Beatriz Villanueva

The Impact of Genetic Architecture on Genome-Wide Evaluation Methods

The rapid increase in high-throughput single-nucleotide polymorphism data has led to a great interest in applying genome-wide evaluation methods to identify an individuals genetic merit. Genome-wide evaluation combines statistical methods with genomic data to predict genetic values for complex traits. Considerable uncertainty currently exists in determining which genome-wide evaluation method is the most appropriate. We hypothesize that genome-wide methods deal differently with the genetic architecture of quantitative traits and genomes. A genomic linear method (GBLUP), and a genomic nonlinear Bayesian variable selection method (BayesB) are compared using stochastic simulation across three effective population sizes and a wide range of numbers of quantitative trait loci (NQTL). GBLUP had a constant accuracy, for a given heritability and sample size, regardless of NQTL. BayesB had a higher accuracy than GBLUP when NQTL was low, but this advantage diminished as NQTL increased and when NQTL became large, GBLUP slightly outperformed BayesB. In addition, deterministic equations are extended to predict the accuracy of both methods and to estimate the number of independent chromosome segments (Me) and NQTL. The predictions of accuracy and estimates of Me and NQTL were generally in good agreement with results from simulated data. We conclude that the relative accuracy of GBLUP and BayesB for a given number of records and heritability are highly dependent on Me, which is a property of the target genome, as well as the architecture of the trait (NQTL).

Accuracy of Predicting the Genetic Risk of Disease Using a Genome-Wide Approach

Background The prediction of the genetic disease risk of an individual is a powerful public health tool. While predicting risk has been successful in diseases which follow simple Mendelian inheritance, it has proven challenging in complex diseases for which a large number of loci contribute to the genetic variance. The large numbers of single nucleotide polymorphisms now available provide new opportunities for predicting genetic risk of complex diseases with high accuracy. Methodology/Principal Findings We have derived simple deterministic formulae to predict the accuracy of predicted genetic risk from population or case control studies using a genome-wide approach and assuming a dichotomous disease phenotype with an underlying continuous liability. We show that the prediction equations are special cases of the more general problem of predicting the accuracy of estimates of genetic values of a continuous phenotype. Our predictive equations are responsive to all parameters that affect accuracy and they are independent of allele frequency and effect distributions. Deterministic prediction errors when tested by simulation were generally small. The common link among the expressions for accuracy is that they are best summarized as the product of the ratio of number of phenotypic records per number of risk loci and the observed heritability. Conclusions/Significance This study advances the understanding of the relative power of case control and population studies of disease. The predictions represent an upper bound of accuracy which may be achievable with improved effect estimation methods. The formulae derived will help researchers determine an appropriate sample size to attain a certain accuracy when predicting genetic risk.

Using genome-wide information to minimize the loss of diversity in conservation programmes.

We study here the effect of using genome-wide marker data versus genealogical data in population management for the maintenance of diversity in conservation schemes using optimal contributions. We re-examine the benefits of using molecular data for different population and genome sizes and compare different management strategies according to the group of individuals where we take decisions (parents or offspring). We also study the consequences of using estimated genealogical coancestries calculated from molecular information. Using genome-wide marker data performed usually better than using genealogical data or estimated genealogical coancestry to maintain expected and observed heterozygosity. Furthermore, when we could take decisions acting on the offspring, a larger heterozygosity was maintained than when we based our decisions on the potential parents.

Using genomic tools to maintain diversity and fitness in conservation programmes

Conservation programmes aim at maximizing the survival probability of populations, by minimizing the loss of genetic diversity, which allows populations to adapt to changes, and controlling inbreeding increases. The best known strategy to achieve these goals is optimizing the contributions of the parents to minimize global coancestry in their offspring. Results on neutral scenarios showed that management based on molecular coancestry could maintain more diversity than management based on genealogical coancestry when a large number of markers were available. However, if the population has deleterious mutations, managing using optimal contributions can lead to a decrease in fitness, especially using molecular coancestry, because both beneficial and harmful alleles are maintained, compromising the long‐term viability of the population. We introduce here two strategies to avoid this problem: The first one uses molecular coancestry calculated removing markers with low minor allele frequencies, as they could be linked to selected loci. The second one uses a coancestry based on segments of identity by descent, which measures the proportion of genome segments shared by two individuals because of a common ancestor. We compare these strategies under two contrasting mutational models of fitness effects, one assuming many mutations of small effect and another with few mutations of large effect. Using markers at intermediate frequencies maintains a larger fitness than using all markers, but leads to maintaining less diversity. Using the segment‐based coancestry provides a compromise solution between maintaining diversity and fitness, especially when the population has some inbreeding load.

Disease Resistance in Atlantic Salmon (Salmo salar): Coinfection of the Intracellular Bacterial Pathogen Piscirickettsia salmonis and the Sea Louse Caligus rogercresseyi

Background Naturally occurring coinfections of pathogens have been reported in salmonids, but their consequences on disease resistance are unclear. We hypothesized that 1) coinfection of Caligus rogercresseyi reduces the resistance of Atlantic salmon to Piscirickettsia salmonis; and 2) coinfection resistance is a heritable trait that does not correlate with resistance to a single infection. Methodology In total, 1,634 pedigreed Atlantic salmon were exposed to a single infection (SI) of P. salmonis (primary pathogen) or coinfection with C. rogercresseyi (secondary pathogen). Low and high level of coinfection were evaluated (LC = 44 copepodites per fish; HC = 88 copepodites per fish). Survival and quantitative genetic analyses were performed to determine the resistance to the single infection and coinfections. Main Findings C. rogercresseyi significantly increased the mortality in fish infected with P. salmonis (SI mortality = 251/545; LC mortality = 544/544 and HC mortality = 545/545). Heritability estimates for resistance to P. salmonis were similar and of medium magnitude in all treatments (h 2 SI = 0.23±0.07; h 2 LC = 0.17±0.08; h 2 HC = 0.24±0.07). A large and significant genetic correlation with regard to resistance was observed between coinfection treatments (rg LC-HC = 0.99±0.01) but not between the single and coinfection treatments (rg SI-LC = −0.14±0.33; rg SI-HC = 0.32±0.34). Conclusions/Significance C. rogercresseyi, as a secondary pathogen, reduces the resistance of Atlantic salmon to the pathogen P. salmonis. Resistance to coinfection of Piscirickettsia salmonis and Caligus rogercresseyi in Atlantic salmon is a heritable trait. The absence of a genetic correlation between resistance to a single infection and resistance to coinfection indicates that different genes control these processes. Coinfection of different pathogens and resistance to coinfection needs to be considered in future research on salmon farming, selective breeding and conservation.

Novel methods for quantifying individual host response to infectious pathogens for genetic analyses

We propose two novel approaches for describing and quantifying the response of individual hosts to pathogen challenge in terms of infection severity and impact on host performance. The first approach is a direct extension of the methodology for estimating group tolerance (the change in performance with respect to changes in pathogen burden in a host population) to the level of individuals. The second approach aims to capture the dynamic aspects of individual resistance and tolerance over the entire time course of infections. In contrast to the first approach, which provides a means to disentangle host resistance from tolerance, the second approach focuses on the combined effects of both characteristics. Both approaches provide new individual phenotypes for subsequent genetic analyses and come with specific data requirements. In particular, both approaches rely on the availability of repeated performance and pathogen burden measurements of individuals over the time course of one or several episodes of infection. Consideration of individual tolerance also highlights some of the assumptions hidden within the concept of group tolerance, indicating where care needs to be taken in trait definition and measurement.

Potential benefit from using an identified major gene in BLUP evaluation with truncation and optimal selection

This study investigates the benefit of including information on an identi- fied major gene in the estimation of breeding values in BLUP selection programmes. Selection for a quantitative trait is controlled by polygenes and a major locus with known effect. The benefit of using the gene information obtained in the short-term was maintained in the long-term by applying a selection tool which makes use of BLUP evaluation and optimisation of genetic contributions for maximising genetic gain while restricting the rate of inbreeding. In the mixed inheritance model the se- lection tool, initially proposed for an infinitesimal model, was able to restrict the rate of inbreeding to the desired value and to give higher rates of response than standard truncation selection both when using and ignoring the information on the major gene. The simple use of BLUP (standard truncation selection) allowed long-term benefits from using the gene in situations where the favourable allele was recessive or additive with large effect.

Genome-wide estimates of coancestry and inbreeding in a closed herd of ancient Iberian pigs.

Maintaining genetic variation and controlling the increase in inbreeding are crucial requirements in animal conservation programs. The most widely accepted strategy for achieving these objectives is to maximize the effective population size by minimizing the global coancestry obtained from a particular pedigree. However, for most natural or captive populations genealogical information is absent. In this situation, microsatellites have been traditionally the markers of choice to characterize genetic variation, and several estimators of genealogical coefficients have been developed using marker data, with unsatisfactory results. The development of high-throughput genotyping techniques states the necessity of reviewing the paradigm that genealogical coancestry is the best parameter for measuring genetic diversity. In this study, the Illumina PorcineSNP60 BeadChip was used to obtain genome-wide estimates of rates of coancestry and inbreeding and effective population size for an ancient strain of Iberian pigs that is now in serious danger of extinction and for which very accurate genealogical information is available (the Guadyerbas strain). Genome-wide estimates were compared with those obtained from microsatellite and from pedigree data. Estimates of coancestry and inbreeding computed from the SNP chip were strongly correlated with genealogical estimates and these correlations were substantially higher than those between microsatellite and genealogical coefficients. Also, molecular coancestry computed from SNP information was a better predictor of genealogical coancestry than coancestry computed from microsatellites. Rates of change in coancestry and inbreeding and effective population size estimated from molecular data were very similar to those estimated from genealogical data. However, estimates of effective population size obtained from changes in coancestry or inbreeding differed. Our results indicate that genome-wide information represents a useful alternative to genealogical information for measuring and maintaining genetic diversity.

Implications of Host Genetic Variation on the Risk and Prevalence of Infectious Diseases Transmitted Through the Environment

Previous studies have shown that host genetic heterogeneity in the response to infectious challenge can affect the emergence risk and the severity of diseases transmitted through direct contact between individuals. However, there is substantial uncertainty about the degree and direction of influence owing to different definitions of genetic variation, most of which are not in line with the current understanding of the genetic architecture of disease traits. Also, the relevance of previous results for diseases transmitted through environmental sources is unclear. In this article a compartmental genetic–epidemiological model was developed to quantify the impact of host genetic diversity on epidemiological characteristics of diseases transmitted through a contaminated environment. The model was parameterized for footrot in sheep. Genetic variation was defined through continuous distributions with varying shape and degree of dispersion for different disease traits. The model predicts a strong impact of genetic heterogeneity on the disease risk and its progression and severity, as well as on observable host phenotypes, when dispersion in key epidemiological parameters is high. The impact of host variation depends on the disease trait for which variation occurs and on environmental conditions affecting pathogen survival. In particular, compared to homogeneous populations with the same average susceptibility, disease risk and severity are substantially higher in populations containing a large proportion of highly susceptible individuals, and the differences are strongest when environmental contamination is low. The implications of our results for the recording and analysis of disease data and for predicting response to selection are discussed.

Purging deleterious mutations in conservation programmes: combining optimal contributions with inbred matings

Conservation programmes aim at minimising the loss of genetic diversity, which allows populations to adapt to potential environmental changes. This can be achieved by calculating how many offspring every individual should contribute to the next generation to minimise global coancestry. However, an undesired consequence of this strategy is that it maintains deleterious mutations, compromising the viability of the population. In order to avoid this, optimal contributions could be combined with inbred matings, to expose and eliminate recessive deleterious mutations by natural selection in a process known as purging. Although some populations that have undergone purging experienced reduced inbreeding depression, this effect is not consistent across species. Whether purging by inbred matings is efficient in conservation programmes depends on the balance between the loss of diversity, the initial decrease in fitness and the reduction in mutational load. Here we perform computer simulations to determine whether managing a population by combining optimal contributions with inbred matings improves its long-term viability while keeping reasonable levels of diversity. We compare the management based on genealogical information with management based on molecular data to calculate coancestries. In the scenarios analysed, inbred matings never led to higher fitness and usually maintained lower diversity than random or minimum coancestry matings. Replacing genealogical with molecular coancestry can maintain a larger genetic diversity but can also lead to a lower fitness. Our results are strongly dependent on the mutational model assumed for the trait under selection, the population size during management and the reproductive rate.