telomeric and rest of the genome is marginally significant.
Looking at the most extreme ρ/ρ ratios… • Is there any biological outliers? • Selected regions with 2+ extreme windows within 3Mb (and in the same direction) • The region with the most extreme ratios corresponds to a well-described • polymorphic inversion on chromosome 8p (Giglio 2001) African-American/HanChinese 6 2 European-American/HanChinese 6 7 1 African-American/European-American