A critical analysis of production-associated DNA polymorphisms in the genes of cattle, goat, sheep, and pig

Increasing productivity is one of the main objectives in animal production. Traditional breeding methods have led to increased gains in some traits but gains are not easily attainable in traits with low heritabilities. Exploiting the genetic variations underlying desired phenotypes is the goal of today's animal producers. Such positive genetic variants must, however, be known before possible application. Consequently, candidate genes of traits of interest have been searched for possible relationships with such traits or to explain reported quantitative trait loci (QTL) for such traits. DNA variants or polymorphisms have been identified in many such genes and their relationships with production traits determined. However, only a few genes have been evaluated, given the wealth of information on reported QTL for production traits, and in most cases genes are only partially investigated. This review presents available information on DNA variants for production traits and discusses steps that are required for effective utilization of this information for successful marker-assisted selection programs.     

The influence of genetics on future crop production strategies: from traits to genes, and genes to traits

This paper discusses how a genetical approach to plant physiology can contribute to research underpinning the production of new crop varieties. It highlights the interactions between genetics and plant breeding and how the current advances in genetics and the new science of genomics can contribute to our understanding of the genetical control of key agronomic traits ‐ the process of ‘translating’ traits to identified and mapped genes. Advances in genomics, such as the sequencing of whole genomes and expressed sequence tags, are producing information on genes and gene structures, but without knowing their function. A great deal more biology will be necessary to translate gene structure to function ‐ the process of translating genes to traits. Combining these ‘forward’ and ‘reverse’ genetic approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical and molecular levels, so that the ‘circuitry’ of our crop plants can be elucidated. This will enable plant breeders to manipulate crop phenotype using marker‐assisted breeding or genetic engineering approaches with a precision not previously possible.     

Testing hypotheses regarding the genetics of adaptation

Many of the hypotheses regarding the genetics of adaptation require that one know specific details about the genetic basis of complex traits, such as the number and effects of the loci involved. Developments in molecular biology have made it possible to create relatively dense maps of markers that can potentially be used to map genes underlying specific traits. However, there are a number of reasons to doubt that such mapping will provide the level of resolution necessary to specifically address many evolutionary questions. Moreover, evolutionary change is built upon the substitution of individual mutations, many of which may now be cosegregating in the same allele. In order for this developing area not to become a mirage that traps the efforts of an entire field, the genetic dissection of adaptive traits should be conducted within a strict hypothesis-testing framework and within systems that promise a reasonable chance of identifying the specific genetic changes of interest. Continuing advances in molecular technology may lead the way here, but some form of genetic testing is likely to be forever required.     

Sex‐linked inheritance,genetic correlations and sexual dimorphism in three melanin‐based colour traits in the barn owl

Theory states that genes on the sex chromosomes have stronger effects on sexual dimorphism than genes on the autosomes. Although empirical data are not necessarily consistent with this theory, this situation may prevail because the relative role of sex‐linked and autosomally inherited genes on sexual dimorphism has rarely been evaluated. We estimated the quantitative genetics of three sexually dimorphic melanin‐based traits in the barn owl (Tyto alba), in which females are on average darker reddish pheomelanic and display more and larger black eumelanic feather spots than males. The plumage traits with higher sex‐linked inheritance showed lower heritability and genetic correlations, but contrary to prediction, these traits showed less pronounced sexual dimorphism. Strong offspring sexual dimorphism primarily resulted from daughters not expressing malelike melanin‐based traits and from sons expressing femalelike traits to similar degrees as their sisters. We conclude that in the barn owl, polymorphism at autosomal genes rather than at sex‐linked genes generate variation in sexual dimorphism in melanin‐based traits.     

Human Complex Trait Genetics: Lifting the Lid of the Genomics Toolbox - from Pathways to Prediction

During the initial stages of the genome revolution human genetics was hugely successful in discovering the underlying genes for monogenic diseases. Over 3,000 monogenic diseases have been discovered with simple patterns of inheritance. The unravelling and identification of the genetic variants underlying complex or multifactorial traits, however, is proving much more elusive. There have been over 1,000 significant variants found for many quantitative and binary traits yet they explain very little of the estimated genetic variance or heritability evident from family analysis. There are many hypotheses as to why this might be the case. This apparent lack of information is holding back the clinical application of genetics and shedding doubt on whether more of the same will reveal where the remainder of the variation lies. Here we explore the current state of play, the types of variants we can detect and how they are currently exploited. Finally we look at the future challenges we must face to persuade the human genome to yield its secrets.     

Social traits,social networks and evolutionary biology

The social environment is both an important agent of selection for most organisms, and an emergent property of their interactions. As an aggregation of interactions among members of a population, the social environment is a product of many sets of relationships and so can be represented as a network or matrix. Social network analysis in animals has focused on why these networks possess the structure they do, and whether individuals’ network traits, representing some aspect of their social phenotype, relate to their fitness. Meanwhile, quantitative geneticists have demonstrated that traits expressed in a social context can depend on the phenotypes and genotypes of interacting partners, leading to influences of the social environment on the traits and fitness of individuals and the evolutionary trajectories of populations. Therefore, both fields are investigating similar topics, yet have arrived at these points relatively independently. We review how these approaches are diverged, and yet how they retain clear parallelism and so strong potential for complementarity. This demonstrates that, despite separate bodies of theory, advances in one might inform the other. Techniques in network analysis for quantifying social phenotypes, and for identifying community structure, should be useful for those studying the relationship between individual behaviour and group‐level phenotypes. Entering social association matrices into quantitative genetic models may also reduce bias in heritability estimates, and allow the estimation of the influence of social connectedness on trait expression. Current methods for measuring natural selection in a social context explicitly account for the fact that a trait is not necessarily the property of a single individual, something the network approaches have not yet considered when relating network metrics to individual fitness. Harnessing evolutionary models that consider traits affected by genes in other individuals (i.e. indirect genetic effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes.     

Evolutionary genomics of animal personality

Research on animal personality can be approached from both a phenotypic and a genetic perspective. While using a phenotypic approach one can measure present selection on personality traits and their combinations. However, this approach cannot reconstruct the historical trajectory that was taken by evolution. Therefore, it is essential for our understanding of the causes and consequences of personality diversity to link phenotypic variation in personality traits with polymorphisms in genomic regions that code for this trait variation. Identifying genes or genome regions that underlie personality traits will open exciting possibilities to study natural selection at the molecular level, gene-gene and gene-environment interactions, pleiotropic effects and how gene expression shapes personality phenotypes. In this paper, we will discuss how genome information revealed by already established approaches and some more recent techniques such as high-throughput sequencing of genomic regions in a large number of individuals can be used to infer micro-evolutionary processes, historical selection and finally the maintenance of personality trait variation. We will do this by reviewing recent advances in molecular genetics of animal personality, but will also use advanced human personality studies as case studies of how molecular information may be used in animal personality research in the near future.     

Quantitative genetics,version 3.0: where have we gone since 1987 and where are we headed?

The last 20 years since the previous World Congress have seen tremendous advancements in quantitative genetics, in large part due to the advancements in genomics, computation, and statistics. One central theme of this last 20 years has been the exploitation of the vast harvest of molecular markers—examples include QTL and association mapping, marker-assisted selection and introgression, scans for loci under selection, and methods to infer degree of coancestry, population membership, and past demographic history. One consequence of this harvest is that phenotyping, rather than genotyping, is now the bottleneck in molecular quantitative genetics studies. Equally important have been advances in statistics, many developed to effectively use this treasure trove of markers. Computational improvements in statistics, and in particular Markov Chain Monte Carlo (MCMC) methods, have facilitated many of these methods, as have significantly improved computational abilities for mixed models. Indeed, one could argue that mixed models have had at least as great an impact in quantitative genetics as have molecular markers. A final important theme over the past 20 years has been the fusion of population and quantitative genetics, in particular the importance of coalescence theory with its applications for association mapping, scans for loci under selection, and estimation of the demography history of a population. What are the future directions of the field? While obviously important surprises await us, the general trend seems to be moving into higher and higher dimensional traits and, in general, dimensional considerations. We have methods to deal with infinite-dimensional traits indexed by a single variable (such as a trait varying over time), but the future will require us to treat much more complex objects, such as infinite-dimensional traits indexed over several variables and with graphs and dynamical networks. A second important direction is the interfacing of quantitative genetics with physiological and developmental models as a step towards both the gene–phenotype map as well as predicting the effects of environmental changes. The high-dimensional objects we will need to consider almost certainly have most of their variation residing on a lower (likely much lower) dimensional subspace, and how to treat these constraints will be an important area of future research. Conversely, the univariate traits we currently deal with are themselves projections of more complex structures onto a lower dimensional space, and simply treating these as univariate traits can result in serious errors in understanding their selection and biology. As a field, our future is quite bright. We have new tools and techniques, and (most importantly) new talent with an exciting international group of vibrant young investigators who have received their degrees since the last Congress. One cloud for concern, however, has been the replacement at many universities of plant and animal breeders with plant and animal molecular biologists. Molecular tools are now an integral part of breeding, but breeding is not an integral part of molecular biology.     

Estimating evolutionary parameters when viability selection is operating

Some individuals die before a trait is measured or expressed (the invisible fraction), and some relevant traits are not measured in any individual (missing traits). This paper discusses how these concepts can be cast in terms of missing data problems from statistics. Using missing data theory, I show formally the conditions under which a valid evolutionary inference is possible when the invisible fraction and/or missing traits are ignored. These conditions are restrictive and unlikely to be met in even the most comprehensive long-term studies. When these conditions are not met, many selection and quantitative genetic parameters cannot be estimated accurately unless the missing data process is explicitly modelled. Surprisingly, this does not seem to have been attempted in evolutionary biology. In the case of the invisible fraction, viability selection and the missing data process are often intimately linked. In such cases, models used in survival analysis can be extended to provide a flexible and justified model of the missing data mechanism. Although missing traits pose a more difficult problem, important biological parameters can still be estimated without bias when appropriate techniques are used. This is in contrast to current methods which have large biases and poor precision. Generally, the quantitative genetic approach is shown to be superior to phenotypic studies of selection when invisible fractions or missing traits exist because part of the missing information can be recovered from relatives.     

单核苷酸多态性在林木中的研究进展

摘要: 单核苷酸多态性(Single nucleotide polymorphisms, SNPs)是许多生物体最丰富的遗传变异形式。林木是重要的植物类群和陆地植物生态系统的重要组成部分, SNP作为新的分子标记已应用于松、杨、黄杉、桉和云杉等属的多个树种的遗传育种学研究, 获得了包括核苷酸多样性、连锁不平衡及群体结构等相关的遗传信息, 这些研究主要建立在对候选基因序列进行测序分析的基础上。基于SNP的关联遗传学分析或连锁不平衡(Linkage disequilibrium, LD)作图, 已成为研究林木复杂数量性状的理想工具, 对桉树和火炬松的关联遗传学研究发现, 多个基因内的SNP位点与不同的木材性状相关联。利用SNP标记对林木遗传参数的估算从不同程度上揭示了林木群体进化规律及其生态学意义。SNP标记在林木中应用的不断深入, 必将极大地推动林木遗传育种学研究的发展。     

The genetic differentiation at quantitative trait loci under local adaptation

Most adaptive traits are controlled by large number of genes that may all together be the targets of selection. Adaptation may thus involve multiple but not necessarily substantial allele frequency changes. This has important consequences for the detection of selected loci and implies that a quantitative genetics framework may be more appropriate than the classical 'selective sweep' paradigm. Preferred methods to detect loci involved in local adaptation are based on the detection of 'outlier' values of the allelic differentiation F(ST) . A quantitative genetics framework is adopted here to review theoretical expectations for how allelic differentiation at quantitative trait loci (F(STQ) ) relates to (i), neutral genetic differentiation (F(ST) ) and (ii), phenotypic differentiation (Q(ST) ). We identify cases where results of outlier-based methods are likely to be poor and where differentiation at selected loci conveys little information regarding local adaptation. A first case is when neutral differentiation is high, so that local adaptation does not necessitate increased differentiation. A second case is when local adaptation is reached via an increased covariance of allelic effects rather than via allele frequency changes, which is more likely under high gene flow when the number of loci is high and selection is recent. The comparison of theoretical predictions with observed data from the literature suggests that polygenic local adaptation involving only faint allele frequency changes are very likely in some species such as forest trees and for climate-related traits. Recent methodological improvements that may alleviate the weakness of F(ST) -based detection methods are presented.     

Entering the second century of maize quantitative genetics

Maize is the most widely grown cereal in the world. In addition to its role in global agriculture, it has also long served as a model organism for genetic research. Maize stands at a genetic crossroads, as it has access to all the tools available for plant genetics but exhibits a genetic architecture more similar to other outcrossing organisms than to self-pollinating crops and model plants. In this review, we summarize recent advances in maize genetics, including the development of powerful populations for genetic mapping and genome-wide association studies (GWAS), and the insights these studies yield on the mechanisms underlying complex maize traits. Most maize traits are controlled by a large number of genes, and linkage analysis of several traits implicates a ‘common gene, rare allele'' model of genetic variation where some genes have many individually rare alleles contributing. Most natural alleles exhibit small effect sizes with little-to-no detectable pleiotropy or epistasis. Additionally, many of these genes are locked away in low-recombination regions that encourage the formation of multi-gene blocks that may underlie maize''s strong heterotic effect. Domestication left strong marks on the maize genome, and some of the differences in trait architectures may be due to different selective pressures over time. Overall, maize''s advantages as a model system make it highly desirable for studying the genetics of outcrossing species, and results from it can provide insight into other such species, including humans.     

Evolutionary genetics of juvenile hormone and ecdysteroid regulation in Gryllus: a case study in the microevolution of endocrine regulation

During the past 15 years the first detailed synthesis of endocrinology and population genetics has begun, in which natural genetic variations for endocrine regulators have been characterized, almost exclusively in species of the cricket genus Gryllus. Artificial selection studies have documented that regulators of the juvenile hormone titer can rapidly evolve and exhibit levels of genetic variability similar to other physiological traits. Strong genetic correlations exist between some but not all regulators of the JH titer during the juvenile stage. No genetic correlation exists between regulators functioning in juvenile and adult stages, and thus, endocrine regulation can evolve independently in these stages. Genetic variation in the JH titer, the ecdysteroid titer, and JHE activity, in adult and juvenile stages, have been documented in genetic stocks of wing-polymorphic crickets; morph-specific differences in these endocrine traits are potentially responsible for genetically based differences in aspects of wing and flight muscle development, adult egg production, and adult dispersal. An unexpected morph-specific, genetic polymorphism for a circadian rhythm for the JH titer was observed in both the laboratory and field. Few comparable studies exist in non-Gryllus species, in which in vivo endocrine-genetic variation has been directly quantified using reliable analytical methods; many reported cases of endocrine variation in these species have been obtained using an inappropriate method and thus should be considered unsubstantiated. Obtaining basic information on the characteristics of natural genetic variation for endocrine regulators still remains one of the most important tasks of the fledgling subdiscipline of evolutionary endocrinology. Single gene endocrine mutants in Drosophila are promising candidates for investigating molecular-genetic variation in natural populations. Future studies should also focus on endocrine traits studied in the field and geographic variation in endocrine regulation.     

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.     

Systems genetics approach reveals candidate genes for parasite resistance from quantitative trait loci studies in agricultural species

A systems genetics approach combining pathway analysis of quantitative trait loci (QTL) and gene expression information has provided strong evidence for common pathways associated with genetic resistance to internal parasites. Gene data, collected from published QTL regions in sheep, cattle, mice, rats and humans, and microarray data from sheep, were converted to human Entrez Gene IDs and compared to the KEGG pathway database. Selection of pathways from QTL data was based on a selection index that ensured that the selected pathways were in all species and the majority of the projects overall and within species. Pathways with either up- and down-regulated genes, primarily up-regulated genes or primarily down-regulated genes, were selected from gene expression data. After comparing the data sets independently, the pathways from each data set were compared and the common set of pathways and genes was identified. Comparisons within data sets identified 21 pathways from QTL data and 66 pathways from gene expression data. Both selected sets were enriched with pathways involved in immune functions, disease and cell responses to signals. The analysis identified 14 pathways that were common between QTL and gene expression data, and four directly associated with IFNγ or MHCII, with 31 common genes, including three MHCII genes. In conclusion, a systems genetics approach combining data from multiple QTL and gene expression projects led to the discovery of common pathways associated with genetic resistance to internal parasites. This systems genetics approach may prove significant for the discovery of candidate genes for many other multifactorial, economically important traits.     

GENOME DIVERGENCE AND THE GENETIC ARCHITECTURE OF BARRIERS TO GENE FLOW BETWEEN LYCAEIDES IDAS AND L. MELISSA

Genome divergence during speciation is a dynamic process that is affected by various factors, including the genetic architecture of barriers to gene flow. Herein we quantitatively describe aspects of the genetic architecture of two sets of traits, male genitalic morphology and oviposition preference, that putatively function as barriers to gene flow between the butterfly species Lycaeides idas and L. melissa. Our analyses are based on unmapped DNA sequence data and a recently developed Bayesian regression approach that includes variable selection and explicit parameters for the genetic architecture of traits. A modest number of nucleotide polymorphisms explained a small to large proportion of the variation in each trait, and average genetic variant effects were nonnegligible. Several genetic regions were associated with variation in multiple traits or with trait variation within‐ and among‐populations. In some instances, genetic regions associated with trait variation also exhibited exceptional genetic differentiation between species or exceptional introgression in hybrids. These results are consistent with the hypothesis that divergent selection on male genitalia has contributed to heterogeneous genetic differentiation, and that both sets of traits affect fitness in hybrids. Although these results are encouraging, we highlight several difficulties related to understanding the genetics of speciation.     

Putting the genes into community genetics

As part of the long‐term fusion of evolutionary biology and ecology (Ford, 1964), the field of community genetics has made tremendous progress in describing the impacts of plant genetic variation on community and ecosystem processes. In the “genes‐to‐ecosystems” framework (Whitham et al., 2003), genetically based traits of plant species have ecological consequences, but previous studies have not identified specific plant genes responsible for community phenotypes. The study by Barker et al. (2019) in this issue of Molecular Ecology uses an impressive common garden experiment of trembling aspen (Figure 1) to test for the genetic basis of tree traits that shape the insect community composition. Using a Genome‐Wide Association Study (GWAS), they found that genomic regions associated with phytochemical traits best explain variation in herbivore community composition, and identified specific genes associated with different types of leaf‐modifying herbivores and ants. This is one of the first studies to identify candidate genes underlying the heritable plant traits that explain patterns of insect biodiversity.     

Demographic and genetic interrelationships among the Gavlis of Dharwad

Lot of work has been done in recent years on the genetics of isolated and small population groups. But J. Sutter (1963) notes that these studies have not yielded satisfactory results, because these investigators have applied the formulae and models constructed by the mathematicians which are based on the assumption of panmixia, whereas panmixia cannot occur in human populations especially if the population is very small. Sometimes we speak of genetic drift and selection without taking into account the fact that the population at the same time is controlled by two most important demographic parameters of fertility and mortality which can alter genetic drift and selection. The geneticists are primarily interested in fertility. They want to determine, for any given couple, the number of offspring reaching the age of reproduction. One might therefore assume that the measurement of fertility should play a major role in population genetics. Thus, there is an urgent need for the establishment of meaningful relationship between demography and population genetics. In view of the above facts, an attempt is made in the present study to analyse the “Demographic and Genetic Interrelationships among the Gavlis of Dharwad” so as to throw light on some of the complex genetic issues like endogamy, inbreeding and selection potential.     

Pervasive genetic integration directs the evolution of human skull shape

It has long been unclear whether the different derived cranial traits of modern humans evolved independently in response to separate selection pressures or whether they resulted from the inherent morphological integration throughout the skull. In a novel approach to this issue, we combine evolutionary quantitative genetics and geometric morphometrics to analyze genetic and phenotypic integration in human skull shape. We measured human skulls in the ossuary of Hallstatt (Austria), which offer a unique opportunity because they are associated with genealogical data. Our results indicate pronounced covariation of traits throughout the skull. Separate simulations of selection for localized shape changes corresponding to some of the principal derived characters of modern human skulls produced outcomes that were similar to each other and involved a joint response in all of these traits. The data for both genetic and phenotypic shape variation were not consistent with the hypothesis that the face, cranial base, and cranial vault are completely independent modules but relatively strongly integrated structures. These results indicate pervasive integration in the human skull and suggest a reinterpretation of the selective scenario for human evolution where the origin of any one of the derived characters may have facilitated the evolution of the others.