Genome-Wide Prediction of Traits with Different Genetic Architecture Through Efficient Variable Selection

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.     

Might there be a medical conscience?

I defend the feasibility of a medical conscience in the following sense: a medical professional can object to the prevailing medical norms because they are incorrect as medical norms. In other words, I provide an account of conscientious objection that makes use of the idea that the conscience can issue true normative claims, but the claims in question are claims about medical norms rather than about general moral norms. I further argue that in order for this line of reasoning to succeed, there needs to be an internal morality of medicine that determines what medical professionals ought to do qua medical professionals. I utilize a constructivist approach to the internal morality of medicine and argue that medical professionals can conscientiously object to providing treatment X, if providing treatment X is not in accordance with norms that would have been constructed, in light of the end of medicine, by the appropriate agents under the appropriate conditions.     

The two faces of revenge: moral responsibility and the culture of honor

Retributive emotions and behavior are thought to be adaptive for their role in improving social coordination. However, since retaliation is generally not in the short-term interests of the individual, rational self-interest erodes the motivational link between retributive emotions and the accompanying adaptive behavior. I argue that two different sets of norms have emerged to reinforce this link: (1) norms about honor and (2) norms about moral responsibility and desert. I observe that the primary difference between these types of retribution motivators lies in where the normative focus is placed after an offense. In the first form of retribution, the normative focus is on the offended party. In the second, it is on the offender. Next, I show how each class of norms is well tailored to the particular features of the environment in which these forms of retributive behavior emerge. Finally, I consider some philosophical implications of these observations. I suggest that my account, if correct, would pose tough challenges for contemporary philosophical theories of moral responsibility and punishment.

Rhesus macaques use probabilities to predict future events

Humans can use an intuitive sense of statistics to make predictions about uncertain future events, a cognitive skill that underpins logical and mathematical reasoning. Recent research shows that some of these abilities for statistical inferences can emerge in preverbal infants and non-human primates such as apes and capuchins. An important question is therefore whether animals share the full complement of intuitive reasoning abilities demonstrated by humans, as well as what evolutionary contexts promote the emergence of such skills. Here, we examined whether free-ranging rhesus macaques (Macaca mulatta) can use probability information to infer the most likely outcome of a random lottery, in the first test of whether primates can make such inferences in the absence of direct prior experience. We developed a novel expectancy-violation looking time task, adapted from prior studies of infants, in order to assess the monkeys' expectations. In Study 1, we confirmed that monkeys (n = 20) looked similarly at different sampled items if they had no prior knowledge about the population they were drawn from. In Study 2, monkeys (n = 80) saw a dynamic ‘lottery’ machine containing a mix of two types of fruit outcomes, and then saw either the more common fruit (expected trial) or the relatively rare fruit (unexpected trial) fall from the machine. We found that monkeys looked longer when they witnessed the unexpected outcome. In Study 3, we confirmed that this effect depended on the causal relationship between the sample and the population, not visual mismatch: monkeys (n = 80) looked equally at both outcomes if the experimenter pulled the sampled item from her pocket. These results reveal that rhesus monkeys spontaneously use information about probability to reason about likely outcomes, and show how comparative studies of nonhumans can disentangle the evolutionary history of logical reasoning capacities.     

Sensorimotor Recalibration Depends on Attribution of Sensory Prediction Errors to Internal Causes

Sensorimotor learning critically depends on error signals. Learning usually tries to minimise these error signals to guarantee optimal performance. Errors can, however, have both internal causes, resulting from one’s sensorimotor system, and external causes, resulting from external disturbances. Does learning take into account the perceived cause of error information? Here, we investigated the recalibration of internal predictions about the sensory consequences of one’s actions. Since these predictions underlie the distinction of self- and externally produced sensory events, we assumed them to be recalibrated only by prediction errors attributed to internal causes. When subjects were confronted with experimentally induced visual prediction errors about their pointing movements in virtual reality, they recalibrated the predicted visual consequences of their movements. Recalibration was not proportional to the externally generated prediction error, but correlated with the error component which subjects attributed to internal causes. We also revealed adaptation in subjects’ motor performance which reflected their recalibrated sensory predictions. Thus, causal attribution of error information is essential for sensorimotor learning.     

Stigmatizing attitudes toward obesity in a representative population-based sample

Objective: The aim of this study was to determine stigmatizing attitudes toward obesity in the population, and its related psychological and sociodemographic determinants. Methods and Procedures: In a representative population‐based survey (N = 1,000), computer‐assisted telephone interviewing was used to assess stigmatizing attitudes toward obesity, causal attributions of obesity, the labeling of obesity as an illness, perceptions about prevalence, severity, and chronicity of obesity, support of obesity prevention, and sociodemographic characteristics. Results: Of the 1,000 participants, 23.5% (n = 235) had stigmatizing attitudes toward obesity, 21.5% (n = 215) did not have stigmatizing attitudes toward obesity, and 55.0% (n = 550) had attitudes that were undetermined with respect to stigmatization. Predictors of greater stigmatization were more causal attributions of obesity to individual behavior, less education, and older age, while causal attributions of obesity to heredity and labeling obesity as an illness predicted less stigmatization. Stigmatizing attitudes were significantly associated with stronger overall support of obesity prevention, but less readiness to support prevention financially. Discussion: Our results indicate that stigmatizing attitudes toward obesity are prevalent in the population. Information about the etiology of obesity and the clinical relevance of this condition could prove useful for destigmatization efforts.     

Unification and coherence as methodological objectives in the biological sciences

In this paper I respond to Wim van der Steen's arguments against the supposed current overemphasis on norms ofcoherence andinterdisciplinary integration in biology. On the normative level, I argue that these aremiddle-range norms which, although they may be misapplied in short-term attempts to solve (temporarily?) intractable problems, play a guiding role in the longer-term treatment of biological problems. This stance is supported by a case study of apartial success story, the development of the one gene — one enzyme hypothesis. As that case shows, thegoal of coherent interdisciplinary integration not only provides guidance for research, but also provides the standard for recognizingfailed integrations of the sort that van der Steen criticizes.     

<Emphasis Type="Italic">Llaki</Emphasis> and <Emphasis Type="Italic">Ñakary</Emphasis>: Idioms of Distress and Suffering Among the Highland Quechua in the Peruvian Andes

This article examines some of the long-term health outcomes of extreme adversities and the ways in which social inequalities and idioms of distress are historically and socially produced in the Peruvian context. We describe how the highland Quechua of northern Ayacucho construct and experience expressions of distress and suffering such as pinsamientuwan (worrying thoughts, worries), ñakary (suffering) and llaki (sorrow, sadness), in a context of persistent social inequalities, social exclusion and a recent history of political violence. It is concluded that the multiple expressions of distress and suffering are closely related to past and current events, shaped by beliefs, core values and cultural norms and, in this process, transformed, recreated and invested with new meanings and attributions.     

Predictions of vegetation change following fire in a great salt lake marsh

A Gleasonian model of succession (van der Valk, 1981) was used to predict the species composition of a freshwater marsh following fire. Seed bank samples from 5 vegetation types (Distichlis spicata (L.) Greene, Scirpus lacustris L., S. maritimus L., Typha spp., open water) were used in conjuction with plant life history characteristics to make predictions of post-fire species composition for each vegetation type and the entire study area. In general, predictions for specific vegetation types were not good, but were more accurate for the entire area. Stronge correlations for specific vegetation types may have been prevented by considering marsh vegetation types as discrete units, statistical sampling problems, competitive interactions, or more specific germination requirements of some species.     

Spatial invasion by a mutant pathogen

Imagine a pathogen that is spreading radially as a circular wave through a population of susceptible hosts. In the interior of this circular region, the infection dies out due to a subcritical density of susceptibles. If a mutant pathogen, having some advantage over wild-type pathogens, arises in this region it is likely to die out without leaving a noticeable trace. Mutants that arise closer to the infection wavefront have access to more susceptible hosts and thus are more likely to become established and perhaps (locally) out-compete the original pathogen. Among the factors (position, transmission rate, pathogen-induced death rate) that influence the fate of a mutant, which are most important? What does this tell us about the types of mutants that are likely to invade and become established? How do such tendencies serve to steer the evolution of pathogens in a spatial setting? Do different types of models of the same phenomena lead to similar conclusions? We address these issues from the point of view of an individual-based stochastic spatial model of host-pathogen interactions. We consider the probability of a successful invasion by a single mutant as a function of the transmissibility and virulence strengths and the mutant position in the wavefront. Next, for a version of the model in which mutations arise spontaneously, we obtain analytical and simulation results on the mean time to a successful invasion. We also use our model predictions to gain insight into experimental data on bacteriophage plaques. Finally, we compare our results to those based on ordinary and partial differential equations to better understand how different models might influence our predictions on the fate of a mutant pathogen.     

Association of polymorphisms in odorant-binding protein genes with variation in olfactory response to benzaldehyde in Drosophila

Adaptive evolution of animals depends on behaviors that are essential for their survival and reproduction. The olfactory system of Drosophila melanogaster has emerged as one of the best characterized olfactory systems, which in addition to a family of odorant receptors, contains an approximately equal number of odorant-binding proteins (OBPs), encoded by a multigene family of 51 genes. Despite their abundant expression, little is known about their role in chemosensation, largely due to the lack of available mutations in these genes. We capitalized on naturally occurring mutations (polymorphisms) to gain insights into their functions. We analyzed the sequences of 13 Obp genes in two chromosomal clusters in a population of wild-derived inbred lines, and asked whether polymorphisms in these genes are associated with variation in olfactory responsiveness. Four polymorphisms in 3 Obp genes exceeded the statistical permutation threshold for association with responsiveness to benzaldehyde, suggesting redundancy and/or combinatorial recognition by these OBPs of this odorant. Model predictions of alternative pre-mRNA secondary structures associated with polymorphic sites suggest that alterations in Obp mRNA structure could contribute to phenotypic variation in olfactory behavior.     

Predicting the spatial distribution of the blue-spotted maskray Neotrygon kuhlii (Myliobatiformes, Dasyatidae) on the Australian North and Northwest Shelf comparing two different methods of habitat modeling

Knowledge about distribution and habitat requirements of species is important for analyzing their role in marine ecosystems or establishing sanctuaries. However, knowledge is scarce especially in many chondrichthyan species. In this study, the spatial distribution of the stingray Neotrygon kuhlii on the Australian North and Northwest Shelf was predicted model-based for the first time. Predictions based on two different types of habitat suitability models, logistic regression and maximum entropy modeling. Catch data of N. kuhlii from Australian trawl surveys combined with randomly selected pseudo-absences were used for modeling together with data sets of several environmental variables. Both modeling methods yielded plausible and validated habitat suitability models containing water depth and salinity as significant independent variables. The model-based predictions of the probability of occurrence of N. kuhlii were similar for both methods and thus emphasized the goodness of the models. Following the predictions, N. kuhlii has its highest probability of occurrence in about 60 m water depth and at a salinity of about 35 PSU. The results indicate that both modeling methods are powerful tools to predict spatial distribution and habitat quality for marine fish species. Therefore, they are suitable for detecting possible distribution in areas with only few field records.     

Learning a Prior on Regulatory Potential from eQTL Data

Genome-wide RNA expression data provide a detailed view of an organism's biological state; hence, a dataset measuring expression variation between genetically diverse individuals (eQTL data) may provide important insights into the genetics of complex traits. However, with data from a relatively small number of individuals, it is difficult to distinguish true causal polymorphisms from the large number of possibilities. The problem is particularly challenging in populations with significant linkage disequilibrium, where traits are often linked to large chromosomal regions containing many genes. Here, we present a novel method, Lirnet, that automatically learns a regulatory potential for each sequence polymorphism, estimating how likely it is to have a significant effect on gene expression. This regulatory potential is defined in terms of “regulatory features”—including the function of the gene and the conservation, type, and position of genetic polymorphisms—that are available for any organism. The extent to which the different features influence the regulatory potential is learned automatically, making Lirnet readily applicable to different datasets, organisms, and feature sets. We apply Lirnet both to the human HapMap eQTL dataset and to a yeast eQTL dataset and provide statistical and biological results demonstrating that Lirnet produces significantly better regulatory programs than other recent approaches. We demonstrate in the yeast data that Lirnet can correctly suggest a specific causal sequence variation within a large, linked chromosomal region. In one example, Lirnet uncovered a novel, experimentally validated connection between Puf3—a sequence-specific RNA binding protein—and P-bodies—cytoplasmic structures that regulate translation and RNA stability—as well as the particular causative polymorphism, a SNP in Mkt1, that induces the variation in the pathway.     

Variation in reaction norms: Statistical considerations and biological interpretation

Analysis of reaction norms, the functions by which the phenotype produced by a given genotype depends on the environment, is critical to studying many aspects of phenotypic evolution. Different techniques are available for quantifying different aspects of reaction norm variation. We examine what biological inferences can be drawn from some of the more readily applicable analyses for studying reaction norms. We adopt a strongly biologically motivated view, but draw on statistical theory to highlight strengths and drawbacks of different techniques. In particular, consideration of some formal statistical theory leads to revision of some recently, and forcefully, advocated opinions on reaction norm analysis. We clarify what simple analysis of the slope between mean phenotype in two environments can tell us about reaction norms, explore the conditions under which polynomial regression can provide robust inferences about reaction norm shape, and explore how different existing approaches may be used to draw inferences about variation in reaction norm shape. We show how mixed model‐based approaches can provide more robust inferences than more commonly used multistep statistical approaches, and derive new metrics of the relative importance of variation in reaction norm intercepts, slopes, and curvatures.