Supervised Machine Learning for Population Genetics: A New Paradigm
Authors: Daniel R. Schrider, Andrew D. Kern
Journal: Trends in Genetics (2018)

ML methods are powerful approaches that have revolutionized many fields, but their use in population genetics inference is only beginning. These methods are able to take advantage of high dimensional input – an important asset for population genetics inference – and are often more robust than other statistical approaches. The early applications of ML to population genetics demonstrate that they outperform traditional approaches. In this review we introduce ML to a biology audience, discuss examples of their application to evolutionary and population genetics, and lay out future directions that we view as promising. As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. Population genetics over the past 50 years has been squarely focused on reconciling molecular genetic data with theoretical models that describe patterns of variation produced by a combination of evolutionary forces. This interplay between empiricism and theory means that many advances in the field have come from the introduction of new stochastic population genetic models, often of increasing complexity, that describe how population parameters (e.g., recombination or mutation rates) might generate specific features of genetic polymorphism (e.g., the site frequency spectrum, SFS; see Glossary). The goal, broadly stated, is to formulate a model that describes how nature will produce patterns of variation that we observe. With such a model in hand, all one would need to do would be to estimate its parameters, and in so doing learn everything about the evolution of a given population. Thus an overwhelming majority of population genetics research has focused on classical statistical estimation from a convenient probabilistic model (i.e., the Wright–Fisher model), or through an approximation to that model (i.e., the coalescent). The central assertion here is that the model sufficiently describes the data such that insights into nature can be made through parameter estimation. This mode of analysis that pervades population genetics is what Leo Breiman [1Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author).Stat. Sci. 2001; 16: 199-231Crossref Scopus (2435) Google Scholar] famously referred to as the 'data modeling culture', wherein independent variables (i.e., the evolutionary and genomic parameters) are fed into a model and the response variables (some aspect of genetic variation) come out the other side. Models are validated in this worldview through the use of goodness-of-fit tests or examination of residuals (a recent modern example can be found in [2Elyashiv E. et al.A genomic map of the effects of linked selection in Drosophila.PLoS Genet. 2016; 12e1006130Crossref PubMed Scopus (73) Google Scholar]). In this review we argue that researchers should consider utilizing a powerful mode of analysis that has recently emerged within population genetics – the 'algorithmic modeling culture', or what is now commonly called machine learning (ML). 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An equally important advantage of the ML paradigm is that it enables the efficient use of high-dimensional inputs which act as dependent variables, without specific knowledge of the joint probability distribution of these variables. Inputs that consist of thousands of variables (also known as 'features' in the ML world) have been used with great success (e.g., [19Amit Y. Geman D. Shape quantization and recognition with randomized trees.Neural Comput. 1997; 9: 1545-1588Crossref Scopus (801) Google Scholar, 20Chen D. et al.Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification.in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE2013: 3025-3032Crossref Scopus (582) Google Scholar]), and increases in the number of features can often yield greater predictive power [1Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author).Stat. Sci. 2001; 16: 199-231Crossref Scopus (2435) Google Scholar]. Given the ever-increasing dimensionality of modern genomic data, this is a particularly desirable property of ML. In this paper we describe several examples where, through a hybrid of the 'data modeling' and 'algorithmic modeling' paradigms, ML methods can leverage high-dimensional data to attain far greater predictive power than competing methods. These early successes demonstrate that ML approaches could have the potential to revolutionize the practice of population genetic data analysis.