Frequent Nonallelic Gene Conversion on the Human Lineage and Its Effect on the Divergence Of\Xmlpi{\\} Gene Duplicates

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Z.G., and X.L., Z.G., X.L., A.H., research; performed J.K.P. ae prahs(9 4 edt els aibe AChas in mutation NAGC point than variable: faster less times 100 be to 10 to be to tend estimated been 34) (19, approaches evolutionary- based Alternatively, 33). them, (27, between variability distance experimental the and and sequences duplicate sim- the sequence of location, ilarity genomic magnitude as of such that orders rate experiments, the across eight of vary as determinants key much to as due by 29–32)—presumably of (26, vary estimates NAGC However, (28). of has pairs rate experi- base length the hundred and few tract organisms a be mean across to ments consistently The fairly 27). estimated (26, been sequences artificially genes—typically, DNA NAGC accumu- single inserted of mutation to limited with rate experiments been organisms the have lation nonhuman parameters (i) in These parameters: probed length. tract mostly key converted two the (ii) know and to need we with interplay its and NAGC mutations. in characterize other 25). evolution must (3, function ques- we duplicates and in gene sequence duplicates, for puts for expectations clocks it develop NAGC molecular To divergence, if of Importantly, fidelity sequence the 19–24). down tion 11, slowing (3, indeed paralogs is in evolution similar sequence restrict be Both may to selection. selection natural positive is and Another evolution purifying 18). concerted 14, of 13, driver (10, possible homogenizes accumulate mechanisms that mutational it differences other because through reversing evolution, by sequences concerted paralogous driving for pect uhrcnrbtos ...... n ...dsge eerh ...... and Z.G., X.L., A.H., research; designed J.K.P. and Z.G., X.L., A.H., contributions: Author stanford.edu. owo orsodnemyb drse.Eal rehsafr.d rpritch@ or [email protected] Email: addressed. be may correspondence whom work. To this to equally contributed X.L. and A.H. ua ulctsadcnetdeouini o sperva- as thought. not previously is as of sur-sive evolution divergence a concerted sequence has average duplicates—and the NAGC the human than rate, on faster effect high times small this 20 prisingly Despite is rate. humans mutation in point the NAGC that of find and rate We NAGC duplicates. char- baseline gene study well human to for not consequences tools are its statistical NAGC developed any govern We that eliminate acterized. parameters to evo- impor- the its acts “concerted Despite tance, the it them. between drive because accumulate that to duplicates than differences thought gene more also of of is driver lution” It a is diseases. (NAGC) 20 conversion gene Nonallelic Significance natmtn oln ACmttost euneevolution, sequence to mutations NAGC link to attempting In a,b,c,2 NSlicense. PNAS . www.pnas.org/lookup/suppl/doi:10. NSEryEdition Early PNAS c oadHughes Howard | f6 of 1

EVOLUTION Saccharomyces cerevisiae (35), Drosophila melanogaster (36, 37), A NAGC affects divergence patterns and human (19, 38–40). These estimates are typically based on single loci (but see refs. 41, 42). Recent family studies (43–45) G duplication −6 point have estimated the rate of AGC to be 5.9×10 per base pair per mutation generation. This is likely an upper bound on the rate of NAGC, G T GGspeciation since NAGC requires a misalignment of homologous chromo- NAGC somes during recombination, while AGC does not. T Here, we estimate the parameters governing NAGC with a sequence evolution model. Our method is not based on direct GTT G empirical observations, but it leverages substantially more infor- macaque gene 1 human gene 1 human gene 2 macaque gene 2 (m1) (h1) (h2) (m2) mation than previous experimental and computational methods: We use data from a large set of segmental duplicates in mul- B Genealogy changes (hidden) tiple species, and exploit information from a long evolutionary Null tree NAGC tree history. We estimate that the rate of NAGC in newborn dupli- NAGC initiation cates is an order of magnitude higher than the point mutation rate in humans. Surprisingly, we show that this high rate does not necessarily imply that NAGC distorts the molecular clock. NAGC tract ends Results m1 h1 h2 m2 unlikely m1 h1 h2 m2 To investigate NAGC in duplicate sequences across primates, we used a set of gene duplicate pairs in humans that we had assembled previously (46). We focused on young pairs where we esti- C Divergence patterns likely unlikely likely mate that the duplication occurred after the human–mouse split, (observed) h1 A T T and identified their orthologs in the reference genomes of chim- h2 C T T panzee, gorilla, orangutan, macaque, and mouse. We required m1 A G G that each gene pair have both orthologs in at least one nonhu- m2 C T G man primate and exactly one ortholog in mouse. Since our inference methods implicitly assume neutral sequence evolution, we D Inferred genealogy map focused our analysis on intronic sequence at least 50 bp away Inferred NAGC tract Intron boundary from intron–exon junctions. After applying these filters, our data ****** **** ***** * consisted of 97, 055 bp of sequence in 169 intronic regions from 040kbCYP2C9 and CYP2C19 75 gene families (SI Appendix). We examined divergence patterns (the partition of alleles in ** **** ******** * gene copies across primates) in these gene families. We noticed 0 FCN1 and FCN2 4kb that some divergence patterns are rare and clustered in spe- Fig. 1. NAGC alters divergence patterns. (A) NAGC can drive otherwise rare cific regions. We hypothesized that NAGC might be driving this divergence patterns, like the sharing of alleles between paralogs but not clustering. To illustrate this, consider a family of two duplicates orthologs. (B) An example of a local change in genealogy, caused by NAGC. in human and macaque which resulted from a duplication fol- (C) Examples of divergence patterns in a small multigene family. Some diver- lowed by a speciation event—as illustrated in Fig. 1B (“Null gence patterns—such as the one highlighted in purple—were both rare and tree”). Under this genealogy, we expect certain divergence pat- spatially clustered. We hypothesized that underlying these changes are local terns across the four genes to occur more frequently than oth- changes in genealogy, caused by NAGC. (D) Genealogy map (null geneal- ers. For example, the gray sites in Fig. 1C can be parsimoniously ogy marked by white, NAGC marked by purple tracts) inferred by our HMM explained by one substitution under the null genealogy. They based on observed divergence patterns (stars). Two different gene families should therefore be much more common than purple sites, as are shown. For simplicity, only the most informative patterns (purple and gray sites, as exemplified in C) are plotted. purple sites require at least two mutations. However, if we consider sites in which an NAGC event occurred after speciation (Fig. 1A and “NAGC tree” in Fig. 1B), our expectation for divergence patterns changes: Now, purple sites are much more likely Applying our HMM, we identified putatively converted tracts than gray sites. in 18/39 (46%) of the gene families, affecting 25.8% of the intronic sequence (Fig. 2A; see complete list of identified tracts in Mapping Recent NAGC Events. We developed a Hidden Markov Datasets S1–S4). Previous studies estimate that only several per- Model (HMM) which exploits the fact that observed local cent of the sequence is affected by NAGC, but the definition of changes in divergence patterns may point to hidden local changes “affected sequence” statistic is arguably method-dependendent in the genealogy of a gene family (Fig. 1 B and C). In our model, and therefore not directly comparable (41, 51, 52). Fig. 1D shows genealogy switches occur along the sequence at some rate; the an example of the maximum likelihood genealogy maps for two likelihood of a given divergence pattern at a site then depends gene families. The average length of the detected converted only on its own genealogy and nucleotide substitution rates. Our tracts is 880 bp (Fig. 2B). As previously discussed for other meth- method is similar to others that are based on incongruency of ods (27), this is likely an overestimate of the mean tract length of inferred genealogies along a sequence (47–49), but it is model- NAGC, because some identified NAGC tracts result from mul- based and robust to substitution rate variation across genes (SI tiple NAGC events occurring in close proximity (SI Appendix, Appendix). Fig. S2). We applied the HMM to a subset of the gene families When an AT/GC heteroduplex DNA arises during AGC, that we described above: families of four genes consisting of it is preferentially repaired toward GC alleles (53, 54). We two duplicates in human and a nonhuman primate. Since the sought to examine whether the same bias can be observed for HMM assumes that the duplication preceded the speciation, we NAGC (53, 55–57). We found that converted regions have a required that the overall intronic divergence patterns support this high GC content (percentage of bases that are either guanine genealogy, using the software MrBayes (50). This requirement or cytosine): 48.9%, compared with 39.6% in matched uncon- decreased the number of gene families considered to 39. verted regions (p = 4 × 10−5, two-sided t test; Fig. 2C). This

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EVOLUTION tory power of these different theoretical models for synonymous A NAGC events are correlated for nearby sites divergence in human duplicates. We wished to obtain an esti- The two focal sites are far apart The two focal sites are nearby mate of the age of duplication that is independent of ds between gene A gene B gene A gene B the human duplicates; we therefore used the extent of sharing of both paralogs in different species as a measure of the substitutions substitutions duplication time. For example, if a duplicate pair was found in human, gorilla, and orangutan—but only one ortholog was NAGC NAGC found in macaque—we estimated that the duplication occurred at the time interval between the human–macaque split and the human–orangutan split. Except for the continuous NAGC model mouse B Datum for the two-site model (or global threshold ≥4.5%), all models displayed similar broad agreement with the data (Fig. 4). The small effect of NAGC on divergence levels is intuitive in retrospect: For identical sequences, NAGC has no effect. Once human chimp. gorilla orangutan macaque differences start to accumulate, there is only a small window of gene A gene B opportunity for NAGC to act before the paralogous sequences C Rate MLE decreases with seq. divergence D Composite likelihood estimates (ds<5%) escape from its hold. This suggests that neutral sequence diver- -5 10 gence (e.g., ds) may be an appropriate molecular clock even Spearman p=10-5 bootstrap in the presence of NAGC (as also suggested by refs. 41, 46, -6 10 1000 samples and 68).

-7 Discussion 10

Tract length (bp) Tract 100 In this work, we identify recently converted regions in humans -8 10

NAGC rate estimate rate NAGC and other primates, and estimate the parameters that govern −7 0.0 0.1 0.2 0.3 0.4 0.5 1 5 x 10 NAGC. Previously, it has been somewhat ambiguous whether NAGC rate ds (in exons) (conversions per bp generation) (conversions per bp per generation) concerted evolution observations were due to natural selection, abundant NAGC, or a combination of the two (3, 22, 23). Today, Fig. 3. Estimation of NAGC parameters. (A) The two-site sequence evo- equipped with genomic data, we can revisit the pervasiveness of lution model exploits the correlated effect of NAGC on nearby sites (near concerted evolution; the data in Fig. 4 suggest that, in humans, with respect to the mean tract length). In this illustration, orange squares duplicates’ divergence levels are roughly as expected from the represent focal sites. Point substitutions are shown by the red points, and accumulation of point mutations alone. When we plugged in our a converted tract is shown by the purple rectangle. (B) Illustration of a single datum on which we compute the full likelihood, composed of two sites estimates for NAGC rate, most mechanistic models of NAGC in two duplicates across multiple species (except for the mouse outgroup also predicted a small effect on neutral sequence divergence. for which only one ortholog exists). (C) MLE rate estimates for each intron This result suggests that neutral sequence divergence may be an (orange points). MLEs of zero are plotted at the bottom. The solid line shows appropriate molecular clock even in the presence of NAGC. a natural cubic spline ﬁt. The rate decreases with sequence divergence (ds). One important topic left for future investigation is the varia- We therefore only use lowly diverged genes (ds ≤ 5%) to get point estimates tion of NAGC parameters. Our model assumes constant action of the baseline rate. (D) Composite likelihood estimates. The black point is of NAGC through time and across the genome to get a robust centered at our point estimates for ds ≤ 5% genes. The blue points show 1,000 nonparametric bootstrap estimates, where the intensity of each point corresponds to the number of bootstrap samples. The corresponding 95% marginal conﬁdence intervals are shown by black lines. Small effect of NAGC on the divergence of duplicates

75% constant rate that we estimated throughout the duplicates’ evolution (“continuous NAGC”). In this case, divergence is expected 30% to plateau around 4.5%, and concerted evolution continues for a long time [red line in Fig. 4; in practice, there will eventually 10% be an “escape” through a chance rapid accumulation of multi- 5% ple mutations (11, 66)]. However, NAGC is hypothesized to be contingent on high sequence similarity between paralogs. data (median) We therefore considered two alternative models of NAGC no NAGC 1% local threshold dynamics: ﬁrst, a model in which NAGC acts only while the global threshold 3% sequence divergence between the paralogs is below some thresh- global threshold ≥ 4.5%

old (“global threshold”); second, a model in which the initiation between duplicates Sequence divergence of NAGC at a site is contingent on perfect sequence homology today at a short 400-bp ﬂanking region upstream from the site [“local human divergence time with: chimp. gorilla orang. macaque mouse opossum chicken threshold”, (2, 27, 67)]. The local threshold model yielded a sim- Duplication age (log time-scale) ilar average trajectory to that in the absence of NAGC. A global threshold of as low as 4.5% may lead to an extended period of Fig. 4. The effect of NAGC on the divergence of duplicates. The ﬁg- concerted evolution as in the continuous NAGC model. A global ure shows both data from human paralogs and theoretical predictions of threshold of <4.5% results in a different trajectory. For example, different NAGC models. The blue line shows the expected divergence in with a global threshold of 3%, duplicates born at the time of the the absence of NAGC and the red line shows the expected divergence primates’ most recent common ancestor would diverge at 3.9% with NAGC acting continuously. The pink, orange, and red lines show the expected divergence for models in which NAGC initiation is contingent on of their sequence, compared with 5.7% in the absence of NAGC sequence similarity between the paralogs. The gray horizontal bars cor- (Fig. 4 and SI Appendix, Figs. S10–S12 show trajectories for other respond to human duplicate pairs. The duplication time for each pair is rates and threshold values). inferred by examining the nonhuman species that carry orthologs for both Lastly, we asked what these results mean for the validity of of the human paralogs. The y axis shows the synonymous sequence dissimi- molecular clocks for gene duplicates. We examined the explana- larity between the two human paralogs.

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Blood 2:191–209. eoeRes Genome 95:2150–2156. a e Genet Rev Nat u Mutat Hum r A tte“ih”st ncp ,adallele and A, copy in site “right” the at ecnie h vlto ftobial- two of evolution the consider We -ie ,NvroA(04 nepa finter- of Interplay (2014) A Navarro M, o-Vives ´ rcC arnsG 20)Gn conversion: Gene (2007) GP Patrinos C, erec ´ u o Genet Mol Hum 22:429–435. l A a e Genet Rev Nat 27:545–552. tte“et iei oyA allele A, copy in site “left” the at Clmi nvPes e York). New Press, Univ (Columbia 11:97–108. Cl pigHro a rs,Cold Press, Lab Harbor Spring (Cold µ 7:1239–1243. 8:762–775. = nuRvBiochem Rev Annu 1.2 –evn swith us )–leaving × 10 rcNt Acad Natl Proc −8 e site per Cold 40: r B 0 hle h ,Kas J iklf A(98 eecneso rc ietoaiyis directionality tract Gene-conversion (1998) JA Nickoloff GJ, Khalsa J, Cho Whelden intrachromosomal 30. of products gene of interlocus analysis Fine-resolution detecting conversion (1997) for AS gene Waldman methods D, Yang the of of length 29. power tract The (2010) and H rate Innan SP, The Mansai (2011) H 28. Innan T, Kado SP, Mansai 27. between conversion gene meiotic yeast High-frequency the (1985) in TD conversion Petes gene S, of Jinks-Robertson rate a low 26. in Very (2012) conversion MW gene Hahn ectopic GC, Conant and C, sweep Casola selective 25. Hard (2013) to concerted al. force under et genes a M, duplicated as of Hanikenne rate product 24. evolutionary same The the (2008) H of Innan more S, the for Mano in Selection 23. conversion (2006) gene H and Innan selection RP, of Sugino signatures 22. Complex (2007) al. et JF, Storz 21. 0 ehm M na 20)Noucinlzto fdpiae ee ne the under genes duplicated of Neofunctionalization (2007) H Innan application its KM, and Teshima selection with 20. model conversion gene two-locus A (2003) evolve. H families Innan gene How 19. (1990) T Ohta 18. 7 imrE atnS eelyS a ,Wlo C(90 ai ulcto n loss and duplication Rapid (1980) AC Wilson Y, Kan S, Beverley S, Martin E, Zimmer 17. (1991) D Graur WH, Li of 16. DNAs 5S (1973) K Sugimoto DD, Brown 15. npr,b elwhp rmteSafr etrfrCmuainl Evolu- Computational, for Center Genomics. Stanford Human HG009431 the supported, and was were from tionary and Z.G. fellowships work and HG008140 by A.H. part, This Institute. Grants Medical in Health Hughes manuscript. Howard of the the by Institutes and on National discussions/comments by funded review- helpful anonymous two for and Siepel, ers Adam Rosenberg, Noah Knowles, David ACKNOWLEDGMENTS. is j node and matrix i probability node between transition edge the correspond- the Therefore, the to rate. that—for equal assume mutation rate We a ing state. at gen- a occurs to constant types—substitution corresponds a mutation node assumed both Each and y. 70, 25 of ref. time from eration times split primate for of root estimates the lengths on prior edge a constant set reference and to used human be Appendix only the (SI will tree gene) two- the in one mouse in The alleles sites genomes. (two reference the state primate bit other encoding four to paralogs) one bial- and two two genome to in (corresponding sites vectors state lelic of consists datum Each timescales. a has sites) two the between on breakpoint In effect a negligible distribution. (with geometric recombination the that show of memorylessness the by the includes it on conditional site one including is conversion other a of mean with ability distributed geometrically as length alleles. unobserved fourth) and (third transi- to full mutations the possible derive ignores can we Similarly, matrix other. probability the tion includes it that given sites where right the involving A copy therefore to is B probability copy transition from The the left. NAGC the at by not mutation or but point A site through copy either of happen site are can right that transition sites two This for apart. 0100, bp to 0110 from probability transition generation order the of terms aty etr ocmuetasto rbblte ln evolutionary along probabilities transition compute to turn we Lastly, derive next We nune ytecrmsm environment. chromosome the by influenced cells. mammalian in recombination homeologous conversion. genes. duplicated between yeast. in chromosomes 82:3350–3354. nonhomologous on genes repeated genome. adaptation. environmental affording cluster gene evolution. genes. duplicated of evolution concerted enhance mice. house of genes globin duplicated rsueo eeconversion. gene of pressure genes. RHD and RHCE human the to 2158–2162. fgnscdn o h lh hiso hemoglobin. of chains alpha the for coding genes of MA). derland, family. gene a of lution P g(d (0110 stepoaiiyo ovrineeticuigoeo the of one including event conversion a of probability the is ) o ilEvol Biol Mol Genetics Genetics → 0100) g(d O(µ 180:493–505. g .W sueacntn renml,afie topology fixed a tree—namely, constant a assume We ). 184:517–527. init udmnaso oeua Evolution Molecular of Fundamentals .Floigpeiu ok(8,w oe h tract the model we (28), work previous Following ). 29:3817–3826. = P o Biol Mol J 2 etakElnSao,DcEg,Kle Harris, Kelley Edge, Doc Sharon, Eilon thank We . ), IAppendix (SI µ/3 O(c Genes g {t Genetics init ij 2 + } P (d ,and ), deij) (edge ∗ sdfie in defined as 78:397–415. 2:313–331. c ) (1 = rcNt cdSiUSA Sci Acad Natl Proc 1398:1385–1398. − Genetics O(µc .W oeta u parameterization our that note We ). ho ou Biol Popul Theor g(d = 1 eou laevis Xenopus − P )) urGenet Curr .Freape osdrteper- the consider example, For ). t ij λ 1 + . 177:481–500. o elBiol Cell Mol eused We S5. Fig. Appendix, SI LSGenet PLoS O(µ rnsGenet Trends d tflosta h prob- the that follows It λ. , NSEryEdition Early PNAS 2 ) rcNt cdSiUSA Sci Acad Natl Proc 34:269–279. + and 37:213–219. 100:8793–8798. SnurAscae,Sun- Associates, (Sinauer O(c rcNt cdSiUSA Sci Acad Natl Proc 9:e1003707. eou mulleri Xenopus 17:3614–3628. 2 22:642–644. IAppendix SI ) + O(µc P deij) (edge ∗ | ), f6 of 5 Evo- : we , for 77: d

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