Population genomics in Sardinia: a novel approach to hunt for genomic combinations underlying complex traits and diseases.

The availability of highly polymorphic markers permits testing whether complex traits and diseases result from genomic interactions between nonallelic normal variants at separate loci. Such variants may be identified by deviations from the expected distributions of alleles at a high number of polymorphic loci, when individuals with the phenotype of interest are compared to normal controls of the same breeding unit, provided that both groups share the same remote ancestry and had no ancestors in common for the last three to four generations. The circumstances needed for such studies are ideally met on the island of Sardinia. The recurrent finding of the same type of association in separate breeding units between the phenotype of interest and a given genotype should allow a distinction between true genetic identity by descent and randomly occurring identities, as these will be obviously different in separate breeding units. The availability of several breeding units located in sharply different ecological environments will permit assessment of the role of nature/nurture factors in the degree of manifestation of each newly discovered genotype/phenotype association. A pilot study to evaluate the proposed strategy has been carried out in the Sardinian village of Carloforte, a community of about 8,000 individuals who have remained genetically homogeneous. Fifty-five control samples have been genotyped with six tetranucleotide microsatellites and with a subset of the 400 markers contained in the ABI PRISM linkage mapping panel, version 2. The allele frequencies for these microsatellite markers have been determined for these 55 individuals and compared to those from a random sampling of subsets of these 55 persons. For the six tetranucleotide microsatellites, a subset of as few as 20 people displayed the same allele frequency distributions as observed with the original 55 unrelated individuals. In conclusion, when samples are chosen from the same breeding unit, the number of individuals sufficient to draw the genomic profile of an isolated population can be relatively small. Likewise, the number of probands with the phenotype of interest can be even smaller when they are ascertained with the same genealogical criteria as the normal controls. By comparing the genomic profile of the probands to a fraction of the control samples within each of several separate breeding units of common remote ancestry, the search for genotype/phenotype association for mono- and multifactorial traits and diseases should be simplified and yield unequivocal results.     

Prospects for increasing yield in macadamia using component traits and genomics

Selection of candidate cultivars in macadamia requires extensive phenotypic measurements over many years and trials. In particular, yield traits such as nut-in-shell yield and kernel yield are economically vital characteristics and therefore guide the selection process for new cultivars. However, these traits can only be measured in mature trees, resulting in long generation intervals and slow rates of genetic gain. In addition, these traits are expensive to measure. Strategies to reduce the generation interval and increase the intensity of selection include using yield component traits, identification of markers associated with component traits, and genomic selection for yield. Yield component traits that contribute to resource availability for fruit formation include floral and nut characteristics. In this review, these traits will be investigated to estimate their relative importance in macadamia breeding and their heritability and correlations with yield. Furthermore, the usefulness of genome-wide association studies regarding yield component traits will be reviewed. Genetic-based breeding techniques could exploit this information to increase yield gains per breeding cycle and estimate the quantitative nature of yield traits. Genomic selection uses genome-wide molecular markers to predict the phenotype of individuals at an early age before maturity, thereby reducing the cycle time and increasing gain per unit time in plant breeding programmes. This review evaluates the potential for measurement of yield component traits, genome-wide association studies, and genomic selection to be employed in the Australian macadamia breeding programme to accelerate gains for nut yield.     

From complex traits to complex alleles

Several recent studies using natural populations of Drosophila show that one must be very careful when sorting among the large number of molecular polymorphisms found at most loci to identify the nucleotide changes responsible for phenotypic variation in complex traits. Indeed, several mutations within a single allele can interact to generate the overall observed effect. The results are instructive both for those interested in the genetics of evolutionary change and for those attempting to ferret out the genetic basis of complex human diseases.     

Use of population isolates for mapping complex traits

Geneticists have repeatedly turned to population isolates for mapping and cloning Mendelian disease genes. Population isolates possess many advantages in this regard. Foremost among these is the tendency for affected individuals to share ancestral haplotypes derived from a handful of founders. These haplotype signatures have guided scientists in the fine mapping of scores of rare disease genes. The past successes with Mendelian disorders using population isolates have prompted unprecedented interest among medical researchers in both the public and private sectors. Despite the obvious genetic and environmental complications, geneticists have targeted several population isolates for mapping genes for complex diseases.     

Complex traits analysis of chicken growth using targeted genetical genomics

Dissecting the genetic control of complex trait variation remains very challenging, despite many advances in technology. The aim of this study was to use a major growth quantitative trait locus (QTL) in chickens mapped to chromosome 4 as a model for a targeted approach to dissect the QTL. We applied a variant of the genetical genomics approach to investigate genome-wide gene expression differences between two contrasting genotypes of a marked QTL. This targeted approach allows the direct quantification of the link between the genotypes and the genetic responses, thus narrowing the QTL-phenotype gap using fewer samples (i.e. microarrays) compared with the genome-wide genetical genomics studies. Four differentially expressed genes were localized under the region of the QTL. One of these genes is a potential positional candidate gene (AADAT) that affects lysine and tryptophan metabolism and has alternative splicing variants between the two genotypes. In addition, the lysine and glycolysis metabolism pathways were significantly enriched for differentially expressed genes across the genome. The targeted approach provided a complementary route to fine mapping of QTL by characterizing the local and the global downstream effects of the QTL and thus generating further hypotheses about the action of that QTL.     

Cost-effective designs for linkage disequilibrium mapping of complex traits

The current development of densely spaced collections of single nucleotide polymorphisms (SNPs) will lead to genomewide association studies for a wide range of diseases in many different populations. Determinations of the appropriate number of SNPs to genotype involve a balancing of power and cost. Several variables are important in these determinations. We show that there are different combinations of sample size and marker density that can be expected to achieve the same power. Within certain bounds, investigators can choose between designs with more subjects and fewer markers or those with more markers and fewer subjects. Which designs are more cost-effective depends on the cost of phenotyping versus the cost of genotyping. We show that, under the assumption of a set cost for genotyping, one can calculate a "threshold cost" for phenotyping; when phenotyping costs per subject are less than this threshold, designs with more subjects will be more cost-effective than designs with more markers. This framework for determining a cost-effective study will aid in the planning of studies, especially if there are choices to be made with respect to phenotyping methods or study populations.     

Genome-wide association studies for common diseases and complex traits

Genetic factors strongly affect susceptibility to common diseases and also influence disease-related quantitative traits. Identifying the relevant genes has been difficult, in part because each causal gene only makes a small contribution to overall heritability. Genetic association studies offer a potentially powerful approach for mapping causal genes with modest effects, but are limited because only a small number of genes can be studied at a time. Genome-wide association studies will soon become possible, and could open new frontiers in our understanding and treatment of disease. However, the execution and analysis of such studies will require great care.     

Whole-genome linkage disequilibrium screening for complex traits in horses

The identification of candidate genes for significant traits is crucial. In this study, we developed and tested effective and systematic methods based on linkage disequilibrium (LD) for the identification of candidate regions for genes with Mendelian inheritance and those associated with complex traits. Our approach entailed the combination of primary screening using pooled DNA samples based on ΔTAC, secondary screening using an individual typing method and tertiary screening using a permutation test based on the differences in the haplotype frequency between two neighbouring microsatellites. This series of methods was evaluated using horse coat colour traits (chestnut/non-chestnut) as a simple Mendelian inheritance model. In addition, the methods were evaluated using a complex trait model constructed by mixing samples from chestnut and non-chestnut horses. Using both models, the methods could detect the expected regions for the horse coat colour trait. The results revealed that LD extends up to several centimorgans in horses, indicating that whole-genome LD screening in horses could be performed systematically and efficiently by combining the above-mentioned methods. Since genetic maps based on microsatellites have been constructed for many other species, the approaches present here could have wide applicability.     

A fast algorithm for functional mapping of complex traits

By integrating the underlying developmental mechanisms for the phenotypic formation of traits into a mapping framework, functional mapping has emerged as an important statistical approach for mapping complex traits. In this note, we explore the feasibility of using the simplex algorithm as an alternative to solve the mixture-based likelihood for functional mapping of complex traits. The results from the simplex algorithm are consistent with those from the traditional EM algorithm, but the simplex algorithm has considerably reduced computational times. Moreover, because of its nonderivative nature and easy implementation with current software, the simplex algorithm enjoys an advantage over the EM algorithm in the dynamic modeling and analysis of complex traits.     

A non-stationary model for functional mapping of complex traits

SUMMARY: Understanding the genetic control of growth is fundamental to agricultural, evolutionary and biomedical genetic research. In this article, we present a statistical model for mapping quantitative trait loci (QTL) that are responsible for genetic differences in growth trajectories during ontogenetic development. This model is derived within the maximum likelihood context, implemented with the expectation-maximization algorithm. We incorporate mathematical aspects of growth processes to model the mean vector and structured antedependence models to approximate time-dependent covariance matrices for longitudinal traits. Our model has been employed to map QTL that affect body mass growth trajectories in both male and female mice of an F2 population derived from the Large and Small mouse strains. The results from this model are compared with those from the autoregressive-based functional mapping approach. Based on results from computer simulation studies, we suggest that these two models are alternative to one another and should be used simultaneously for the same dataset.     

Biobanks for genomics and genomics for biobanks

Biobanks include biological samples and attached databases. Human biobanks occur in research, technological development and medical activities. Population genomics is highly dependent on the availability of large biobanks. Ethical issues must be considered: protecting the rights of those people whose samples or data are in biobanks (information, autonomy, confidentiality, protection of private life), assuring the non-commercial use of human body elements and the optimal use of samples and data. They balance other issues, such as protecting the rights of researchers and companies, allowing long-term use of biobanks while detailed information on future uses is not available. At the level of populations, the traditional form of informed consent is challenged. Other dimensions relate to the rights of a group as such, in addition to individual rights. Conditions of return of results and/or benefit to a population need to be defined. With 'large-scale biobanking' a marked trend in genomics, new societal dimensions appear, regarding communication, debate, regulation, societal control and valorization of such large biobanks. Exploring how genomics can help health sector biobanks to become more rationally constituted and exploited is an interesting perspective. For example, evaluating how genomic approaches can help in optimizing haematopoietic stem cell donor registries using new markers and high-throughput techniques to increase immunogenetic variability in such registries is a challenge currently being addressed. Ethical issues in such contexts are important, as not only individual decisions or projects are concerned, but also national policies in the international arena and organization of democratic debate about science, medicine and society.     

Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.     

Mapping complex traits using Random Forests

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.     

Analyzing complex traits with congenic strains

Congenic strains continue to be a fundamental resource for dissecting the genetic basis of complex traits. Traditionally, genetic variants (QTLs) that account for phenotypic variation in a panel of congenic strains are sought first by comparing phenotypes for each strain to the host (reference) strain, and then by examining the results to identify a common chromosome segment that provides the best match between genotype and phenotype across the panel. However, this “common-segment” method has significant limitations, including the subjective nature of the genetic model and an inability to deal formally with strain phenotypes that do not fit the model. We propose an alternative that we call “sequential” analysis and that is based on a unique principle of QTL analysis where each strain, corresponding to a single genotype, is tested individually for QTL effects rather than testing the congenic panel collectively for common effects across heterogeneous backgrounds. A minimum spanning tree, based on principles of graph theory, is used to determine the optimal sequence of strain comparisons. For two traits in two panels of congenic strains in mice, we compared results for the sequential method with the common-segment method as well as with two standard methods of QTL analysis, namely, interval mapping and multiple linear regression. The general utility of the sequential method was demonstrated with analysis of five additional traits in congenic panels from mice and rats. Sequential analysis rigorously resolved phenotypic heterogeneity among strains in the congenic panels and found QTLs that other methods failed to detect.     

Meta-analysis for microarray studies of the genetics of complex traits

In comparison to other complex disease traits, alcoholism and alcohol abuse are influenced by the combined effects of many genes that alter susceptibility, phenotypic expression and associated morbidity, respectively. Many genetic studies, in both animal models and humans, have identified genetic intervals containing genes that influence alcoholism or behavioral responses to ethanol. Concurrently, a growing number of microarray studies have identified gene expression differences related to ethanol drinking or other ethanol behaviors. However, concerns about the statistical power of these experiments, combined with the complexity of the underlying phenotypes, have greatly hampered the identification of candidate genes underlying ethanol behaviors. Meta-analysis approaches using recent compilations of large datasets of microarray, behavioral and genetic data promise improved statistical power for detecting the genes or gene networks affecting ethanol behaviors and other complex traits.     

Predicting unobserved phenotypes for complex traits from whole-genome SNP data

Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.     

Genetics of personalities: no simple answers for complex traits

Identifying the genes that underlie phenotypic variation in natural populations, and assessing the consequences of polymorphisms at these loci for individual fitness are major objectives in evolutionary biology. Yet, with the exception of a few success stories, little progress has been made, and our understanding of the link between genotype and phenotype is still in its infancy. For example, although body length in humans is largely genetically determined, with heritability estimates greater than 0.8, massive genome‐wide association studies (GWAS) have only been able to account for a very small proportion of this variation ( Gudbjartsson et al. 2008 ). If it is so difficult to explain the genetics behind relatively ‘simple’ traits, can we envision that it will at all be possible to find genes underlying complex behavioural traits in wild non‐model organisms? Some notable examples suggest that this can indeed be a worthwhile endeavour. Recently, the circadian rhythm gene Clock has been associated with timing of breeding in a wild blue tit population ( Johnsen et al. 2007 ; Liedvogel et al. 2009 ) and the Pgi gene to variation in dispersal and flight endurance in Glanville fritillary butterflies ( Niitepold et al. 2009 ). A promising candidate gene for influencing complex animal personality traits, also known as behavioural syndromes ( Sih et al. 2004 ), is the dopamine receptor D4 (DRD4) gene. Within the last decade, polymorphisms in this gene have been associated with variation in novelty seeking and exploration behaviour in a range of species, from humans to great tits ( Schinka et al. 2002 ; Fidler et al. 2007 ). In this issue, Korsten et al. (2010) attempt to replicate this previously observed association in wild‐living birds, and test for the generality of the association between DRD4 and personality across a number of European great tit populations.