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“Evolutionary Biology & the Theory of Computing” (Spring ..., Lecture notes of Evolutionary biology

opportunity to understand the fundamental concepts and key questions in evolutionary biology. This was an ambitious program that aimed to bring together ...

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Download “Evolutionary Biology & the Theory of Computing” (Spring ... and more Lecture notes Evolutionary biology in PDF only on Docsity! “Evolutionary Biology & the Theory of Computing” (Spring 2014) Final Program Report Yun S. Song (Organizing Chair) Overall objectives and assessment of the program Evolutionary biology is an intellectually rich field with a long history which has advanced re- markably through a synergistic interplay between deep understanding of biology and mathematical techniques, especially from probability and statistics. Over the past several decades, the role of computer science in studying biology has grown enormously, and computation has now become an indispensable part of the intellectual mix. Many current problems in evolutionary biology push the limits of computation, and new algorithmic insights are needed to make progress. The main objectives of the Simons Institute program on “Evolutionary Biology & the Theory of Computing” were twofold: 1. To promote the interaction between theoretical computer scientists and researchers from the evolutionary biology, physics, and probability and statistics communities. 2. To encourage the participants to collaborate on identifying and tackling some of the most important theoretical and computational challenges arising from evolutionary biology. One important tactical goal of the program was to provide theoretical computer scientists with the opportunity to understand the fundamental concepts and key questions in evolutionary biology. This was an ambitious program that aimed to bring together experts from diverse disciplines and encourage interaction. The initial language barrier was not a major problem; a much bigger challenge was bridging the gap between disparate research interests to find common goals. Most mathematicians, statisticians and physicists participating in the program already had experience of collaborating fruitfully with evolutionary biologists. However, the cultural difference between the- oretical computer scientists and the rest of the program participants was bigger than anticipated. Theoretical computer scientists prefer to work with simple models that can be understood in detail and for which they can prove rigorous theorems which may or may not generalize to more com- plex models. In contrast, evolutionary biologists put much more emphasis on biologically realistic models and inference methods for analyzing data. Another observed division was at the level of modeling. Specifically, most evolutionary biologists were focused on understanding the evolution- ary mechanisms underlying population genetic variation, and the genetic basis of phenotypic traits and adaptation to new environments. On the other hand, a large fraction of theoretical computer scientists were more interested in understanding how systems evolve. Realizing these differences is a necessary step towards bringing the two communities closer, and the Simons Institute program provided a valuable opportunity for each community to become more aware of the other group’s views and interests. Despite the challenges just described, all participants found the program to be thought- provoking, and it was largely successful at meeting the aforementioned main objectives. The pro- gram’s Research Fellows played a pivotal role in giving life to the program throughout the semester. Also, several junior theoretical computer scientists — especially Varun Kanade, Paul Valiant and 26 Greg Valiant — deserve credit for taking part in many of the activities described below and trying to reach out to the participants from other disciplines. The reunion workshop (held July 27-29, 2015) provided an excellent opportunity to reflect on the program. In particular, there was a panel discussion to highlight some of the key open prob- lems in theoretical biology, and to discuss how to bridge better the existing gap between biology and theoretical computer science. Paul Valiant voiced the view that the biology community is inward-looking and that it has largely ignored previous biology-related work by theoretical com- puter scientists. To increase impact and visibility, it was suggested that theoretical computer scientists should try to work on problems that biologists care about, and to publish in biology journals and have their papers peer reviewed by biologists. Furthermore, trying to tackle narrow, well-defined problems rather than aiming at overly general, ambitious goals might be a promising way to bring the two communities closer in the immediate future. Lastly, Chuck Langley pointed out that biology is currently awash with data, often of poor quality, and that biologists are struggling to process and make sense of it. Theoretical investigations that address this issue would be most welcome, as would work that characterizes the fundamental limits of what can be learned from data. Program activities and range of themes covered The program started with a week-long Boot Camp, with introductory lectures given by theoret- ical computer scientists, biologists, mathematicians and physicists. A wide range of topics were covered, touching on the key themes of the program. Monty Slatkin’s account of the historical development of evolutionary biology and Charles Marshall’s lectures on “The Origin and Evolution of Life on Earth” were particularly well received. Christos Papadimitriou and Varun Kanade also gave excellent lectures on “Computational Views of Evolution” and “Evolution as Computational Learning” respectively; these lectures helped to clarify to the scientists from other disciplines the key questions in evolutionary biology that are of interest to theoretical computer scientists and how they go about thinking about them. There were three workshops associated with the program. The first workshop was centered on statistical inference methods and computational challenges in large-scale population genomics in light of the recent explosion of DNA sequence data. The second workshop was closer to theoretical computer science, showcasing the key models and theories of evolution inspired by computational considerations, as well as highlighting research questions in evolutionary biology which might benefit from computational insights and methodology. The third workshop focused on new directions in probabilistic models of evolution, addressing a broad set of topics including the evolution of diseases and pathogens. Details on the outcomes of these workshops are provided in separate reports. Three weekly activities were organized throughout the program to facilitate the convergence of backgrounds and research interests. A seminar series was held on Tuesdays, during which participants from diverse areas gave talks on their research. Every Thursday afternoon, there was an informal discussion session on “Ideas and Problems” related to evolutionary biology. Lastly, every Friday afternoon, there was a reading group dedicated to discussing classic and recent papers relevant to the main themes of the program. These activities encouraged participants to interact with each other on a daily basis, exchange ideas, and help each other learn complementary subjects. 27 ated a project during the program and showed that allele frequency data are not very informative about the deep history of populations [104]. In particular, if the population size has undergone a constriction in the past, say due to a migration bottleneck, the minimax error in estimating the historical population size at times more ancient than the bottleneck is at least O(1/ log s), where s is the number of independent polymorphic sites used in the analysis. This rate is exponentially worse than known convergence rates for many classical estimation problems in statistics. Another surprising aspect of their theoretical bound is that it does not depend on the number of sampled individuals. This means that, for a fixed number s of polymorphic sites considered, using more individuals does not help to reduce the minimax error bound. Also during the program, Anand Bhaskar, Yun Song and Sebastian Roch initiated a collaboration to study the geometry of the distribution of allele frequencies in a genomic sample and characterize its dependence on the un- derlying population demographic model. They have several novel and unexpected results about the limits of demographic inference from allele frequency data that apply to any inference algorithm. Assortative mating in human populations: A standard assumption in most population genetic analyses is that populations are well-mixed and individuals mate randomly. While this assumption is made for mathematical and computational convenience, little work has been done to study the extent to which this violation is violated in practice. During the program, Research Fellows James Zou (Harvard University) and Sriram Sankararaman (Harvard University) initiated a collaboration and realized that publicly available genomic data could be used to answer this question. Initially they found that, in admixed populations such as African-Americans and Latinos, the maternal and paternal genomes of an individual are significantly more similar than that of random couples. To understand this observation, they needed to infer the ancestries of the parents of an individual from the genotype of the individual. These ancestries can then be used to quantify the propensity for assortative (i.e., non-random) mating and to identify genetic loci that could mediate these patterns. Together with long-term participant Eran Halperin (Tel Aviv University), they developed a statistical model and method to estimate the genome-wide ancestral contributions of each parent of an admixed individual from the individual’s genomic data [122]. The statistical model employed in this work consists of a pooled semi-Markov process and is related to factorial hidden Markov models. There are interesting statistical questions about efficient inference in these models, since the combinatorial constraints of the pooling make standard variational inference inapplicable. To apply this model to better understand genomic data, they collaborated with groups at UCSF who had collected genotype and socio-economic data of Mexican and Puerto Rican individuals. By jointly analyzing the socio-economic and genomic data, they have been able to infer the relative contributions of genetic vs. socio-economic factors to non-random mating, and to identify specific subsets of the genome that are associated with these patterns of assortative mating [123]. They have found that genomic ancestry is a major factor in determining mating patterns, much more so than education level and other socio-economic factors. This project also involved collaboration with long-term participants Yun Song and Eran Halperin. This line of work has helped to characterize the extent of non-random or assortative mating in human populations and has clearly underlined the importance of moving beyond the traditional assumptions of random mating in population genetics models. Taking this thread of research fur- ther, Halperin and Noah Zaitlen (UCSF) have been developing population genetic theory that can better account for assortative mating. In particular, they have found that estimation of parameters such as migration rates, recombination rates, and the dates of admixture are all affected by assor- tative mating, and they have derived analytic formulas to infer these parameters from sequence data under assortative mating models. 30 Other research highlights of the program Nayantara Bhatnagar (University of Delaware) initiated a collaboration with Erick Matsen and Robert Bradley from the Fred Hutchinson Cancer Research Center [10]. They are looking at statistical barriers to sequence alignment, in particular giving statistical explanations for the barrier encountered by most commonly used alignment programs in the “twilight zone” of sequence identity. Research Fellow Iain Mathieson (Harvard) started a collaboration with Ken Wachter, pro- fessor of Demography at Berkeley. They are looking at the effect of genetic load on cognitive function and morbidity in the elderly. Wachter (along with Steve Evans and David Steinsalz) has done some theoretical work on this topic and has access to some suitable cohorts to investigate empirically. The plan is that they will directly test for an effect in these cohorts. The program allowed Gerton Lunter (University of Oxford) to meet for the first time David Patterson of the Computer Science Division at Berkeley, to discuss a common benchmarking strat- egy for variant identification in genomics data. This visit culminated in their both participating in the Global Alliance for Genomics and Health (GA4GH, http://genomicsandhealth.org), in particular the Benchmarking and Reference Variation task teams, to develop common standards and protocols aimed at facilitating the exchange of genetics information. David Tse (Stanford) had discussions with Yun Song and Anand Bhaskar that formed the seed for a project on developing algorithms for geographical localization of individuals from genotype data, and using such information to correct for spurious associations due to population stratification in genome-wide association studies. This project began in earnest during the Spring 2015 program on Information Theory at the Simons Institute (organized by Tse). Eleazar Eskin (UCLA) collaborated with Eran Halperin and James Zou to develop statistical techniques to deconvolve multiple cell types in epigenetic data. Their work provides a new capability for analyzing such data without the need for expensive reference panels. Oskar Hallatschek (UC Berkeley) started a collaboration with Joachim Hermisson (Uni- versity of Vienna) on adaptation in a spatially structured population and its consequences for the site frequency spectrum. Hermisson also initiated a collaboration with Peter Pfaffelhuber (Uni- versity of Freiburg) to derive analytical results for the frequency of soft selective sweeps in spatially structured populations [44]. Paul Valiant led a popular open problems session where he demonstrated the challenges of simulating evolution on a computer through his attempts to evolve agents that can play Go. Agents receive a reward (fitness) based on their moves and can make local changes (mutations) to their algorithm. Impact of the program on the participants Several participants, especially the Research Fellows, benefited significantly from the program, by being given a chance to meet scientists from several fields and at various stages of their career. The daily tea time at the institute provided an informal yet structured setting where participants could 31 get together and talk about their work. Several participants also noted that the ample coffee breaks during the week-long workshops were a big success in letting people follow up on the discussions that arose during the preceding workshop talks, and several collaborations arose spontaneously over these discussions. Over the course of the program, the computer science theory community gained a more so- phisticated understanding of the biological complexities of evolution due to exposure to ideas from other communities. The effects of this became evident, for example, in the work of theoretical computer scientist Vishnoi [111], which rigorously studies the mixing time of a very realistic and complicated Markov chain that commonly arises in evolutionary genetic models. The program can be viewed as a successful and important first step towards developing non-trivial connections across interdisciplinary boundaries in the study of evolution; it is to be hoped that the momentum initiated by the program will lead to a deepening of these connections over time. 32 [52] V. KANADE, E. MOSSEL, and T. SCHRAMM. Global and Local Information in Clustering Labeled Block Models. Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/ RANDOM), pp. 779–792, 2014. [53] V. KANADE and J. THALER. Distribution-Independent Reliable Learning. In Proceedings of the 27th Annual Conference on Learning Theory, pp. 3–24, 2014. [54] J. KELLEHER, A. ETHERIDGE, and N. BARTON. Coalescent simulation in continuous space: Algorithms for large neighbourhood size. Theoretical population biology, 95, pp. 13–23, 2014. [55] J. KIM, E. MOSSEL, M. Z. RÁCZ, and N. ROSS. Can one hear the shape of a population history? Theoretical Population Biology, 100, pp. 26–38, 2015. [56] S. KRYAZHIMSKIY, D. P. RICE, E. JERISON, and M. M. DESAI. Global Epistasis Makes Adaptation Predictable Despite Sequence-Level Stochasticity. Science, 344, pp. 1519–1522, 2014. [57] R. KUMAR and L. POPOVIC. Large deviations with averaging of jump-diffusion processes. Preprint, 2014. In review for Stochastic Processes and their Applications. [58] J. B. LACK, C. M. CARDENO, M. W. CREPEAU, W. TAYLOR, R. B. CORBETT-DETIG, K. A. STEVENS, C. H. LANGLEY, and J. E. POOL. The Drosophila Genome Nexus: a population genomic resource of 623 Drosophila melanogaster genomes, including 197 from a single ancestral range population. Genetics, 199, 4, pp. 1229–1241, 2015. [59] G. I. LANG and M. M. DESAI. The spectrum of adaptive mutations in experimental evolution. Genomics, 104, pp. 412–416, 2014. [60] S. A. LANGLEY, G. H. KARPEN, and C. H. LANGLEY. Nucleosomes shape DNA polymorphism and divergence. PLoS genetics, 10, 7, pp. e1004457, 2014. [61] I. LAZARIDIS, N. PATTERSON, A. MITTNIK, G. RENAUD, S. MALLICK, P. H. SUDMANT, J. G. SCHRAIBER, S. CASTELLANO, K. KIRSANOW, C. ECONOMOU, and OTHERS. Ancient human genomes suggest three ancestral populations for present-day Europeans. arXiv preprint arXiv:1312.6639, 2013. [62] M. LIANG and R. NIELSEN. The lengths of admixture tracts. Genetics, 197, 3, pp. 953–967, 2014. [63] A. LIVNAT. A manuscript on the source of creativity in evolution. To be submitted. [64] A. LIVNAT and C. PAPADIMITRIOU. A note on finite populations. To be submitted. [65] A. LIVNAT, C. PAPADIMITRIOU, and U. VAZIRANI. Algorithms, games and evolution: the sequel. To be submitted. [66] A. LIVNAT and C. PAPADIMITRIOU. Sex as an algorithm: evolution under the lens of computation. Under review, CACM. [67] A. LIVNAT, C. PAPADIMITRIOU, A. RUBINSTEIN, G. VALIANT, and A. WAN. Satisfiability and evolution. In Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on, pp. 524–530, IEEE, 2014. [68] S. LUO and J. MATTINGLY. Scaling limits of a model for selection at two scales. Preprint. 2015. [69] S. MANGUL, N. C. WU, N. MANCUSO, A. ZELIKOVSKY, R. SUN, and E. ESKIN. Accurate viral population assembly from ultra-deep sequencing data. Bioinformatics, 30, 12, pp. 329–337, 2014. [70] I. MATHIESON and G. MCVEAN. Demography and the age of rare variants. PLoS Genetics, 10, e1004528, 2014. [71] I. MOLTKE, M. FUMAGALLI, T. S. KORNELIUSSEN, J. E. CRAWFORD, P. BJERREGAARD, M. E. JØRGENSEN, N. GRARUP, H. C. GULLØV, A. LINNEBERG, O. PEDERSEN, and OTHERS. Uncovering the genetic history of the present-day greenlandic population. The American Journal of Human Genetics, 96, 1, pp. 54–69, 2015. [72] E. MOSSEL and S. ROCH. Distance-based species tree estimation: information-theoretic trade-off between number of loci and sequence length under the coalescent. RANDOM. 2015. To appear. [73] E. MOSSEL and M. STEEL. Majority rule has transition ratio 4 on Yule trees under a 2-state symmetric model. Journal of theoretical biology, 360, pp. 315–318, 2014. [74] D. B. NEALE, J. L. WEGRZYN, K. A. STEVENS, A. V. ZIMIN, D. PUIU, M. W. CREPEAU, C. CARDENO, M. KORIABINE, A. E. HOLTZ-MORRIS, J. D. LIECHTY, and OTHERS. Decoding the massive genome of loblolly pine using haploid DNA and novel assembly strategies. Genome biology, 15, 3, pp. R59, 2014. [75] J. NEIDHART, I. G. SZENDRO, and J. KRUG. Adaptation in tunably rugged fitness landscapes: The Rough Mount Fuji Model. Genetics, 198, 2, pp. 699–721, 2014. [76] I. E. OCHS and M. M. DESAI. The competition between simple and complex evolutionary trajectories in asexual populations. BMC evolutionary biology, 15, 1, pp. 55, 2015. 35 [77] J. OTWINOWSKI and J. KRUG. Clonal interference and Mullerʼs ratchet in spatial habitats. Physical biology, 11, 5, pp. 056003, 2014. [78] T. PAIXAO, G. BADKOBEHE, N. H. BARTON, C. DOLGAN, D. C. DANG, T. FRIEDRICH, P. K. LEHRE, D. SUDHOLT, and B. TRUBENOVA. A unifying framework for evolutionary processes. J. Theor. Biol. In review. [79] I. PANAGEAS, P. SRIVASTAVA, and N. K. VISHNOI. Evolutionary Dynamics in Finite Populations Mix Rapidly. Submitted. [80] C. PAPADIMITRIOU. Algorithms, complexity, and the sciences. Proceedings of the National Academy of Sciences, 111, 45, pp. 15881–15887, 2014. [81] C. H. PAPADIMITRIOU and N. K. VISHNOI. On the Poincaré-Bendixson Theorem and Computational Complexity. Submitted. [82] P. A. PAPAKONSTANTINOU, J. XU, and Z. CAO. Bagging by Design (on the Suboptimality of Bagging). In Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014. [83] P. A. PAPAKONSTANTINOU and G. YANG. Cryptography with Streaming Algorithms. In CRYPTO, pp. 55– 70, August 2014. [84] S.-C. PARK and J. KRUG. The c-record process and evolution in epistatic rough Mount Fuji fitness landscapes. Preprint. 2015. [85] S.-C. PARK, J. NEIDHART, and J. KRUG. Greedy adaptive walks on a correlated fitness landscape. Preprint. 2015. [86] S.-C. PARK, I. G. SZENDRO, J. NEIDHART, and J. KRUG. Phase transition in adaptive walks on the rough Mount Fuji fitness landscape. Phys. Rev., 91, 042707, 2015. [87] B. M. PETER and M. SLATKIN. The effective founder effect in a spatially expanding population. Evolution, 69, 3, pp. 721–734, 2015. [88] P. PFAFFELHUBER and L. POPOVIC. How spatial heterogeneity shapes multi-scale biochemical reactions. Journal of the Royal Society Interface, 12, 104, 2015. [89] L. POPOVIC and M. RIVAS. Cherries and parameter inference on multi-type Yule trees. Journal of Mathematical Biology. In review. [90] L. POPOVIC and M. RIVAS. The coalescent point process of multi-type branching trees. Stochastic Processes and their Applications, 124, 12, 2014. [91] F. RACIMO, M. KUHLWILM, and M. SLATKIN. A test for ancient selective sweeps and an application to candidate sites in modern humans. Molecular biology and evolution, 31, 12, pp. 3344–3358, 2014. [92] F. RACIMO, S. SANKARARAMAN, R. NIELSEN, and E. HUERTA-SÁNCHEZ. Evidence for archaic adaptive introgression in humans. Nature Reviews Genetics, 16, pp. 359–371, 2015. [93] P. L. RALPH and G. COOP. Convergent Evolution During Local Adaptation to Patchy Landscapes. bioRxiv, pp. 006940, 2014. [94] D. P. RICE, B. H. GOOD, and M. M. DESAI. The evolutionarily stable distribution of fitness effects. Genetics, 200, pp. 321–329, 2015. [95] A. RIMMER, H. PHAN, I. MATHIESON, Z. IQBAL, S. TWIGG, A. WILKIE, G. MCVEAN, and G. LUNTER. Integrating mapping-, assembly- and haplotype-based approaches for calling variants in clinical sequencing applications. Nat Genet, 46, 8, pp. 912–918, 2014. [96] S. ROCH and M. STEEL. Likelihood-based tree reconstruction on a concatenation of alignments can be positively misleading. Theoretical Population Biology, 100, pp. 56–62, 2015. [97] S. ROCH and T. WARNOW. On the Robustness to Gene Tree Estimation Error (or lack thereof) of Coalescent- Based Species Tree Methods. Syst Biol, 2015. Doi:10.1093/sysbio/syv016. [98] J. G. SCHRAIBER, S. N. EVANS, and M. SLATKIN. Bayesian inference of natural selection from allele frequency time series. In preparation. [99] P. R. STAAB, S. ZHU, D. METZLER, and G. LUNTER. scrm: efficiently simulating long sequences using the approximated coalescent with recombination. Bioinformatics, 31, 10, pp. 1680–1682, 2015. [100] M. STEEL. Tracing evolutionary links between species. American Mathematical Monthly, 121, 9, pp. 771–792, 2015. [101] M. STEEL and J. D. VELASCO. Axiomatic opportunities and obstacles for inferring a species tree from gene trees. Systematic Biology, 63, 5, pp. 772–778, 2014. [102] J. H. SUL, T. RAJ, S. DE JONG, P. I. DE BAKKER, S. RAYCHAUDHURI, R. A. OPHOFF, and B. HAN. Accurate and fast multiple-testing correction in eqtl studies. American Journal of Human Genetics, 96, 6, pp. 857– 36 868, 2015. [103] P. TATARU, J. A. NIRODY, and Y. S. SONG. diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals. Bioinformatics, 30, 23, pp. 3430–3431, 2014. [104] J. TERHORST and Y. S. SONG. Fundamental limits on the accuracy of demographic inference based on the sample frequency spectrum. In Proc. Natl. Acad. Sci. U.S.A., volume 112, pp. 7677–7682, 2015. [105] J. TERHORST and Y. S. SONG. Multi-locus analysis of genomic time series data from experimental evolution. PLoS Genetics, 11, 4, pp. e1005069, 2015. [106] J. THALER. Lower Bounds for the Approximate Degree of Block-Composed Functions. In Electronic Colloquium on Computational Complexity (ECCC), volume 22, pp. 150, 2014. [107] M. V. TROTTER, D. B. WEISSMAN, G. I. PETERSON, K. M. PECK, and J. MASEL. Cryptic genetic variation can make "irreducible complexity" a common mode of adaptation in sexual populations. Evolution, 68, pp. 3357– 3367, 2014. [108] G. VALIANT and P. VALIANT. An Automatic Inequality Prover and Instance Optimal Identity Testing. In Proceedings of IEEE FOCS, 2014. [109] G. VALIANT and P. VALIANT. Instance Optimal Learning. arXiv preprint arXiv:1504.05321v1, 2015. [110] P. VALIANT. Evolvability of Real Functions. ACM Transactions on Computation Theory (TOCT), 6, 3, pp. 12, 2014. [111] N. K. VISHNOI. The Speed of Evolution. In Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1590–1601, 2015. [112] H. P. DE VLADAR and N. BARTON. Stability and response of polygenic traits to stabilizing selection and mutation. Genetics, 197, 2, pp. 749–767, 2014. [113] Z. WANG, J. H. SUL, S. SNIR, J. A. LOZANO, and E. ESKIN. Gene-Gene interactions detection using a two- stage model. Journal of Computational Biology, 2015. [114] J. L. WEGRZYN, J. D. LIECHTY, K. A. STEVENS, L.-S. WU, C. A. LOOPSTRA, H. A. VASQUEZ-GROSS, W. M. DOUGHERTY, B. Y. LIN, J. J. ZIEVE, P. J. MARTÍNEZ-GARCÍA, C. HOLT, M. YANDELL, A. V. ZIMIN, J. A. YORKE, M. W. CREPEAU, D. PUIU, S. L. SALZBERG, P. J. DE JONG, K. MOCKAITIS, D. MAIN, C. H. LANGLEY, and D. B. NEALE. Unique Features of the Loblolly Pine (Pinus taeda L.) Megagenome Revealed Through Sequence Annotation. Genetics, 196, pp. 891–909, 2014. [115] D. B. WEISSMAN. Stress-induced variation can cause average mutation and recombination rates to be positively correlated with fitness. In ALIFE 14: The Fourteenth Conference on the Synthesis and Simulation of Living Systems, volume 14, pp. 43–44, 2014. [116] D. B. WEISSMAN and O. HALLATSCHEK. The rate of adaptation in large sexual populations with linear chromosomes. Genetics, 196, 4, pp. 1167–1183, 2014. [117] A. WOLLSTEIN and W. STEPHAN. Adaptive Fixation in Two-Locus Models of Stabilizing Selection and Genetic Drift. Genetics, 198, 2, pp. 685–697, 2014. [118] M. A. YANG, K. HARRIS, and M. SLATKIN. The projection of a test genome onto a reference population and applications to humans and archaic hominins. Genetics, 198, 4, pp. 1655–1670, 2014. [119] W. Y. YANG, A. PLATT, C. W. CHIANG, E. ESKIN, J. NOVEMBRE, and B. PASANIUC. Spatial localization of recent ancestors for admixed individuals. G3, 4, 12, pp. 2505-18, 2014. [120] A. ZIMIN, K. A. STEVENS, M. W. CREPEAU, A. HOLTZ-MORRIS, M. KORIABINE, G. MARÇAIS, D. PUIU, M. ROBERTS, J. L. WEGRZYN, P. J. DEJONG, D. B. NEALE, S. L. SALZBERG, J. A. YORKE, and C. H. LANGLEY. Sequencing and Assembly of the 22-Gb Loblolly Pine Genome. Genetics, 196, pp. 875–890, 2014. [121] D. ŽIVKOVIĆ, M. STEINRÜCKEN, Y. S. SONG, and W. STEPHAN. Transition densities and sample frequency spectra of diffusion processes with selection and variable population size. Genetics, 200, 2, pp. 601–617, 2015. [122] J. ZOU, E. HALPERIN, and S. SANKARARAMAN. Inferring parental genomic ancestries using pooled semi- Markov processes. Proceedings of ISMB. 2015. To appear. [123] J. Y. ZOU, D. S. PARK, E. G. BURCHARD, D. G. TORGERSON, M. PINO-YANES, Y. S. SONG, S. SANKARARAMAN, E. HALPERIN, and N. ZAITLEN. A genetic and socio-economic study of mate choice in Latinos reveals novel assortment patterns. Under review. 37
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