The anthropoid postcranial axial skeleton: comments on development, variation, and evolution

Within-species phenotypic variation is the raw material on which natural selection acts to shape evolutionary change, and understanding more about the developmental genetics of intraspecific as well as interspecific phenotypic variation is an important component of the Evo-Devo agenda. The axial skeleton is a useful system to analyze from such a perspective. Its development is increasingly well understood, and between-species differences in functionally important developmental parameters are well documented. I present data on intraspecific variation in the axial postcranial skeleton of some Primates, including hominoids (apes and humans). Hominoid species are particularly valuable, because counts of total numbers of vertebrae, and hence original somite numbers, are available for large samples. Evolutionary changes in the axial skeleton of various primate lineages, including bipedal humans, are reviewed, and hypotheses presented to explain the changes in terms of developmental genetics. Further relevant experiments on model organisms are suggested in order to explore more fully the differences in developmental processes between primate species, and hence to test these hypotheses.     

Testing evolutionary hypotheses about the phylotypic period of zebrafish

Vertebrate embryos pass through a period of morphological similarity, the phylotypic period. Since Haeckel's biogenetic law of recapitulation, proximate and ultimate evolutionary causes of such similarity of embryos were discussed. We test predictions about changes in phenotypic and genetic variances that were derived from three hypotheses about the evolutionary origin of the phylotypic stage, i.e. random, epigenetic effects, and stabilizing selection. The random hypothesis predicts increasing values for phenotypic variances and stable or increasing values for genetic variances; the epigenetic effects hypothesis predicts declining values for phenotypic variances but stable or increasing values of genetic variances, and the stabilizing selection predicts stable phenotypic variances but decreasing genetic variances. We studied zebrafish as a model species, because it can be bred in large numbers as necessary for a quantitative genetics breeding design. A half-sib breeding scheme provided estimates of additive genetic variances from 11 embryonic characters from 12 through to 24 hr after fertilization, i.e. before, during (15-19 hr), and after the phylotypic period. Because additive genetic variances are size dependent, we calculated narrow-sense heritabilities as a size independent gauge of genetic contributions to the phenotype. The results show declining phenotypic variances and stable heritabilities. In conclusion, we reject the random and the stabilizing selection hypotheses and favor ideas about epigenetic effects that constrain the early embryonic development. Additive genetic variance during the phylotypic stage makes it accessible for evolution, thus explaining in a simple and straightforward way why the phylotypic period differs among vertebrates in timing, duration, and morphologies.     

Dealing with uncertainty in landscape genetic resistance models: a case of three co‐occurring marsupials

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model‐based inference. We illustrate the approach empirically using co‐occurring, woodland‐preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground‐dwelling antechinus (Antechinus flavipes). First, we use maximum‐likelihood and a bootstrap procedure to identify the best‐supported isolation‐by‐resistance model out of 56 models defined by linear and non‐linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision‐making, where dealing with uncertainty is critical.     

The effect of SNP discovery method and sample size on estimation of population genetic data for Chinese and Indian rhesus macaques (<Emphasis Type="Italic">Macaca mulatta</Emphasis>)

This study was designed to address issues regarding sample size and marker location that have arisen from the discovery of SNPs in the genomes of poorly characterized primate species and the application of these markers to the study of primate population genetics. We predict the effect of discovery sample size on the probability of discovering both rare and common SNPs and then compare this prediction with the proportion of common and rare SNPs discovered when different numbers of individuals are sequenced. Second, we examine the effect of genomic region on estimates of common population genetic data, comparing markers from both coding and non-coding regions of the rhesus macaque genome and the population genetic data calculated from these markers, to measure the degree and direction of bias introduced by SNPs located in coding versus non-coding regions of the genome. We found that both discovery sample size and genomic region surveyed affect SNP marker attributes and population genetic estimates, even when these are calculated from an expanded data set containing more individuals than the original discovery data set. Although none of the SNP detection methods or genomic regions tested in this study was completely uninformative, these results show that each has a different kind of genetic variation that is suitable for different purposes, and each introduces specific types of bias. Given that each SNP marker has an individual evolutionary history, we calculated that the most complete and unbiased representation of the genetic diversity present in the individual can be obtained by incorporating at least 10 individuals into the discovery sample set, to ensure the discovery of both common and rare polymorphisms.     

Applying Quantitative Genetic Methods to Primate Social Behavior

Increasingly, behavioral ecologists have applied quantitative genetic methods to investigate the evolution of behaviors in wild animal populations. The promise of quantitative genetics in unmanaged populations opens the door for simultaneous analysis of inheritance, phenotypic plasticity, and patterns of selection on behavioral phenotypes all within the same study. In this article, we describe how quantitative genetic techniques provide studies of the evolution of behavior with information that is unique and valuable. We outline technical obstacles for applying quantitative genetic techniques that are of particular relevance to studies of behavior in primates, especially those living in noncaptive populations, e.g., the need for pedigree information, non-Gaussian phenotypes, and demonstrate how many of these barriers are now surmountable. We illustrate this by applying recent quantitative genetic methods to spatial proximity data, a simple and widely collected primate social behavior, from adult rhesus macaques on Cayo Santiago. Our analysis shows that proximity measures are consistent across repeated measurements on individuals (repeatable) and that kin have similar mean measurements (heritable). Quantitative genetics may hold lessons of considerable importance for studies of primate behavior, even those without a specific genetic focus.     

Confidence Intervals for Population Allele Frequencies: The General Case of Sampling from a Finite Diploid Population of Any Size

The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (%), a sample size of is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L.), occupying meadows in Finland. For each population, the method is used to derive % confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint % confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a % confidence interval for Jost''s D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.     

Estimating genetic correlations in natural populations

Information on the genetic correlation between traits provides fundamental insight into the constraints on the evolutionary process. Estimates of such correlations are conventionally obtained by raising individuals of known relatedness in artificial environments. However, many species are not readily amenable to controlled breeding programmes, and considerable uncertainty exists over the extent to which estimates derived under benign laboratory conditions reflect the properties of populations in natural settings. Here, non-invasive methods that allow the estimation of genetic correlations from phenotypic measurements derived from individuals of unknown relatedness are introduced. Like the conventional approach, these methods demand large sample sizes in order to yield reasonably precise estimates, and special precautions need to be taken to eliminate bias from shared environmental effects. Provided the sample consists of at least 20% or so relatives, informative estimates of the genetic correlation are obtainable with sample sizes of several hundred individuals, particularly if supplemental information on relatedness is available from polymorphic molecular markers.     

SIMULATION‐BASED LIKELIHOOD APPROACH FOR EVOLUTIONARY MODELS OF PHENOTYPIC TRAITS ON PHYLOGENY

Phylogenetic comparative methods (PCMs) have been used to test evolutionary hypotheses at phenotypic levels. The evolutionary modes commonly included in PCMs are Brownian motion (genetic drift) and the Ornstein–Uhlenbeck process (stabilizing selection), whose likelihood functions are mathematically tractable. More complicated models of evolutionary modes, such as branch‐specific directional selection, have not been used because calculations of likelihood and parameter estimates in the maximum‐likelihood framework are not straightforward. To solve this problem, we introduced a population genetics framework into a PCM, and here, we present a flexible and comprehensive framework for estimating evolutionary parameters through simulation‐based likelihood computations. The method does not require analytic likelihood computations, and evolutionary models can be used as long as simulation is possible. Our approach has many advantages: it incorporates different evolutionary modes for phenotypes into phylogeny, it takes intraspecific variation into account, it evaluates full likelihood instead of using summary statistics, and it can be used to estimate ancestral traits. We present a successful application of the method to the evolution of brain size in primates. Our method can be easily implemented in more computationally effective frameworks such as approximate Bayesian computation (ABC), which will enhance the use of computationally intensive methods in the study of phenotypic evolution.     

Measuring the relationship between dietary quality and body size in primates

The feeding niche and the body size of any species are fundamental parameters that constrain the evolution of many other phenotypic characters. Moreover, previous work has shown that body size and diet are correlated, as a consequence of the negative allometry of metabolic rate. Unfortunately, the precise form of the association between body size and diet has never been specified, principally because no suitable cross-species measure of diet has been advanced. Here we develop a measure of diet that is sensitive over the whole spectrum of primate feeding niches, and use this measure to define the relationship between body size and diet for a sample of 72 primate species. Subsequently, we present several examples of how behavioral and ecological hypotheses can be tested by examining the extent to which particular species deviate from the general diet-body size pattern.     

Multiple Quantitative Trait Analysis Using Bayesian Networks

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness.     

A COMPARISON OF GENETIC AND PHENOTYPIC CORRELATIONS

Genetic variances and correlations lie at the center of quantitative evolutionary theory. They are often difficult to estimate, however, due to the large samples of related individuals that are required. I investigated the relationship of genetic- and phenotypic-correlation magnitudes and patterns in 41 pairs of matrices drawn from the literature in order to determine their degree of similarity and whether phenotypic parameters could be used in place of their genetic counterparts in situations where genetic variances and correlations cannot be precisely estimated. The analysis indicates that squared genetic correlations were on average much higher than squared phenotypic correlations and that genetic and phenotypic correlations had only broadly similar patterns. These results could be due either to biological causes or to imprecision of genetic-correlation estimates due to sampling error. When only those studies based on the largest sample sizes (effective sample size of 40 or more) were included, squared genetic-correlation estimates were only slightly greater than their phenotypic counterparts and the patterns of correlation were strikingly similar. Thus, much of the dissimilarity between phenotypic- and genetic-correlation estimates seems to be due to imprecise estimates of genetic correlations. Phenotypic correlations are likely to be fair estimates of their genetic counterparts in many situations. These further results also indicate that genetic and environmental causes of phenotypic variation tend to act on growth and development in a similar manner.     

The Baboon as a Nonhuman Primate Model for the Study of the Genetics of Obesity

Objective: At present, rodents represent the most common animal model for research in obesity and its comorbidities (e.g., type 2 diabetes and coronary heart disease), however, there are several physiological and developmental differences between rodents and humans reflective of their relatively ancient evolutionary divergence (approximately 65 to 75 million years ago). Therefore, we are currently developing the baboon as a nonhuman primate model for the study of the genetics of obesity. Research Methods and Procedures: At present, we are collecting extensive phenotypic data in a large pedigreed colony (N > 2000) of baboons housed at the Southwest Foundation for Biomedical Research in San Antonio, Texas. The long‐term goal of this project is to identify genes influencing adiposity‐related phenotypes and to test hypotheses regarding their pleiotropic effects on other phenotypes related to increased risk for a variety of common diseases (e.g., coronary heart disease and type 2 diabetes). Results: To date we have obtained various adipose‐specific endocrine measures, adipose tissue biopsies, and estimates of body composition on a substantial portion of our pedigreed colony. The pattern of adipose tissue accumulation follows closely that seen in humans, and we have detected significant additive genetic heritabilities for these obesity‐related phenotypes. Discussion: Given the physiological and developmental similarities between humans and baboons, along with the ability to collect data under well‐controlled situations and the extensive pedigree data available in our colony, the baboon offers an extremely valuable nonhuman primate model for the study of obesity and its comorbidities.     

Estimating the phylogeny and divergence times of primates using a supermatrix approach

Background  

The primates are among the most broadly studied mammalian orders, with the published literature containing extensive analyses of their behavior, physiology, genetics and ecology. The importance of this group in medical and biological research is well appreciated, and explains the numerous molecular phylogenies that have been proposed for most primate families and genera. Composite estimates for the entire order have been infrequently attempted, with the last phylogenetic reconstruction spanning the full range of primate evolutionary relationships having been conducted over a decade ago.     

Assessing the Effect of Sequencing Depth and Sample Size in Population Genetics Inferences

Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Recently, researchers have proposed statistical modelling to address genotyping uncertainty associated with NGS data. However, an ongoing debate is whether it is more beneficial to increase the number of sequenced individuals or the per-sample sequencing depth for estimating genetic variation. Through extensive simulations, I assessed the accuracy of estimating nucleotide diversity, detecting polymorphic sites, and predicting population structure under different experimental scenarios. Results show that the greatest accuracy for estimating population genetics parameters is achieved by employing a large sample size, despite single individuals being sequenced at low depth. Under some circumstances, the minimum sequencing depth for obtaining accurate estimates of allele frequencies and to identify polymorphic sites is , where both alleles are more likely to have been sequenced. On the other hand, inferences of population structure are more accurate at very large sample sizes, even with extremely low sequencing depth. This all points to the conclusion that under various experimental scenarios, in cost-limited population genetics studies, large sample sizes at low sequencing depth are desirable to achieve high accuracy. These findings will help researchers design their experimental set-ups and guide further investigation on the effect of protocol design for genetic research.     

Population, quantitative and comparative genomics of adaptation in forest trees

High-throughput DNA sequencing and genotyping technologies have enabled a new generation of research in plant genetics where combined quantitative and population genetic approaches can be used to better understand the relationship between naturally occurring genotypic and phenotypic diversity. Forest trees are highly amenable to such studies because of their combined undomesticated and partially domesticated state. Forest geneticists are using association genetics to dissect complex adaptive traits and discover the underlying genes. In parallel, they are using resequencing of candidate genes and modern population genetics methods to discover genes under natural selection. This combined approach is identifying the most important genes that determine patterns of complex trait adaptation observed in many tree populations.     

Sexual Antagonism,Temporally Fluctuating Selection,and Variable Dominance Affect a Regulatory Polymorphism in Drosophila melanogaster

Understanding how genetic variation is maintained within species is a major goal of evolutionary genetics that can shed light on the preservation of biodiversity. Here, we examined the maintenance of a regulatory single-nucleotide polymorphism (SNP) of the X-linked Drosophila melanogaster gene fezzik. The derived variant at this site is at intermediate frequency in many worldwide populations but absent in populations from the ancestral species range in sub-Saharan Africa. We collected and genotyped wild-caught individuals from a single European population biannually over a period of 5 years, which revealed an overall difference in allele frequency between the sexes and a consistent change in allele frequency across seasons in females but not in males. Modeling based on the observed allele and genotype frequencies suggested that both sexually antagonistic and temporally fluctuating selection may help maintain variation at this site. The derived variant is predicted to be female-beneficial and mostly recessive; however, there was uncertainty surrounding our dominance estimates and long-term modeling projections suggest that it is more likely to be dominant. By examining gene expression phenotypes, we found that phenotypic dominance was variable and dependent upon developmental stage and genetic background, suggesting that dominance may be variable at this locus. We further determined that fezzik expression and genotype are associated with starvation resistance in a sex-dependent manner, suggesting a potential phenotypic target of selection. By characterizing the mechanisms of selection acting on this SNP, our results improve our understanding of how selection maintains genetic and phenotypic variation in natural populations.     

A geographical genetics framework for inferring homing reproductive behavior in fishes

One of the most intriguing patterns of migration and gene flow that affects genetic structure is the reproductive homing behavior of fishes, wherein the adults return to the areas in which they were spawned. Here we reviewed the literature on homing behavior in fish and propose an analytical framework for testing hypotheses regarding this behavior and its effects on the genetic structure of fish in an explicit geographical context, using a geographical genetics toolbox. Although disentangling the many potential causes underlying genetic population structure and unambiguously demonstrating that the homing behavior causes these genetic patterns is difficult, our framework allows the successive testing of homing behavior with increasing levels of complexity based on the following: (1) establishment of population structures among waterheads; (2) patterns of genetic variability throughout the adult migratory pool; (3) analyses of the non-migratory adult pool; and (4) comparisons among successive generations. We expect that the framework presented here will help delineating the appropriate uses of different sampling designs to make inferences regarding homing behavior and illustrate the limits imposed by the interpretation of different types of genetic data. More importantly, we hope this framework enables researchers to understand how a particular dataset can be utilized in a broader context as an ongoing part of a larger research program and thus guide future research by developing better and more integrated sampling designs.     

CONNECTING QTLS TO THE G-MATRIX OF EVOLUTIONARY QUANTITATIVE GENETICS

Evolutionary quantitative genetics has recently advanced in two distinct streams. Many biologists address evolutionary questions by estimating phenotypic selection and genetic (co)variances ( G matrices). Simultaneously, an increasing number of studies have applied quantitative trait locus (QTL) mapping methods to dissect variation. Both conceptual and practical difficulties have isolated these two foci of quantitative genetics. A conceptual integration follows from the recognition that QTL allele frequencies are the essential variables relating the G -matrix to marker-based mapping experiments. Breeding designs initiated from randomly selected parental genotypes can be used to estimate QTL-specific genetic (co)variances. These statistics appropriately distill allelic variation and provide an explicit population context for QTL mapping estimates. Within this framework, one can parse the G -matrix into a set of mutually exclusive genomic components and ask whether these parts are similar or dissimilar in their respective features, for example the magnitude of phenotypic effects and the extent and nature of pleiotropy. As these features are critical determinants of sustained response to selection, the integration of QTL mapping methods into G -matrix estimation can provide a concrete, genetically based experimental program to investigate the evolutionary potential of natural populations.     

EcoGenetics: An R package for the management and exploratory analysis of spatial data in landscape genetics

The integration of ecology and genetics has become established in recent decades, in hand with the development of new technologies, whose implementation is allowing an improvement of the tools used for data analysis. In a landscape genetics context, integrative management of population information from different sources can make spatial studies involving phenotypic, genotypic and environmental data simpler, more accessible and faster. Tools for exploratory analysis of autocorrelation can help to uncover the spatial genetic structure of populations and generate appropriate hypotheses in searching for possible causes and consequences of their spatial processes. This study presents EcoGenetics, an R package with tools for multisource management and exploratory analysis in landscape genetics.