Outline. Gene clusters in comparative genomics: Accident or design? New genes come from... Evolution of vertebrate genomes

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1 Gene clustes in compaative genomics: Accident o design? Dannie Duand Compute Science, Biological Sciences Canegie Mellon Univesity Outline Vetebate genome evolution Tests fo gene clusteing An application: Evolution of the insulin/igf1 signalling pathway Open poblems Evolution of vetebate genomes New genes come fom... ~15,500 genes? agcgagcctgagcactcgaggcatctctgcacattcagcatgggatgggcctcctgtccctgtatgcgcctgatga duplication 23,000-40,000 genes aggcctcgcctctcccagcatgggctggggctcctgtccctgtatgcg.cactgatatgggctggggctcctgtcccgc jawed vetebates followed by mutation. aggaggccctctcccagcatgggatgggcctcctgtgactgtatgcg. cactgatatgcgctgatgctcctgttgcgta An example: linking gene duplication to limb development tbx2/3/4/5 Tbx4 and Tbx5 have a ole in limb specification and pattening -- tbx4 Expession patten Gibson-Bown et al, 96; Chapman et al., 96; Gibson- Bown et al, 98; tbx2/3 tbx4/5 tbx2 tbx3 tbx4 tbx5 -- tbx5 -- tbx4 & tbx5 toes finges & toes finges finges toes Agulnik et al, 1996 Misexpession studies Ectopic limb induction 1

2 Linking tbx duplication to limb duplication one gene, no fins Ruvinsky et al, 2000, Ruvinsky & Gibson-Bow 2003 two genes, two pais of limb buds Evolution of vetebate genomes ~15,500 genes Two whole genome duplications occued ealy in the vetebate lineage Gene duplication dives developmental innovation in vetebates Ohno, ,000 40,000 genes jawed vetebates Pedictions of Ohno s Hypothesis Tempoal: An excess of duplicated genes oiginated befoe the emegence of bony fish (~500MYA) Spatial: Regions that shae significant similaity in gene content and ode. Genome duplication and divegence An Example: A B C D E F P Q R S T U V A B C D E F P Q R S T U V Whole genome duplication Genome duplication and divegence An Example: Genome duplication and divegence An Example: A B C D E F P Q R S T U V A B C D E F P Q R S T U V P Q R S T U V A B S T U V P Q R C D E F A B C D E F Recipocal tanslocation Recipocal tanslocation 2

3 Conseved Segments Genome duplication and divegence An Example: A B C D E F P Q R S T U V A B C E D F P Q R S T YU V A B S T U V P Q R C D E F A B S T U V P Q R C X D E F Distinct chomosomal egions with identical gene content and ode. Local mutations Small invesions Deletions Insetions Gene Clustes Local Spatial Evidence A B C E D F P Q R S T YU V Chomo some 5 Chomos ome Cyba4 Cybb1/2/3 4 0 A B S T U V P Q R C X D E F Lhx5 Tcf1 Tbx3/5 Nos1 Tcf2 Cyba1, Nos2 Lhx1 Tbx2/ Vetebate gene clustes discussed in the liteatue TBOX MHC Cyb, Lhx, Nos, Tbx, Tcf, Pka Abc, C3/4/5, Col, Hsp, Notch, Pbx, Psmb, Rx, Ten HOX Ach, Ccnd, Cdc, Cdk, Dlx, E Evx, Gli, Hh, Hox, If, Inhb, Nh, Npy/Ppy, Wnt Distinct chomosomal egions with simila gene content. Gene content and ode ae not peseved. Ruvinsky and Silve, Gene, 97 FGR MATN Eya, Hck, Mat Myb, Myc, Sdc, Sc Ad, Ank, Eg, Fgf, Pa, Vmat, Lpl Spatial Evidence : Genome scale study of paalogous egions Paalogon Identification d 30 Identify paalogous genes Stingent standads educed data set fom 20,800 to 9,500 aa sequences Find candidate duplicate egions ( paalogons ) Test signficance of candidate paalogons. McLysaght, Hokamp, Wolfe, Nat Ge 2002 Compaed blastp hits with those of neighboing poteins, scanning them fo matches within the same emote chomosomal location" Gap size: maximum numbe (d) of unduplicated genes allowed between two duplicated genes in each paalogon. McLysaght,Hokamp, Wolfe, Nat Ge

4 Spatial Evidence : Genome scale study of paalogous egions Identify paalogous genes Stingent standads educed data set fom 20,800 to 9,500 aa sequences Find candidate paalgous egions ( paalogons ) Test signficance of candidate paalogons. Significance Testing McLysaght,Hokamp, Wolfe, Nat Ge 2002 Monte Calo hypothesis testing Shuffled the mapping fom sequences to loci Seached fo paalogons Compaed the numbe of paalogons containing m paalogs in the obseved data and the shuffled data McLysaght,Hokamp, Wolfe, Nat Ge 2002 Significance Testing McLysaght,Hokamp, Wolfe, Nat Ge 2002 Conclusions McLysaght,Hokamp, Wolfe, Nat Ge 2002 m O S O: Obseved data S: Shuffled data Z: Zscoe (st.dev.) Z Human genome was geneated by a patten of lage scale duplication. Obseved paalogons ae consistent with one whole genome duplication do not show stong suppot fo two ounds any paalogon with m 6 was vey likely to have been fomed by a single duplication of a chomosomal egion Questions about Gene Clustes Is a paticula cluste statistically significant? Evidence concening a paticula egion. Is the obsevation of k clustes in a whole genome compaison significant? Evidence concening the pocesses that contibuted to the evolution of the genome. Outline Vetebate genome evolution Tests fo gene clusteing An application: Evolution of the insulin/igf1 signalling pathway Open poblems 4

5 Tests fo Gene Clusteing Duand and Sankoff, JCB, 2003 Individual Gene Clustes Individual clustes: Is a paticula gene cluste signficant? Aggegate clustes counts: Is it significant to obseve k clustes? Othologous compaisons: Two diffeent genomes Paalogous compaisons: Genome self-compaison Null hypothesis: Random gene ode Altenate hypotheses: Evolutionay histoy Functional selection Refeence egion Window sampling Whole genome scans The significance of a cluste depends on how you found it Refeence Region: A simple two paamete model Window sampling Given:a egion of inteest containing m genes, is it significant to find m duplicates in a window of size? Window sampling Whole genome scans m Fo each un of m consecutive genes, what is the pobability of finding the same genes in a window of size elsewhee? 5

6 Individual Gene Clustes Individual cluste significance Refeence egion Window sampling Whole genome scans O(n) O(1) O( n 2 ) Possibilities consideed Conseved ode Gene families Patial clustes Multiple copies The significance of a cluste depends on how you found it Refeence Region Genome: G = 1,, no gene families (initially). Event: obsevation of m genes in a window of size at most in G. q( ) = ( n ) 1 ( ) + ( ) m 1 m n ( m) Refeence Region conseved ode q( ) = 1 m! ( n ) 1 ( ) + ( ) m 1 m n ( m) Example: M = { }, m = 5, = 10 Example: M = { }, m = 5, = 10 Individual cluste significance Gene Families Possibilities consideed Conseved ode Gene families Patial clustes Multiple copies Given:a efeence egion containing m genes, is it significant to find m paalogs in a window of size? 6

7 Gene families: Expected numbe of clustes Given a set M of m pe-specified genes, each gene, j, has, f(j) copies in G thee ae Expected numbe of clustes, S F Fo fixed gene family size, f, m S ( = f q( F = j Φ( M ) f ( j) M ( = Φ( M ) q( sets of genes that match M. Gene Families: Pobability of at least one cluste Event E i : obsevation of the ith set of paalogs homologous to M. P M = { } U E i) = P( Ei ) P( Ei, j ) + P( Ei, j k ) L (, f m q( f m 1 f q( m + 1,2 m + 1) 2 Individual cluste significance Possibilities consideed Conseved ode Gene families Patial clustes Multiple copies Patial clustes Given:a efeence egion containing m genes, is it significant to find a subset of m paalogs in a window of size? Thee out of five genes ae found in a window of size 10 Individual cluste significance Multiple copies Possibilities consideed Conseved ode Gene families Patial clustes Multiple copies chomosome i chomosome j chomosome k 7

8 Individual cluste significance Possibilities consideed Conseved ode Gene families Patial clustes Multiple copies Window sampling No gene families Given windows W1 and W2 of size dawn fom genomes G1 and G2, the pobability that they shae at least m genes is: q( = ( ) ( i n ) i= m i n Window sampling With gene families Given windows W1 and W2 of size dawn fom genomes G1 and G2, the pobability that they shae at least m distinct gene families is: P1 ( k, P2 ( k, whee P1() is the pobability that thee ae k distinct gene families in W1 and P2() is the pobability that at least m of those k families ae epesented in W2. k = m Tests fo individual gene clustes Event: Given a set M of m pe-specified genes, h < m ae found in k windows of size at most in a andom genome with gene families. Tests: Expected numbe of clustes Pobability of obseving a least one cluste Also: tests fo clustes found though Window sampling Whole genome scans Example: Significance of TBX cluste Expected numbe of gene patial clustes: S FH m h ( h, = ( f 1) q( n, h, h Example: Significance of TBX cluste Expected numbe of patial gene clustes: S FH m h ( h, = h ( f 1) q( n, h, Refeence chomosome 5 15 genes Refeence chomosome 5 47 genes n = 2888, f = 3 m = 15, h = 6, =48 n = 2888, f = 3 m = 47, h = 7, =65 SFH (2888,6,15,48,3) = S ( 2888,7,47,65,3) = Ruvinsky and Silve, 1997 Ruvinsky and Silve,

9 Example: Significance of TBX cluste Tests fo whole genome compaison Expected numbe of clustes Expected numbe of clustes Aggegate clustes counts: Is it significant to obseve k clustes? family size family size Tests: Whole genome compaison: Expected numbe of paied clustes Window sampling: Given a paticula sample of window pais, Expected numbe of paied clustes in sample. Pobability of obseving a least one cluste in sample Outline Vetebate genome evolution Tests fo gene clusteing An application: Evolution of the insulin/igf1 signalling pathway Open poblems An example: evolution of insulin The insulin family egulates metabolis gowth, cell diffentiatio aging. Reduced insulin signalling in inceases life-span in fly, wom and mouse. We must undestand the compaative evolution of the insulin family in vetebates and invetebates to know the elevance of invetebate models fo human aging. Tata, Batke and Antebi, Science, 2003 Invetebate Insulin Signalling Mammalian Insulin Signalling Many ligands, one ecepto Gowth and metabolism ae coupled Thee ligands (Ins, IGF1, IGF2) Fou eceptos (IR, IGF1R, INSRR) Gowth and metabolism ae decoupled Insulin -like peptides Insulin Gowth Facto (IGF) Insulin ecepto IGF ecepto 9

10 Spatial evidence fo lage-scale duplication of insulin family genes Spatial evidence fo lage-scale duplication of insulin family genes INS IGF2 IR INS IGF2 IR INSRR IGF1 IGF1R INSRR IGF1 IGF1R Ae the insulin family genes in duplicated egions? Spatial evidence fo lage-scale duplication of insulin family genes INSRR INS IGF2 IGF1 IGF1R No! Only INSRR was found in the MHW paalogon data set. IR Spatial evidence fo lage-scale duplication of insulin family genes Cuent spatial evidence does not suppot lage-scale duplication in vetebate insulin evolution. Cuent spatial evidence does not falsify lage-scale duplication in vetebate insulin evolution. State of human gene finding Many genes wee excluded fom analysis Only paalogons of size 6 ae included in data set. Statistical analysis taget at egions suounding insulin genes is needed! Paalogon statistics m d( m 1) + m Outline Vetebate genome evolution Tests fo gene clusteing An application: Evolution of the insulin/igf1 signalling pathway Open poblems These two clustes ae teated identically! 10

11 Including additional infomation Gene oientation Inton/exon stuctue, flanking egions, Age Gene Clusteing fo Functional Infeence in Bacteial Genomes t1 t2 t3 t4 The Use of Gene Clustes to Infe Functional Coupling, Ovebeek et al., PNAS 96: , Incopoating evolutionay histoy in the null hypothesis Modeling ovelapping clustes How to choose the window size to fit the data? Individual clustes: Bette estimates of the pobability of obseving at least one cluste Aggegate cluste counts: p-values Distibution of P(obseving k clustes) vesus d A window sampling model fo thee o moe clustes Bette models of gene families mouse Exact gene families sizes Each gene, j f(j) copies in Had to calculate Fixed gene family sizes j, has copies in G fission yeast Tachtulec and Foejt et al., Mammalian Genome 12: , Need moe accuate appoximation that is tactible 11

12 Detemining homology Identifying Homologous Genes 1. Distinguishing othologs and paalogs 1. Identify genes with significant sequence similaity atgccaggaatcccagtgaatgcaaggagtcccagagcgtgccaggcgctgtct cgacgacttcgggtcacgtatgcgaggtctcccagtgtagaggtcggcagacgt 2. Length limitations: equie that 2. Identifying pais of genes that shae a common ancesto acoss thei entie length. Altenate Hypotheses Acknowledgements Whole gene copy Domain copy David Sankoff, Univesity of Ottawa Naayanan Raghupathy CMU Mac Tata Bown Univesity Limb Development: Jeemy Gibson-Bown Ilya Ruvinsky Segei Agulnik Silve Lab (Pinceton) Papaioannou Lab (Columbia) NHGRI, David and Lucille Packad Foundation 12