Seeing the forest through the gene‐trees

Abstract

The human genome is a dense forest of biological information for us to find our way through. In the past, we could view it as a forest, comfortably assuming the nature of its unseen trees. But new technologies have generated masses of genomic data that raise unexpected challenges to a prevailing view that grew from a theory that melded Darwinian selection and Mendelian genetic causation. Both rested on direct, largely deterministic, and highly simplified concepts of the relationship between genes and what they do. Darwin believed that natural selection was a fine-tuning mechanism that screened competing individuals to detect even the smallest difference among them.1, 2 The causal elements weren't known, but one could assume their existence, as Darwin did, and study the organisms they produced. If selection were universal, then biological functions must have adaptive explanations. Meanwhile, the inheritance that Mendel documented was probabilistic, but in a very limited and rigid way, with fixed probabilities and a few genetically determined outcome states. The discovery of Mendelian determinism led to an extremely effective genetic research program that discovered the nature, location, and arrangement of genes and their protein-coding function, whose legacy we are reaping today. Although Darwin's and Mendel's work developed independently and seemed to be addressing separate questions, by the 1930s these had been connected into a single, simple genetic understanding of life. However, the new data are revealing how complex life's genetic underpinnings actually are. Within each cell, more than six billion nucleotides of DNA encode countless thousands of functional elements. Each of our hundreds of types of cells uses these elements differently, in different contexts; each element can vary among individuals and even among cells, because mutations occur in them during life. The challenge to understand this complexity is daunting, and some rethinking is in order. The data are revealing deep but subtle unity of genetic and evolutionary causation. Some of these similarities are summarized in Figure 1. For example, most alleles (variants at a given position in the genome) have low frequency in the population, a relatively local geographic distribution, and small effects on phenotypes, while common, geographically widespread, large-effect alleles are rarer. General trail map of the genome. Schematic distribution of characteristics discussed in the text and their general functional or epistemological nature. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] If we digest this new knowledge, findings that have been seen as mysterious are easily explained. But sometimes what we can detect contributes less, and what we can't detect contributes more to our understanding of life than has generally been thought. To both Darwin and Mendel, a single concept was fundamental: common ancestry. To Darwin, common ancestry was the cornerstone of evolutionary theory. If all life comes from a common ancestor, its diversity today is due to divergence from that ancestry. In that sense, a species is a unitary phenomenon. This is clear in Darwin's sole figure in The Origin of Species, where he spoke of the image we still use, the Tree of Life. Mendel made a similar implicit assumption: that all the individuals in his pea strains were from a common ancestor. All of the “yellow” or “wrinkled” elements were identical as a result of their inbred history. It is because of evolution that, looking backward in time, today's variation appears to coalesce to a common ancestor. We may think of evolution in terms of change but, in fact, conservation, or homology, is essential to understanding life, and Darwin used patterns of conservation of traits as vital support for his theory. It might be seen as a strikingly satisfying confirmation that although Darwin had no understanding of genetics, when we now look at sequence data we see the kind of conservation he would have predicted. Indeed, Darwin's ideas were about the functionally adaptive nature of traits, but we confirm his theory with nonfunctional, nonadaptive DNA regions, such as introns, pseudogenes, and intergenic sequence, using the clock-like degradation of conservation due to mutations to reconstruct trees of ancestry. According to textbook treatments of evolution, which still repeat classical theory, natural selection is seen as so specific in picking favored variation that it reduces variation between populations and would not produce reliable phylogenies. But that's wrong. Species phylogenies, if not the timing of their branching, can be constructed from data in adaptive regions of DNA, like protein-coding genes. This is because selection picks on locally extant variation, which diverges between populations in tree-like ways. This goes further in an important way. Sequence data have unambiguously confirmed a 40-year-old idea3 that genome architecture—the nature and arrangement of its functional units—is the result of duplication events. This finding is vital to understanding the evolution of new functions.4 Periodic duplication creates gene families. The numbers and arrangement of gene family members also reflect phylogeny and, since after duplication, mutations accumulate in the individual genes, their sequence divergence also confirms the tree structure in a clocklike way. Thus, in multiple ways the evidence from DNA sequence provides independent, indeed striking confirmation of Darwin's ideas of divergence from common ancestry. Independent confirmation is among the most convincing evidence in support of a theory in science. Furthermore, we find exceptions that prove the rule. Phylogenetic relationships can be problematic in some gene families, such as the antibody genes, olfactory receptors, or the SCPP biomineralization genes.5 In these families, it can be difficult to identify specific homologues (that is, differentiating duplicate paralogs from directly descended orthologs). These families exist in adjacent multigene clusters that rapidly accumulate sequence variation because of their function: to recognize as many different pathogens or odorants as possible, or simply to capture calcium ions. These functions do not depend on high degrees of sequence conservation, so the genes accumulate variation randomly or even aided by diversifying natural selection. Also, in dense tandem clusters misalignment during meiosis leads to frequent deletion or duplication. The result of this variation fogs phylogenetic, tree-like relationships. Phylogenetic signal is altered in another way that we had long understood in principle, but that has become much clearer with sequence data. At the nucleotide level, evolution guarantees there must exist a coalescent, of which all copies today are descendants. A perhaps surprising, but fundamental, implication is that while each nucleotide has a coalescent, a single path back to a single common ancestor, this is not so simply true of the genes that make a human or a pancreas.6 At each time, each segment of each gene has passed through differing genomic environments. Each gene has its own complex path to common ancestry, and the coalescent times, places, and individuals differ greatly. There was never a single ancestral human or pancreas. There was never a ‘mitochondrial Eve’ or ‘Y-chromosome Adam’, either. That cute marketing device has led countless students and professionals to misperceive how evolution works. For nonrecombining sequences such as these, we presume there really was a single ancestral copy, but copies of the other genes in those individuals are unlikely to be here today. In any case, the coalescent mtDNA and Y-chromosomes would have been at very different times and locations.7 There were always populations. Although human genetic data have been accumulating for many years, we have only recently had whole genome sequences from specific individuals. Table 1 summarizes some of what we've seen so far.8 Probably the most important single finding is the huge number of unique or at least very rare sequence variants (single nucleotide polymorphisms, or SNPs). Representative individuals from three continents were found to have 736,261 previously known SNPs, but an average of 754,443 variants were unique to each individual. This must be so: A mutation arises about once every 40 million nucleotides per parent-offspring transmission, so each newborn infant carries roughly 155 new mutations in its 6.2billion nucleotides. New mutations start out as single copies, but even successful ones will take many generations to reach substantial frequency in our slow-reproducing species. As Figure 1 suggests, there will always be vast numbers at low frequency, recently arisen, geographically local, and unlikely to be included more than once, if at all, in random samples of individuals. These countless sites reflect new or very recent mutations, which should be roughly similar in amount and uniqueness anywhere in the world. Hence, they are not so useful in reconstructing population history. They are the leaf litter on the genomic forest floor. Among the other noteworthy findings from human whole genome sequences is the many thousands of protein-coding variants found in each person. The donors have been healthy people, usually middle-aged. This suggests that amino acid changes are not as uniformly or strongly deleterious as is often assumed in textbook Darwinian theory. To reconstruct population history, we often rely on older alleles, because the older an allele is, the more geographically widespread it is. SNP alleles found on multiple continents reflect mutations that occurred before the human expansion out of Africa some 100,000 years ago. These are useful in reconstructing our species' global as well as local history. Because humans typically exchange mates from neighboring groups, this gene flow means that allele frequencies have geographic coherence; that is, they change gradually, if sometimes irregularly, over space. Genetic analysis shows that genetic similarities roughly correspond to trees of language and cultural evidence from the same populations because culture also reflects population history. But genetic variation is subtle. We can use the frequencies of globally present alleles to examine genetic differences within and between sampled groups. For a person with a given genotype, say AA, at some locus, the probability may be substantial, or even greater, that a random individual from a different continent, rather than from the same continent, has the same AA genotype. For example, if the A allele frequency is, say, 0.1 in the first continent, but 0.6 in the second, the probability of an AA genotype is only 0.01 in the person's same continent, but 0.36 on the other continent. This might seem to suggest that we're all the same worldwide, except for a few genes like those responsible for skin color. However, if many loci are considered genome-wide, the multi-locus genotype similarities are much greater among people from the same continent than among those from other continents.10-12 The continent of indigenous origin is unambiguous, even if no two people from the same continent have exactly the same genome-wide genotype. Genome-wide, humans carry polygenic genotypes that differ probabilistically much as many phenotypes are polygenic. Genome-wide geographic affinity is even stronger at loci that have been affected by natural selection. This is because selection affects the frequencies of alleles that are found locally, and they usually differ from place to place. The picture becomes more complex, but ancestry is clear in the expected ways in populations, such as that of the United States, in which there has been recent admixture among peoples moving there from distant continents. These geographic relationships must be so if our understanding of evolution as a phenomenon of population history is accurate.13, 14 But the ability to use such data for unambiguous identification of individuals' place of origin depends on how much data are included and the location of the samples one chooses to analyze. At a more detailed local level, continent of origin may be clear, but local group affinity less so.10 Also, nothing in genetic data suggests categorical “race” divisions. It is obvious that individuals from the same geographic area are far from identical.12, 15, 16 This is strange! If races exist according to the usual notion, mustn't there be genetic variation common on one continent but absent elsewhere? In fact, few variants are highly common in one continent yet absent elsewhere. That's what we know to expect from human population history. Alleles not essentially fixed within one continent but absent elsewhere cannot be the basis of a categorical “race” in that continent. The flood of DNA sequence data provides excellent information for reconstructing human history in an increasingly fine-grained way, using a variety of analytic approaches.17-20 But probably the most important point is that these new data raise no conceptual challenges to our understanding of human history. That hasn't changed substantially for decades with perhaps one major exception. Genetic data increasingly suggest that anatomically modern humans expanded out of eastern Africa around 100,000 years ago and somehow replaced the hominins who had been resident across the Old World, adapted to all its ecological diversity, for roughly a million years. That challenges the alternative “multiregional” hypothesis. There is still active debate over when or whether, later on, Neandertals admixed with contemporary “modern” humans. Extensive sequence data from fossil specimens are now available, and although they are somewhat ambiguous, they currently suggest that there may have been some admixture before Neandertals disappeared.21 At least as interesting as detecting such admixture from ancient DNA is the challenge to develop a convincing explanation of how the replacements actually happened and whether they were based on cultural differences alone or involved genetic differences. As we wander through the thicket of our genome, it is natural to ask what all that DNA is doing. What are the phenogenetic connections between genes and traits? There are many ways to identify genetic causation. The easiest cases for us are the same as Mendel's. When there are two very different true-breeding states, such as a serious disease involving a known protein, we can identify and sequence the gene to find the responsible variants. Hundreds of such traits are known (see, for example, www.ncbi.nlm.nih.gov/omim), though once the gene is identified, much more allelic and phenogenetic complexity is usually discovered. There can be many alleles; their penetrance, or the probability of manifesting the trait, can be low. More interesting and more challenging are the complex traits having variation that is of primary interest to both evolutionary anthropology and public health. When the underlying biology is largely unknown, as is the case for many psychiatric or behavioral traits, or too complex to understand from physiological studies alone, as in diabetes or obesity, various approaches are used. These are known as mapping methods. Their objective is to search the entire genome to find genetic variation that is statistically associated with variation in the trait. The favored mapping approaches today are called genome-wide association studies (GWAS). Sampled individuals such as cases and controls for some diseases state are genotyped at large numbers of genetic markers, or variable sites of known locations spanning all the trees in our genome forest at regular intervals. The idea is that the gene or genes having variation that is responsible for our trait's variation must lie chromosomally near to, and thus be statistically associated with, at least one of the markers. The chromosomal region can then be explored to identify the causal elements. A remarkable feature of mapping is that it can be done for any trait, normal or otherwise, even if nothing is known about its biology. In this sense, genome-wide mapping is free of specific hypotheses about the nature of the trait, except that genes somehow affect it. Mapping involves only present-day variation, but it is actually an evolutionary approach because it relies on the assumption of identity by descent. It assumes that specific nucleotide changes rarely recur within the same population, so that a marker allele found in two different individuals (say a G rather than an A in a given genome position) are descendant copies of the same ancestral mutation, that is, today's copies coalesce to that event. The same assumption is made regarding the unseen sought-for causal variant responsible for the phenotype (for example, affected versus unaffected status) that is chromosomally near the typed marker. The history of joint transmission of marker and causal allele generates a statistical association between them, which is why the typed marker allele points to the unknown causal one. Fortunately, humans are a young species, with major recent expansion from small ancestral populations. Rapid, recent expansion preserves association among chromosomally nearby alleles. We are now awash in mapping results. For obvious funding reasons, most of the data are from studies of human disease, though the picture is the same for variation in normal traits that have been studied. The results are rather telling. Assessments of the success of extensive GWAS vary. Some, especially those with the greatest vested interest in the approach, give a very positive assessment,22, 23 while others are more circumspect.24, 25 Nobody disputes the typical findings: a few chromosomal locations generate statistically evidence of but each such typically for only a of the genetic effects as by its that is, by the to which the traits in What is is how well various and might raise the or whether the small generally for to are as in For example, it is that even if a is too to be directly it may at least identify causal gene that can be Representative of GWAS results. study of around cases for each of major diseases in and about Each the entire genome the the are and by and For a given trait, moving the genome, each by its the statistical of marker alleles at its the because a of across the genome are into each and most sites generate very low a few statistically are found for any by by from The [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] There are evolutionary of the for GWAS was based on the that, in common variants would for common this was for evolutionary that if not should have the nature of evolution, which generates the kind of causal in Figure there will be traits for which a few relatively common variants do for much of at least the interesting and are For most traits, many genes, even to in but only very to after many studies with dense markers, hundreds of these genes typically this picture is that in a chromosomal region by GWAS we are to genes because it is to identify alleles that change an amino acid or the But for most normal traits and most complex with which individuals can for altered timing and of gene may be more important than altered gene GWAS to generally support this sites is still an It a major challenge to identify the specific causal variants in regions by the statistical of complex usually rare and mapping can be to have an in one study but not in the The statistical for in GWAS typically to in of if there is no genetic if search hundreds of thousands of will find many that seem associated with trait. To for is Because of the and of are data from studies to to and find the The idea is that a should be found in different However, an will be only to the that the of and other genomic effects are similar amon

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