SYSTEMS OF MATING. I. THE BIOMETRIC RELATIONS BETWEEN PARENT AND OFFSPRING

Rates of inbreeding and genetic adaptation for populations managed as herds in zoos with a rotational mating system or with optimized contribution of parents

This study compares two genetic management scenarios for species kept in herds, such as deer. The simulations were designed so that their results can be extended to a wide range of zoo populations. In the first scenario, the simulated populations of size 3 × 20, 6 × 40 or 20 × 60 (herds × animals in herd) were managed with a rotational mating (RM) scheme in which 10%, 20% or 50% of males were selected for breeding and moved between herds in a circular fashion. The second scenario was based on optimal contribution theory (OC). OC requires an accurate pedigree to calculate kinship; males were selected and assigned numbers of offspring to minimize kinship in the next generation. RM was efficient in restriction of inbreeding and produced results comparable with OC. However, RM can result in genetic adaptation of the population to the zoo environment, in particular when 20% or less males are selected for rotation and selection of animals is not random. Lowest rates of inbreeding were obtained by combining OC with rotation of males as in the RM scheme. RM is easy to implement in practice and does not require pedigree data. When full pedigree is available, OC management is preferable.

Distinct Yellowfin Tuna (Thunnus albacares) Stocks Detected in Western and Central Pacific Ocean (WCPO) Using DNA Microsatellites

The yellowfin tuna, Thunnus albacares (Bonnaterre, 1788), covers majority of the Philippines' tuna catch, one of the major fisheries commodities in the country. Due to its high economic importance sustainable management of these tunas has become an imperative measure to prevent stock depletion. Currently, the Philippine yellowfin tuna is believed to be part of a single stock of the greater WCPO though some reports suggest otherwise. This study therefore aims to establish the genetic stock structure of the said species in the Philippines as compared to Bismarck Sea, Papua New Guinea using nine (9) DNA microsatellite markers. DNA microsatellite data revealed significant genetic differentiation between the Philippine and Bismarck Sea, Papua New Guinea yellowfin tuna samples. (FST = 0.034, P = 0.016), which is further supported by multilocus distance matrix testing (PCoA) and model-based clustering (STRUCTURE 2.2).With these findings, this study posits that the yellowfin tuna population in the Philippines is a separate stock from the Bismarck Sea population. These findings add evidence to the alternative hypothesis of having at least 2 subpopulations of yellowfin tuna in the WCPO and calls for additional scientific studies using other parameters to investigate this. Accurate population information is necessary in formulating a more appropriate management strategy for the sustainability of the yellowfin tuna not only in the Philippines but also in the WCPO.

Pedigrees or markers: Which are better in estimating relatedness and inbreeding coefficient?

Individual inbreeding coefficient (F) and pairwise relatedness (r) are fundamental parameters in population genetics and have important applications in diverse fields such as human medicine, forensics, plant and animal breeding, conservation and evolutionary biology. Traditionally, both parameters are calculated from pedigrees, but are now increasingly estimated from genetic marker data. Conceptually, a pedigree gives the expected F and r values, FP and rP, with the expectations being taken (hypothetically) over an infinite number of individuals with the same pedigree. In contrast, markers give the realised (actual) F and r values at the particular marker loci of the particular individuals, FM and rM. Both pedigree (FP, rP) and marker (FM, rM) estimates can be used as inferences of genomic inbreeding coefficients FG and genomic relatedness rG, which are the underlying quantities relevant to most applications (such as estimating inbreeding depression and heritability) of F and r. In the pre-genomic era, it was widely accepted that pedigrees are much better than markers in delineating FG and rG, and markers should better be used to validate, amend and construct pedigrees rather than to replace them. Is this still true in the genomic era when genome-wide dense SNPs are available? In this simulation study, I showed that genomic markers can yield much better estimates of FG and rG than pedigrees when they are numerous (say, 10(4) SNPs) under realistic situations (e.g. genome and population sizes). Pedigree estimates are especially poor for species with a small genome, where FG and rG are determined to a large extent by Mendelian segregations and may thus deviate substantially from their expectations (FP and rP). Simulations also confirmed that FM, when estimated from many SNPs, can be much more powerful than FP for detecting inbreeding depression in viability. However, I argue that pedigrees cannot be replaced completely by genomic SNPs, because the former allows for the calculation of more complicated IBD coefficients (involving more than 2 individuals, more than one locus, and more than 2 genes at a locus) for which the latter may have reduced capacity or limited power, and because the former has social and other significance for remote relationships which have little genetic significance and cannot be inferred reliably from markers.

Hazards inherent in interdisciplinary behavioral research

Many, if not all, questions in biology and psychology today were formulated and considered in depth, though typically in a different language, from the 1700's to the early 1900's. However, because of politics or fashion, some topics fell out of favor or failed to recruit new scientists and hence languished. Despite greatly expanded scholarship in the history of the life sciences in the twentieth century, many such topics have had to be rediscovered in recent years, while much of the wisdom already accrued stays in the older literature and not in active minds. This is particularly true today when scientific advances appear at breakneck speed. It would not be an exaggeration to say that many 'breakthroughs' turn out really to be rediscoveries of forgotten observations. Two areas of particular significance to the interdisciplinary study of behavior are the Norms of Reaction (from Biology) and the concept of Plasticity (from Psychology). These and related fields benefit from the perspective of epigenetics so long as rigorous operational definitions are implemented. It is also important to revive Hogben's admonition that the interaction of hereditary and environment cannot be understood outside of the context of development. Five examples of increasing complexity in phenotypic plasticity in brain and behavior are presented to illustrate this perspective.

The use and abuse of genetic marker-based estimates of relatedness and inbreeding

Genetic marker-based estimators remain a popular tool for measuring relatedness (r xy ) and inbreeding (F) coefficients at both the population and individual level. The performance of these estimators fluctuates with the number and variability of markers available, and the relatedness composition and demographic history of a population. Several methods are available to evaluate the reliability of the estimates of r xy and F, some of which are implemented in the program COANCESTRY. I used the simulation module in COANCESTRY since assess the performance of marker-based estimators of r xy and F in a species with very low genetic diversity, New Zealand's little spotted kiwi (Apteryx owenii). I also conducted a review of published papers that have used COANCESTRY as its release to assess whether and how the reliability of the estimates of r xy and F produced by genetic markers are being measured and reported in published studies. My simulation results show that even when the correlation between true (simulated) and estimated r xy or F is relatively high (Pearson's r = 0.66-0.72 and 0.81-0.85, respectively) the imprecision of the estimates renders them highly unreliable on an individual basis. The literature review demonstrates that the majority of studies do not report the reliability of marker-based estimates of r xy and F. There is currently no standard practice for selecting the best estimator for a given data set or reporting an estimator's performance. This could lead to experimental results being interpreted out of context and render the robustness of conclusions based on measures of r xy and F debatable.

Mapping complex traits as a dynamic system

Despite increasing emphasis on the genetic study of quantitative traits, we are still far from being able to chart a clear picture of their genetic architecture, given an inherent complexity involved in trait formation. A competing theory for studying such complex traits has emerged by viewing their phenotypic formation as a "system" in which a high-dimensional group of interconnected components act and interact across different levels of biological organization from molecules through cells to whole organisms. This system is initiated by a machinery of DNA sequences that regulate a cascade of biochemical pathways to synthesize endophenotypes and further assemble these endophenotypes toward the end-point phenotype in virtue of various developmental changes. This review focuses on a conceptual framework for genetic mapping of complex traits by which to delineate the underlying components, interactions and mechanisms that govern the system according to biological principles and understand how these components function synergistically under the control of quantitative trait loci (QTLs) to comprise a unified whole. This framework is built by a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function, and provides a quantitative and testable platform for assessing the multiscale interplay between QTLs and development. The method will enable geneticists to shed light on the genetic complexity of any biological system and predict, alter or engineer its physiological and pathological states.

Prevalence estimation for monogenic autosomal recessive diseases using population-based genetic data

Genetic methods can complement epidemiological surveys and clinical registries in determining prevalence of monogenic autosomal recessive diseases. Several large population-based genetic databases, such as the NHLBI GO Exome Sequencing Project, are now publically available. By assuming Hardy-Weinberg equilibrium, the frequency of individuals homozygous in the general population for a particular pathogenic allele can be directly calculated from a sample of chromosomes where some harbor the pathogenic allele. Further assuming that the penetrance of the pathogenic allele(s) is known, the prevalence of recessive phenotypes can be determined. Such work can inform public health efforts for rare recessive diseases. A Bayesian estimation procedure has yet to be applied to the problem of estimating disease prevalence from large population-based genetic data. A Bayesian framework is developed to derive the posterior probability density of monogenic, autosomal recessive phenotypes. Explicit equations are presented for the credible intervals of these disease prevalence estimates. A primary impediment to performing accurate disease prevalence calculations is the determination of truly pathogenic alleles. This issue is discussed, but in many instances remains a significant barrier to investigations solely reliant on statistical interrogation--functional studies can provide important information for solidifying evidence of variant pathogenicity. We also discuss several challenges to these efforts, including the population structure in the sample of chromosomes, the treatment of allelic heterogeneity, and reduced penetrance of pathogenic variants. To illustrate the application of these methods, we utilized recently published genetic data collected on a large sample from the Schmiedeleut Hutterites. We estimate prevalence and calculate 95% credible intervals for 13 autosomal recessive diseases using these data. In addition, the Bayesian estimation procedure is applied to data from a central European study of hereditary fructose intolerance. The methods described herein show a viable path to robustly estimating both the expected prevalence of autosomal recessive phenotypes and corresponding credible intervals using population-based genetic databases that have recently become available. As these genetic databases increase in number and size with the advent of cost-effective next-generation sequencing, we anticipate that these methods and approaches may be helpful in recessive disease prevalence calculations, potentially impacting public health management, health economic analyses, and treatment of rare diseases.

On the explaining-away phenomenon in multivariate latent variable models

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples.

The genetic and environmental roots of variance in negativity toward foreign nationals

This study quantified genetic and environmental roots of variance in prejudice and discriminatory intent toward foreign nationals and examined potential mediators of these genetic influences: right-wing authoritarianism (RWA), social dominance orientation (SDO), and narrow-sense xenophobia (NSX). In line with the dual process motivational (DPM) model, we predicted that the two basic attitudinal and motivational orientations-RWA and SDO-would account for variance in out-group prejudice and discrimination. In line with other theories, we expected that NSX as an affective component would explain additional variance in out-group prejudice and discriminatory intent. Data from 1,397 individuals (incl. twins as well as their spouses) were analyzed. Univariate analyses of twins' and spouses' data yielded genetic (incl. contributions of assortative mating) and multiple environmental sources (i.e., social homogamy, spouse-specific, and individual-specific effects) of variance in negativity toward strangers. Multivariate analyses suggested an extension to the DPM model by including NSX in addition to RWA and SDO as predictor of prejudice and discrimination. RWA and NSX primarily mediated the genetic influences on the variance in prejudice and discriminatory intent toward foreign nationals. In sum, the findings provide the basis of a behavioral genetic framework integrating different scientific disciplines for the study of negativity toward out-groups.

Allelic richness following population founding events--a stochastic modeling framework incorporating gene flow and genetic drift

Allelic richness (number of alleles) is a measure of genetic diversity indicative of a population's long-term potential for adaptability and persistence. It is used less commonly than heterozygosity as a genetic diversity measure, partially because it is more mathematically difficult to take into account the stochastic process of genetic drift for allelic richness. This paper presents a stochastic model for the allelic richness of a newly founded population experiencing genetic drift and gene flow. The model follows the dynamics of alleles lost during the founder event and simulates the effect of gene flow on maintenance and recovery of allelic richness. The probability of an allele's presence in the population was identified as the relevant statistical property for a meaningful interpretation of allelic richness. A method is discussed that combines the probability of allele presence with a population's allele frequency spectrum to provide predictions for allele recovery. The model's analysis provides insights into the dynamics of allelic richness following a founder event, taking into account gene flow and the allele frequency spectrum. Furthermore, the model indicates that the “One Migrant per Generation” rule, a commonly used conservation guideline related to heterozygosity, may be inadequate for addressing preservation of diversity at the allelic level. This highlights the importance of distinguishing between heterozygosity and allelic richness as measures of genetic diversity, since focusing merely on the preservation of heterozygosity might not be enough to adequately preserve allelic richness, which is crucial for species persistence and evolution.

A maximum-likelihood estimation of pairwise relatedness for autopolyploids

Relatedness between individuals is central to ecological genetics. Multiple methods are available to quantify relatedness from molecular data, including method-of-moment and maximum-likelihood estimators. We describe a maximum-likelihood estimator for autopolyploids, and quantify its statistical performance under a range of biologically relevant conditions. The statistical performances of five additional polyploid estimators of relatedness were also quantified under identical conditions. When comparing truncated estimators, the maximum-likelihood estimator exhibited lower root mean square error under some conditions and was more biased for non-relatives, especially when the number of alleles per loci was low. However, even under these conditions, this bias was reduced to be statistically insignificant with more robust genetic sampling. We also considered ambiguity in polyploid heterozygote genotyping and developed a weighting methodology for candidate genotypes. The statistical performances of three polyploid estimators under both ideal and actual conditions (including inbreeding and double reduction) were compared. The software package POLYRELATEDNESS is available to perform this estimation and supports a maximum ploidy of eight.

Joint inference of identity by descent along multiple chromosomes from population samples

There has been much interest in detecting genomic identity by descent (IBD) segments from modern dense genetic marker data and in using them to identify human disease susceptibility loci. Here we present a novel Bayesian framework using Markov chain Monte Carlo (MCMC) realizations to jointly infer IBD states among multiple individuals not known to be related, together with the allelic typing error rate and the IBD process parameters. The data are phased single nucleotide polymorphism (SNP) haplotypes. We model changes in latent IBD state along homologous chromosomes by a continuous time Markov model having the Ewens sampling formula as its stationary distribution. We show by simulation that this model for the IBD process fits quite well with the coalescent predictions. Using simulation data sets of 40 haplotypes over regions of 1 and 10 million base pairs (Mbp), we show that the jointly estimated IBD states are very close to the true values, although the presence of linkage disequilibrium decreases the accuracy. We also present comparisons with the ibd_haplo program, which estimates IBD among sets of four haplotypes. Our new IBD detection method focuses on the scale between genome-wide methods using simple IBD models and complex coalescent-based methods that are limited to short genome segments. At the scale of a few Mbp, our approach offers potentially more power for fine-scale IBD association mapping.

Kernel-based whole-genome prediction of complex traits: a review

Prediction of genetic values has been a focus of applied quantitative genetics since the beginning of the 20th century, with renewed interest following the advent of the era of whole genome-enabled prediction. Opportunities offered by the emergence of high-dimensional genomic data fueled by post-Sanger sequencing technologies, especially molecular markers, have driven researchers to extend Ronald Fisher and Sewall Wright's models to confront new challenges. In particular, kernel methods are gaining consideration as a regression method of choice for genome-enabled prediction. Complex traits are presumably influenced by many genomic regions working in concert with others (clearly so when considering pathways), thus generating interactions. Motivated by this view, a growing number of statistical approaches based on kernels attempt to capture non-additive effects, either parametrically or non-parametrically. This review centers on whole-genome regression using kernel methods applied to a wide range of quantitative traits of agricultural importance in animals and plants. We discuss various kernel-based approaches tailored to capturing total genetic variation, with the aim of arriving at an enhanced predictive performance in the light of available genome annotation information. Connections between prediction machines born in animal breeding, statistics, and machine learning are revisited, and their empirical prediction performance is discussed. Overall, while some encouraging results have been obtained with non-parametric kernels, recovering non-additive genetic variation in a validation dataset remains a challenge in quantitative genetics.

Patterns and dynamics of genetic diversity in Plasmodium falciparum: what past human migrations tell us about malaria

Plasmodium falciparum is the main agent of malaria, one of the major human infectious diseases affecting millions of people worldwide. The genetic diversity of P. falciparum populations is an essential factor in the parasite's ability to adapt to changes in its environment, enabling the development of drug resistance and the evasion from the host immune system through antigenic variation. Therefore, characterizing these patterns and understanding the main drivers of the pathogen's genetic diversity can provide useful inputs for informing control strategies. In this paper, we review the pioneering work led by Professor Kazuyuki Tanabe on the genetic diversity of P. falciparum populations. In a first part, we recall basic results from population genetics for quantifying within-population genetic diversity, and discuss the main mechanisms driving this diversity. Then, we show how these approaches have been used for reconstructing the historical spread of malaria worldwide, and how current patterns of genetic diversity suggest that the pathogen followed our ancestors in their journey out of Africa. Because these results are robust to different types of genetic markers, they provide a baseline for predicting the pathogen's diversity in unsampled populations, and some useful elements for predicting vaccine efficacy and informing malaria control strategies.

XXI.—On the Dominance Ratio

The frequency ratio of the allelomorphs of a Mendelian factor is only stable if selection favours the heterozygote: such factors, though occurring rarely, will accumulate in the stock, while those of opposite tendency will be eliminated.

The survival of a mutant gene although established in a mature and potent individual is to a very large extent a matter of chance ; only when a large number of individuals have become affected does selection, dependent on its contribution to the fitness of the organism, become of importance. This is so even for dominant mutants; for recessive mutants selection remains very small so long as the mutant form is an inconsiderable fraction of the interbreeding group.

The distribution of the frequency ratio for different factors may be calculated from the condition that this distribution is stable, as is that of velocities in the Theory of Gases : in the absence of selection the distribution of log is given in fig. 1. Fig. 2 represents the case of steady decay in variance by the action of random survival (the Hagedoorn effect).

Fig. 3 shows the distribution in the somewhat artificial case of uniform genetic selection : this would be the distribution to be expected in the absence of dominance. Fig. 4 shows the asymmetrical distribution due to uniform genotypic selection with or without homogamy.

Under genotypic selection the dominance ratio for complete dominance comes to be exactly ⅓, in close agreement with the value obtained from human measurements.

The rate of mutation necessary to maintain the variance of the species may be calculated from these distributions. Very infrequent mutation will serve to counterbalance the effect of random survival; for equilibrium with selective action a much higher level is needed, though still mutation may be individually rare, especially in large populations.

It would seem that the supposition of genotypic selection balanced by occasional mutations fitted the facts deduced from the correlations of relatives in mankind.

What is genetic differentiation, and how should we measure it--GST, D, neither or both?

Estimates of the fixation index, F(ST), have been used as measures of population differentiation for many decades. However, there have been persistent voices in the literature suggesting that these statistics do not measure true differentiation. In particular, the statistics Nei's G(ST) and Wier and Cockerham's θ have been criticized for being 'constrained' to not equal one in some situations that seem to represent maximal differentiation. Here, we address the issue of how to evaluate exactly how much information a particular statistic contains about the process of differentiation. This criterion can be used to counter most concerns about the performance of G(ST) (and related statistics), while also being reconciled with the insights of those who have proposed alternative measures of differentiation. In particular, the likelihood-based framework that we put forward can justify the use of G(ST) as an effective measure of differentiation, but also shows that in some situations G(ST) is insufficient on its own and needs supplementing by another measure such as Jost's D or Hedrick's G'(ST). This approach will become increasingly important in the future, as greater emphasis is placed on analysing large data sets.

Evil green beards: Tag recognition can also be used to withhold cooperation in structured populations

Natural selection works against cooperation unless a specific mechanism is at work. These mechanisms are typically studied in isolation. Here we look at the interaction between two such mechanisms: tag recognition and population structure. If cooperators can recognize each other, and only cooperate among themselves, then they can invade defectors. This is known as the green beard effect. Another mechanism is assortment caused by population structure. If interactions occur predominantly between alike individuals, then indiscriminate cooperation can evolve. Here we show that these two mechanisms interact in a non-trivial way. When assortment is low, tags lead to conventional green beard cycles with periods of tag based cooperation and periods of defection. However, if assortment is high, evil green beard cycles emerge. In those cycles, tags are not used to build up cooperation with others that share the tag, but to undermine cooperation with others that do not share the tag. High levels of assortment therefore do not lead to indiscriminate cooperation if tags are available. This shows that mechanisms that are known to promote cooperation in isolation can interact in counterintuitive ways.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Conditions for the validity of SNP-based heritability estimation

The heritability of a trait (h(2)) is the proportion of its population variance caused by genetic differences, and estimates of this parameter are important for interpreting the results of genome-wide association studies (GWAS). In recent years, researchers have adopted a novel method for estimating a lower bound on heritability directly from GWAS data that uses realized genetic similarities between nominally unrelated individuals. The quantity estimated by this method is purported to be the contribution to heritability that could in principle be recovered from association studies employing the given panel of SNPs (h(2)(SNP)). Thus far, the validity of this approach has mostly been tested empirically. Here, we provide a mathematical explication and show that the method should remain a robust means of obtaining h(2)(SNP)) under circumstances wider than those under which it has so far been derived.

Sex-biased mortality associated with inbreeding in Drosophila melanogaster

BACKGROUND

One proposed consequence of inbreeding is a skewed sex ratio arising from sex specific mortality in the homogametic sex caused by inbreeding on the sex chromosome. However, recent work suggests that random distortions in sex ratio due to autosomal inbreeding may be of greater importance. In this study, we investigate the effect of biologically realistic levels of inbreeding on sex ratio and sex specific mortality in Drosophila melanogaster. We use two pedigree crossing designs to either maximise or minimise inbreeding on the X-chromosome whilst producing identical autosomal inbreeding.

RESULTS

We found increased female mortality and male biased sex ratios associated with inbreeding in our high, but not low, X-inbreeding pedigree. While our results are more consistent with being driven by inbreeding on the X-chromosome than on the autosomes, the marked difference between treatments does not fit closely the expectations of either model.

CONCLUSIONS

Our results are only partly consistent with the hypothesis that inbreeding on the X-chromosome can cause greater fitness reductions in the homogametic sex. Whilst the results of our study are not conclusive, they suggest that directional distortions in sex ratio due to inbreeding can occur, and highlight the need for further investigation on this topic.

Carrier frequency of the c.525delT mutation in the SGCG gene and estimated prevalence of limb girdle muscular dystrophy type 2C among the Moroccan population

Autosomal recessive limb-girdle muscular dystrophies (AR-LGMDs) are characterized by clinical and genetic heterogeneity. LGMD type 2C, or γ-sarcoglycanopathy, is the most frequent in North African populations as a result of the founder c.525delT mutation in the SGCG gene. Its epidemiology is poorly known in Morocco, and its prevalence among the Moroccan population has never been evaluated. This study screened 26 patients with a LGMD2C and 45 patients with an AR-LGMD phenotype for the c.525delT mutation. DNA extracted from umbilical cord blood samples of 250 newborns was tested for the same mutation. Molecular epidemiologic methods were used to calculate the frequency of heterozygotes for this mutation in Moroccan newborns and to estimate the prevalence of LGMD2C in the Moroccan population. The carrier frequency was estimated to be 1/250, which would imply that the prevalence of LGMD2C would be approximately 1/20,492 considering the effect of consanguinity. The homozygous c.525delT mutation was found in 65% of all patients with AR-LGMDs. These findings suggest that AR-LGMDs are prevalent in the Moroccan population and LGMD2C is one of the most common forms. This information might be useful for the development of diagnostic strategies on a large scale for better management of patients with AR-LGMD and genetic counseling of families.

Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction

Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

Praziquantel treatment decreases Schistosoma mansoni genetic diversity in experimental infections

BACKGROUND

Schistosomiasis has a considerable impact on public health in many tropical and subtropical areas. In the new world, schistosomiasis is caused by the digenetic trematode Schistosoma mansoni. Chemotherapy is the main measure for controlling schistosomiasis, and the current drug of choice for treatment is praziquantel (PZQ). Although PZQ is efficient and safe, its repetitive large-scale use in endemic areas may lead to the selection of resistant strains. Isolates less susceptible to PZQ have been found in the field and selected for in the laboratory. The impact of selecting strains with a decreased susceptibility phenotype on disease dynamics and parasite population genetics is not fully understood. This study addresses the impact of PZQ pressure on the genetics of a laboratory population by analyzing frequency variations of polymorphic genetic markers.

METHODOLOGY

Infected mice were treated with increasing PZQ doses until the highest dose of 3 × 300 mg/Kg was reached. The effect of PZQ treatment on the parasite population was assessed using five polymorphic microsatellite markers. Parasitological and genetic data were compared with those of the untreated control. After six parasite generations submitted to treatment, it was possible to obtain a S. mansoni population with decreased susceptibility to PZQ. In our experiments we also observed that female worms were more susceptible to PZQ than male worms.

CONCLUSIONS

The selective pressure exerted by PZQ led to decreased genetic variability in S. mansoni and increased endogamy. The understanding of how S. mansoni populations respond to successive drug pressure has important implications on the appearance and maintenance of a PZQ resistance phenotype in endemic regions.

Evaluating status change of soil potassium from path model

The purpose of this study is to determine critical environmental parameters of soil K availability and to quantify those contributors by using a proposed path model. In this study, plot experiments were designed into different treatments, and soil samples were collected and further analyzed in laboratory to investigate soil properties influence on soil potassium forms (water soluble K, exchangeable K, non-exchangeable K). Furthermore, path analysis based on proposed path model was carried out to evaluate the relationship between potassium forms and soil properties. Research findings were achieved as followings. Firstly, key direct factors were soil S, ratio of sodium-potassium (Na/K), the chemical index of alteration (CIA), Soil Organic Matter in soil solution (SOM), Na and total nitrogen in soil solution (TN), and key indirect factors were Carbonate (CO3), Mg, pH, Na, S, and SOM. Secondly, path model can effectively determine direction and quantities of potassium status changes between Exchangeable potassium (eK), Non-exchangeable potassium (neK) and water-soluble potassium (wsK) under influences of specific environmental parameters. In reversible equilibrium state of [Formula: see text], K balance state was inclined to be moved into β and χ directions in treatments of potassium shortage. However in reversible equilibrium of [Formula: see text], K balance state was inclined to be moved into θ and λ directions in treatments of water shortage. Results showed that the proposed path model was able to quantitatively disclose moving direction of K status and quantify its equilibrium threshold. It provided a theoretical and practical basis for scientific and effective fertilization in agricultural plants growth.

Reductions in genetic diversity of Schistosoma mansoni populations under chemotherapeutic pressure: the effect of sampling approach and parasite population definition

Detecting potential changes in genetic diversity in schistosome populations following chemotherapy with praziquantel (PZQ) is crucial if we are to fully understand the impact of such chemotherapy with respect to the potential emergence of resistance and/or other evolutionary outcomes of interventions. Doing so by implementing effective, and cost-efficient sampling protocols will help to optimise time and financial resources, particularly relevant to a disease such as schistosomiasis currently reliant on a single available drug. Here we explore the effect on measures of parasite genetic diversity of applying various field sampling approaches, both in terms of the number of (human) hosts sampled and the number of transmission stages (miracidia) sampled per host for a Schistosoma mansoni population in Tanzania pre- and post-treatment with PZQ. In addition, we explore population structuring within and between hosts by comparing the estimates of genetic diversity obtained assuming a 'component population' approach with those using an 'infrapopulation' approach. We found that increasing the number of hosts sampled, rather than the number of miracidia per host, gives more robust estimates of genetic diversity. We also found statistically significant population structuring (using Wright's F-statistics) and significant differences in the measures of genetic diversity depending on the parasite population definition. The relative advantages, disadvantages and, hence, subsequent reliability of these metrics for parasites with complex life-cycles are discussed, both for the specific epidemiological and ecological scenario under study here and for their future application to other areas and schistosome species.

Hamiltonian inclusive fitness: a fitter fitness concept

In 1963-1964 W. D. Hamilton introduced the concept of inclusive fitness, the only significant elaboration of Darwinian fitness since the nineteenth century. I discuss the origin of the modern fitness concept, providing context for Hamilton's discovery of inclusive fitness in relation to the puzzle of altruism. While fitness conceptually originates with Darwin, the term itself stems from Spencer and crystallized quantitatively in the early twentieth century. Hamiltonian inclusive fitness, with Price's reformulation, provided the solution to Darwin's 'special difficulty'-the evolution of caste polymorphism and sterility in social insects. Hamilton further explored the roles of inclusive fitness and reciprocation to tackle Darwin's other difficulty, the evolution of human altruism. The heuristically powerful inclusive fitness concept ramified over the past 50 years: the number and diversity of 'offspring ideas' that it has engendered render it a fitter fitness concept, one that Darwin would have appreciated.

Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network

Linkage disequilibrium (LD) is defined as a stochastic dependence between alleles at two or more loci. Although understanding LD is important in the study of the genetics of many species, little attention has been paid on how a covariance structure between many loci distributed across the genome should be represented. Given that biological systems at the cellular level often involve gene networks, it is appealing to evaluate LD from a network perspective, i.e., as a set of associated loci involved in a complex system. We applied a Markov network (MN) to study LD using data on 1,279 markers derived from 599 wheat inbred lines. The MN attempts to account for association between two markers, conditionally on the remaining markers in the network model. In this study, the recovery of the structure of a LD network was done through two variants of pseudo-likelihoods subject to an L1 penalty on the MN parameters. It is shown that, while the L1-regularized Markov network preserves features of a Bayesian network (BN), the nodes in the resulting networks have fewer links. The resulting sparse network, encoding conditional independencies, provides a clearer picture of association than marginal LD metrics, and a sparse graph eases interpretation markedly, since it includes a smaller number of edges than a BN. Thus, an L1-regularized sparse Markov network seems appealing for representing conditional LD with high-dimensional genomic data, where variables, e.g., single nucleotide polymorphism markers, are expected to be sparsely connected.

Inbreeding avoidance drives consistent variation of fine-scale genetic structure caused by dispersal in the seasonal mating system of Brandt's voles

Inbreeding depression is a major evolutionary and ecological force influencing population dynamics and the evolution of inbreeding-avoidance traits such as mating systems and dispersal. Mating systems and dispersal are fundamental determinants of population genetic structure. Resolving the relationships among genetic structure, seasonal breeding-related mating systems and dispersal will facilitate our understanding of the evolution of inbreeding avoidance. The goals of this study were as follows: (i) to determine whether females actively avoided mating with relatives in a group-living rodent species, Brandt’s voles (Lasiopodomys brandtii), by combined analysis of their mating system, dispersal and genetic structure; and (ii) to analyze the relationships among the variation in fine-genetic structure, inbreeding avoidance, season-dependent mating strategies and individual dispersal. Using both individual- and population-level analyses, we found that the majority of Brandt’s vole groups consisted of close relatives. However, both group-specific FISs, an inbreeding coefficient that expresses the expected percentage rate of homozygosity arising from a given breeding system, and relatedness of mates showed no sign of inbreeding. Using group pedigrees and paternity analysis, we show that the mating system of Brandt’s voles consists of a type of polygyny for males and extra-group polyandry for females, which may decrease inbreeding by increasing the frequency of mating among distantly-related individuals. The consistent variation in within-group relatedness, among-group relatedness and fine-scale genetic structures was mostly due to dispersal, which primarily occurred during the breeding season. Biologically relevant variation in the fine-scale genetic structure suggests that dispersal during the mating season may be a strategy to avoid inbreeding and drive the polygynous and extra-group polyandrous mating system of this species.

Dispersal and gene flow in free-living marine nematodes

Dispersal and gene flow determine connectivity among populations, and can be studied through population genetics and phylogeography. We here review the results of such a framework for free-living marine nematodes. Although field experiments have illustrated substantial dispersal in nematodes at ecological time scales, analysis of the genetic diversity illustrated the importance of priority effects, founder effects and genetic bottlenecks for population structuring between patches <1 km apart. In contrast, only little genetic structuring was observed within an estuary (<50 km), indicating that these small scale fluctuations in genetic differentiation are stabilized over deeper time scales through extensive gene flow. Interestingly, nematode species with contrasting life histories (extreme colonizers vs persisters) or with different habitat preferences (algae vs sediment) show similar, low genetic structuring. Finally, historical events have shaped the genetic pattern of marine nematodes and show that gene flow is restricted at large geographical scales. We also discuss the presence of substantial cryptic diversity in marine nematodes, and end with highlighting future important steps to further unravel nematode evolution and diversity.

On the measurements of genetic differentiation among populations

FST, a measurement of the genetic differentiation among subpopulations, is a fundamental parameter in population genetics, with many valuable applications in molecular biology, evolutionary biology, conservation and forensics. One of its close relatives, GST, has been widely used to measure differentiation from highly polymorphic markers such as microsatellites. However, because of the high mutation rate of such markers, GST may underestimate the genomic differentiation due to demographic causes such as migration rate and subpopulation size. A new statistic proposed recently, Jost's D, was claimed to have better properties than GST and was advocated to replace GST as a measure of differentiation. This paper shows that D is not a proper measure of differentiation because it fails to meet some fundamental requirements as a differentiation statistic, and is hardly estimable without bias in practice. D is highly dependent on the gene diversity of a marker and on the unknown parameter of the number of subpopulations, is highly sensitive to how alleles and loci are defined and how data are analysed, does not increase monotonically with either divergence time or drift, and does not always have a maximal value of 1. The maximal D value can be zero or close to zero, depending on the number of alleles at a locus relative to the number of subpopulations. I suggest continuing the use of GST, with caution in its interpretation when highly polymorphic markers are used, before a better estimator of FST that explicitly accounts for mutations is developed.

Quasi equilibrium approximations of the fixation index under neutrality: the finite and infinite island models

The fixation index F(ST) and the coefficient of gene differentiation G(ST) are analyzed for the finite island model under short time spans, ignoring mutations. Dividing the reproduction cycle into the three steps-gamete formation, fertilization, and migration-we develop a new approach for computing quasi equilibrium formulas for F(ST) (and G(ST)). Our formulas generalize earlier ones and reveal that the equilibrium value of F(ST) is influenced not only by the migration rate and local effective population size, N(e), but also by the local census size N, particularly so when the migration rate is high. The order of migration and fertilization is found to have a smaller effect on F(ST). A major advantage compared to previous approaches is that stochastic allele frequency of migrants is easily accommodated, thereby avoiding underestimation of F(ST) for large migration rates.

Decisions for others become less impulsive the further away they are on the family tree

Background

People tend to prefer a smaller immediate reward to a larger but delayed reward. Although this discounting of future rewards is often associated with impulsivity, it is not necessarily irrational. Instead it has been suggested that it reflects the decision maker’s greater interest in the ‘me now’ than the ‘me in 10 years’, such that the concern for our future self is about the same as for someone else who is close to us.

Methodology/Principal Findings

To investigate this we used a delay-discounting task to compare discount functions for choices that people would make for themselves against decisions that they think that other people should make, e.g. to accept $500 now or $1000 next week. The psychological distance of the hypothetical beneficiaries was manipulated in terms of the genetic coefficient of relatedness ranging from zero (e.g. a stranger, or unrelated close friend), .125 (e.g. a cousin), .25 (e.g. a nephew or niece), to .5 (parent or sibling).

Conclusions/Significance

The observed discount functions were steeper (i.e. more impulsive) for choices in which the decision-maker was the beneficiary than for all other beneficiaries. Impulsiveness of decisions declined systematically with the distance of the beneficiary from the decision-maker. The data are discussed with reference to the implusivity and interpersonal empathy gaps in decision-making.

An effective rotational mating scheme for inbreeding reduction in captive populations illustrated by the rare sheep breed Kempisch Heideschaap

Within breeds and other captive populations, the risk of high inbreeding rates and loss of diversity can be high within (small) herds or subpopulations. When exchange of animals between different subpopulations is organised according to a rotational mating scheme, inbreeding rates can be restricted. Two such schemes, a breeding circle and a maximum avoidance of inbreeding scheme, are compared. In a breeding circle, flocks are organised in a circle where each flock serves as a donor flock for another flock, and the same donor-recipient combination is used in each breeding season. In the maximum inbreeding avoidance scheme, donor-recipient combinations change each year so that the use of the same combination is postponed as long as possible. Data from the Kempisch Heideschaap were used with computer simulations to determine the long-term effects of different breeding schemes. Without exchanging rams between flocks, high inbreeding rates (>1.5% per year) occurred. Both rotational mating schemes reduced inbreeding rates to on average 0.16% per year and variation across flocks in inbreeding rates, caused by differences in flock size, almost disappeared. Inbreeding rates with maximum inbreeding avoidance were more variable than with a breeding circle. Moreover, a breeding circle is easier to implement and operate. Breeding circles are thus efficient and flexible and can also be efficient for other captive populations, such as zoo populations of endangered wild species.

On S.N. Bernstein's derivation of Mendel's Law and 'rediscovery' of the Hardy-Weinberg distribution

Around 1923 the soon-to-be famous Soviet mathematician and probabilist Sergei N. Bernstein started to construct an axiomatic foundation of a theory of heredity. He began from the premise of stationarity (constancy of type proportions) from the first generation of offspring. This led him to derive the Mendelian coefficients of heredity. It appears that he had no direct influence on the subsequent development of population genetics. A basic assumption of Bernstein was that parents coupled randomly to produce offspring. This paper shows that a simple model of non-random mating, which nevertheless embodies a feature of the Hardy-Weinberg Law, can produce Mendelian coefficients of heredity while maintaining the population distribution. How W. Johannsen’s monograph influenced Bernstein is discussed.

Heritability in the genome-wide association era

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

XIX.—Sexual Maturity in Brown Leghorns, and the Relationship of Genetic Variance to Differences in Environment

The concept of heritability is discussed in relation to the climatic changes which may affect the variations of age at sexual maturity in the fowl, and it is suggested that genetic and environmental variance, as expressed in the customary statistical formulation, may be largely synonymous terms.

Four groups of Brown Leghorn pullets are compared, one subject to no artificial lighting and the sub-optimal conditions of war years, a second to restricted lighting, and a third and fourth to 14-hour day length. The formal estimates of genetic and environmental variance were respectively 910 and 978, 594 and 546, 188 and 295, and 56 and 135. Further reduction of variability is apparent on subdivision into early and late hatches, heritability being apparently zero in the first case but maintained at an average level in the second.

Changes in variability substantiate the presence of genotype-environment interactions as previously reported, and similar conclusions follow from heterogeneity of regressions of age at maturity on date of hatch.

Mouse strain specific gene expression differences for illumina microarray expression profiling in embryos

Background

In the field of mouse genetics the advent of technologies like microarray based expression profiling dramatically increased data availability and sensitivity, yet these advanced methods are often vulnerable to the unavoidable heterogeneity of in vivo material and might therefore reflect differentially expressed genes between mouse strains of no relevance to a targeted experiment. The aim of this study was not to elaborate on the usefulness of microarray analysis in general, but to expand our knowledge regarding this potential “background noise” for the widely used Illumina microarray platform surpassing existing data which focused primarily on the adult sensory and nervous system, by analyzing patterns of gene expression at different embryonic stages using wild type strains and modern transgenic models of often non-isogenic backgrounds.

Results

Wild type embryos of 11 mouse strains commonly used in transgenic and molecular genetic studies at three developmental time points were subjected to Illumina microarray expression profiling in a strain-by-strain comparison. Our data robustly reflects known gene expression patterns during mid-gestation development. Decreasing diversity of the input tissue and/or increasing strain diversity raised the sensitivity of the array towards the genetic background. Consistent strain sensitivity of some probes was attributed to genetic polymorphisms or probe design related artifacts.

Conclusion

Our study provides an extensive reference list of gene expression profiling background noise of value to anyone in the field of developmental biology and transgenic research performing microarray expression profiling with the widely used Illumina microarray platform. Probes identified as strain specific background noise further allow for microarray expression profiling on its own to be a valuable tool for establishing genealogies of mouse inbred strains.

Management of genetic diversity in small farm animal populations

Many local breeds of farm animals have small populations and, consequently, are highly endangered. The correct genetic management of such populations is crucial for their survival. Managing an animal population involves two steps: first, the individuals who will be permitted to leave descendants are to be chosen and the number offspring they will be permitted to produce has to be determined; second, the mating scheme has to be identified. Strategies dealing with the first step are directed towards the maximisation of effective population size and, therefore, act jointly on the reduction in the loss of genetic variation and in the increase of inbreeding. In this paper, the most relevant methods are summarised, including the so-called 'Optimum Contribution' methodology (contributions are proportional to the coancestry of each individual with the rest), which has been shown to be the best. Typically, this method is applied to pedigree information, but molecular marker data can be used to complete or replace the genealogy. When the population is subjected to explicit selection on any trait, the above methodology can be used by balancing the response to selection and the increase in coancestry/inbreeding. Different mating strategies also exist. Some of the mating schemes try to reduce the level of inbreeding in the short term by preventing mating between relatives. Others involve regular (circular) schemes that imply higher levels of inbreeding within populations in the short term, but demonstrate better performance in the long term. In addition, other tools such as cryopreservation and reproductive techniques aid in the management of small populations. In the future, genomic marker panels may replace the pedigree information in measuring the coancestry. The paper also includes the results of several experiments and field studies on the effectiveness and on the consequences of the use of the different strategies.