AN ANALYSIS OF VARIABILITY IN NUMBER OF DIGITS IN AN INBRED STRAIN OF GUINEA PIGS

Widespread recessive effects on common diseases in a cohort of 44,000 British Pakistanis and Bangladeshis with high autozygosity

Genetic association studies have focused on testing additive models in cohorts with European ancestry. Little is known about recessive effects on common diseases, specifically for non-European ancestry. Genes & Health is a cohort of British Pakistani and Bangladeshi individuals with elevated rates of consanguinity and endogamy, making it suitable to study recessive effects. We imputed variants into a genotyped dataset (n = 44,190) by using two reference panels: a set of 4,982 whole-exome sequences from within the cohort and the Trans-Omics for Precision Medicine (TOPMed-r2) panel. We performed association testing with 898 diseases from electronic health records. 185 independent loci reached genome-wide significance (p < 5 × 10-8) under the recessive model, with p values lower than under the additive model, and >40% of these were novel. 140 loci demonstrated nominally significant (p < 0.05) dominance deviation p values, confirming a recessive association pattern. Sixteen loci in three clusters were significant at a Bonferroni threshold, accounting for multiple phenotypes tested (p < 5.4 × 10-12). In FinnGen, we replicated 44% of the expected number of Bonferroni-significant loci we were powered to replicate, at least one from each cluster, including an intronic variant in patatin-like phospholipase domain-containing protein 3 (PNPLA3; rs66812091) and non-alcoholic fatty liver disease, a previously reported additive association. We present evidence suggesting that the association is recessive instead (odds ratio [OR] = 1.3, recessive p = 2 × 10-12, additive p = 2 × 10-11, dominance deviation p = 3 × 10-2, and FinnGen recessive OR = 1.3 and p = 6 × 10-12). We identified a novel protective recessive association between a missense variant in SGLT4 (rs61746559), a sodium-glucose transporter with a possible role in the renin-angiotensin-aldosterone system, and hypertension (OR = 0.2, p = 3 × 10-8, dominance deviation p = 7 × 10-6). These results motivate interrogating recessive effects on common diseases more widely.

Comparison of linear mixed models for genetic feather score analysis in laying hens kept in recurrent testing facilities

Feather pecking, related feather damage, and mortality are crucial welfare and efficiency traits in laying hens. When individuals are kept in sib groups, genetic analysis of feather scores captures the performer and receiver components of feather damage due to pecking. Genetic parameters and breeding values estimated from such data with an ordinary linear mixed model refer to total breeding values. Thus, breeding against feather pecking-related mortality is possible by selecting breeding values for feather score when these are estimated from sib-groups. "Feather score" is part of the selection indexes of some breeding companies. However, there is no public information on the extensive evaluation and validation of the models used. Moreover, survival and feather score are genetically correlated, potentially biasing feather score-breeding value and genetic parameters estimation. This study compared and validated six models for genetic analysis of feather scores in the back and neck regions of laying hens at 45 and 70 weeks of age. We tested univariate models of feather score along with bivariate models for feather score and survival, and both sire and animal models, using individual or cage-level records. We compared the performance of the models based on the accuracy and dispersion of estimated breeding values. Additionally, genetic variances for all traits were estimated and compared. The individual-level univariate animal model showed the poorest performance for both accuracy and dispersion. Apart from the previous, no clear superiority regarding accuracy was observed between animal and sire models, nor between univariate and bivariate models. Breeding values estimated from cage-level observations tended to show less over-dispersion, and the estimates from the univariate cage-level sire model showed no significant over-dispersion for all traits. Since the univariate cage-level sire model was the simplest, as accurate as any of the other models, and showed no over-dispersion, it was considered the best model for feather score analysis with recurrent testing data.

Testing for heterogeneous rates of discrete character evolution on phylogenies

Many hypotheses in the field of phylogenetic comparative biology involve specific changes in the rate or process of trait evolution. This is particularly true of approaches designed to connect macroevolutionary pattern to microevolutionary process. We present a method to test whether the rate of evolution of a discrete character has changed in one or more clades, lineages, or time periods. This method differs from other related approaches (such as the "covarion" model) in that the "regimes" in which the rate or process is postulated to have changed are specified a priori by the user, rather than inferred from the data. Similarly, it differs from methods designed to model a correlation between two binary traits in that the regimes mapped onto the tree are fixed. We apply our method to investigate the rate of dewlap colour and/or caudal vertebra number evolution in Caribbean and mainland clades of the diverse lizard genus Anolis. We find little evidence to support any difference in the evolutionary process between mainland and island evolution for either character. We also examine the statistical properties of the method more generally and show that it has acceptable type I error, parameter estimation, and power. Finally, we discuss some general issues of frequentist hypothesis testing and model adequacy, as well as the relationship of our method to existing models of heterogeneity in the rate of discrete character evolution on phylogenies.

Genetic liability estimated from large-scale family data improves genetic prediction, risk score profiling, and gene mapping for major depression

Large biobank samples provide an opportunity to integrate broad phenotyping, familial records, and molecular genetics data to study complex traits and diseases. We introduce Pearson-Aitken Family Genetic Risk Scores (PA-FGRS), a method for estimating disease liability from patterns of diagnoses in extended, age-censored genealogical records. We then apply the method to study a paradigmatic complex disorder, major depressive disorder (MDD), using the iPSYCH2015 case-cohort study of 30,949 MDD cases, 39,655 random population controls, and more than 2 million relatives. We show that combining PA-FGRS liabilities estimated from family records with molecular genotypes of probands improves three lines of inquiry. Incorporating PA-FGRS liabilities improves classification of MDD over and above polygenic scores, identifies robust genetic contributions to clinical heterogeneity in MDD associated with comorbidity, recurrence, and severity and can improve the power of genome-wide association studies. Our method is flexible and easy to use, and our study approaches are generalizable to other datasets and other complex traits and diseases.

Converting estimated breeding values from the observed to probability scale for health traits

Dairy cattle health traits are paramount from a welfare and economic viewpoint, and modern breeding programs therefore prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not exceeding a threshold. This methodology requires significant computing time and faces convergence challenges, as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome these challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability, and then to the probability scale, in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1 million), ketosis (3.6 million), lameness (5 million), and mastitis (6.3 million) records from a Holstein population with a pedigree of 6 million animals, of which 1.7 million were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3 times longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs

BACKGROUND

In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS).

METHODS

Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates.

RESULTS

Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies.

CONCLUSIONS

Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

A comparative study of body size evolution in moths: evidence of correlated evolution with feeding and phenology-related traits

Interspecific variation in body size is one of the most popular topics in comparative studies. Despite recent advances, little is known about the patterns and processes behind the evolution of body size in insects. Here, we used a robust data set comprising all geometrid moth species occurring in Northern Europe to examine the evolutionary associations involving body size and several life-history traits under an explicitly phylogenetic framework. We provided new insights into the interactive effects of life-history traits on body size and evidence of correlated evolution. We further established the sequence of trait evolution linking body size with the life-history traits correlated with it. We found that most (but not all) of the studied life-history traits, to some extent, influenced interspecific variation in body size, but interactive effects were uncommon. Both bi- and multivariate phylogenetic analyses indicated that larger species tend to be nocturnal flyers, overwinter in the larval stage, feed on the foliage of trees rather than herbs, and have a generalist feeding behaviour. We found evidence of correlated evolution involving body size with overwintering stage, host-plant growth form, and dietary specialization. The examination of evolutionary transitions within the correlated evolution models signalled that overwintering as larvae commonly preceded the evolution of large sizes, as did feeding on tree foliage and the generalist feeding behaviour. By showing that both body size and all life-history traits correlated with it evolve at very slow rates, we caution against uncritical attempts to propose causal explanations for respective associations based on contemporary ecological settings.

Fast Bayesian Inference of Phylogenies from Multiple Continuous Characters

Time-scaled phylogenetic trees are an ultimate goal of evolutionary biology and a necessary ingredient in comparative studies. The accumulation of genomic data has resolved the tree of life to a great extent, yet timing evolutionary events remain challenging if not impossible without external information such as fossil ages and morphological characters. Methods for incorporating morphology in tree estimation have lagged behind their molecular counterparts, especially in the case of continuous characters. Despite recent advances, such tools are still direly needed as we approach the limits of what molecules can teach us. Here, we implement a suite of state-of-the-art methods for leveraging continuous morphology in phylogenetics, and by conducting extensive simulation studies we thoroughly validate and explore our methods' properties. While retaining model generality and scalability, we make it possible to estimate absolute and relative divergence times from multiple continuous characters while accounting for uncertainty. We compile and analyze one of the most data-type diverse data sets to date, comprised of contemporaneous and ancient molecular sequences, and discrete and continuous morphological characters from living and extinct Carnivora taxa. We conclude by synthesizing lessons about our method's behavior, and suggest future research venues.

FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study

Large sample datasets have been regarded as the primary basis for innovative discoveries and the solution to missing heritability in genome-wide association studies. However, their computational complexity cannot consider all comprehensive effects and all polygenic backgrounds, which reduces the effectiveness of large datasets. To address these challenges, we included all effects and polygenic backgrounds in a mixed logistic model for binary traits and compressed four variance components into two. The compressed model combined three computational algorithms to develop an innovative method, called FastBiCmrMLM, for large data analysis. These algorithms were tailored to sample size, computational speed, and reduced memory requirements. To mine additional genes, linkage disequilibrium markers were replaced by bin-based haplotypes, which are analyzed by FastBiCmrMLM, named FastBiCmrMLM-Hap. Simulation studies highlighted the superiority of FastBiCmrMLM over GMMAT, SAIGE and fastGWA-GLMM in identifying dominant, small α (allele substitution effect), and rare variants. In the UK Biobank-scale dataset, we demonstrated that FastBiCmrMLM could detect variants as small as 0.03% and with α ≈ 0. In re-analyses of seven diseases in the WTCCC datasets, 29 candidate genes, with both functional and TWAS evidence, around 36 variants identified only by the new methods, strongly validated the new methods. These methods offer a new way to decipher the genetic architecture of binary traits and address the challenges outlined above.

Predicting photosynthetic pathway from anatomy using machine learning

Plants with Crassulacean acid metabolism (CAM) have long been associated with a specialized anatomy, including succulence and thick photosynthetic tissues. Firm, quantitative boundaries between non-CAM and CAM plants have yet to be established - if they indeed exist. Using novel computer vision software to measure anatomy, we combined new measurements with published data across flowering plants. We then used machine learning and phylogenetic comparative methods to investigate relationships between CAM and anatomy. We found significant differences in photosynthetic tissue anatomy between plants with differing CAM phenotypes. Machine learning-based classification was over 95% accurate in differentiating CAM from non-CAM anatomy, and had over 70% recall of distinct CAM phenotypes. Phylogenetic least squares regression and threshold analyses revealed that CAM evolution was significantly correlated with increased mesophyll cell size, thicker leaves, and decreased intercellular airspace. Our findings suggest that machine learning may be used to aid the discovery of new CAM species and that the evolutionary trajectory from non-CAM to strong, obligate CAM requires continual anatomical specialization.

How do stochastic processes and genetic threshold effects explain incomplete penetrance and inform causal disease mechanisms?

Incomplete penetrance is the rule rather than the exception in Mendelian disease. In syndromic monogenic disorders, phenotypic variability can be viewed as the combination of incomplete penetrance for each of multiple independent clinical features. Within genetically identical individuals, such as isogenic model organisms, stochastic variation at molecular and cellular levels is the primary cause of incomplete penetrance according to a genetic threshold model. By defining specific probability distributions of causal biological readouts and genetic liability values, stochasticity and incomplete penetrance provide information about threshold values in biological systems. Ascertainment of threshold values has been achieved by simultaneous scoring of relatively simple phenotypes and quantitation of molecular readouts at the level of single cells. However, this is much more challenging for complex morphological phenotypes using experimental and reductionist approaches alone, where cause and effect are separated temporally and across multiple biological modes and scales. Here I consider how causal inference, which integrates observational data with high confidence causal models, might be used to quantify the relative contribution of different sources of stochastic variation to phenotypic diversity. Collectively, these approaches could inform disease mechanisms, improve predictions of clinical outcomes and prioritize gene therapy targets across modes and scales of gene function. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

When can we reconstruct the ancestral state? A unified theory

Ancestral state reconstruction is one of the most important tasks in evolutionary biology. Conditions under which we can reliably reconstruct the ancestral state have been studied for both discrete and continuous traits. However, the connection between these results is unclear, and it seems that each model needs different conditions. In this work, we provide a unifying theory on the consistency of ancestral state reconstruction for various types of trait evolution models. Notably, we show that for a sequence of nested trees with bounded heights, the necessary and sufficient conditions for the existence of a consistent ancestral state reconstruction method under discrete models, the Brownian motion model, and the threshold model are equivalent. When tree heights are unbounded, we provide a simple counter-example to show that this equivalence is no longer valid.

What’s Shape Got to Do With It? Examining the Relationship Between Facial Shape and Orofacial Clefting

Nonsyndromic orofacial clefts belong to a class of congenital malformations characterized by a complex and multifactorial etiology. During early facial development, multiple factors can disrupt fusion leading to a cleft; this includes the shape of the embryonic face. The face shape hypothesis (FSH) of orofacial clefting emerged in the 1960s, influenced by morphological differences observed within affected families, comparative studies of mouse models, and advances in modeling genetic liability for complex traits in populations. For the past five decades, studies have documented changes in the shape or spatial arrangement of facial prominences in embryonic mice and altered post-natal facial shape in individuals at elevated risk for orofacial clefting due to their family history. Moreover, recent studies showing how genes that impact facial shape in humans and mice are providing clues about the genetic basis of orofacial clefting. In this review, I discuss the origins of the FSH, provide an overview of the supporting evidence, and discuss ways in which the FSH can inform our understanding of orofacial clefting.

Ecological Filtering and Exaptation in the Evolution of Marine Snakes

AbstractConvergent evolution is often attributed to adaptation of form to function, but it can also result from ecological filtering, exaptation, or nonaptation. Testing among these possibilities is critical to understanding how and why morphological similarities emerge independently in multiple lineages. To address this challenge, we combined multiple preexisting phylogenetic methods to jointly estimate the habitats and morphologies of lineages within a phylogeny. We applied this approach to the invasions of snakes into the marine realm. We utilized a data set for 1,243 extant snake species consisting of newly compiled biome occupancy information and preexisting data on reproductive strategy, body mass, and environmental temperature and elevation. We find evidence for marine clades arising from a variety of aquatic and terrestrial habitats. Furthermore, there is strong evidence of ecological filtering for nonmarine ancestors that were already viviparous, had slightly larger-than-average body sizes, and lived in environments with higher-than-average temperatures and lower-than-average elevations. In aggregate, similarities among independent lineages of marine snakes result from a combination of exaptation and strong ecological filtering. Strong barriers to entry of new habitats appear to be more important than common adaptations following invasions for producing similarities among independent lineages invading a shared, novel habitat.

Polygenic and major-locus contributions to sexual maturation timing in Atlantic salmon

Sexual maturation timing is a life-history trait central to the balance between mortality and reproduction. Maturation may be triggered when an underlying compound trait, called liability, exceeds a threshold. In many different species and especially fishes, this liability is approximated by growth and body condition. However, environmental vs. genetic contributions either directly or via growth and body condition to maturation timing remain unclear. Uncertainty exists also because the maturation process can reverse this causality and itself affect growth and body condition. In addition, disentangling the contributions of polygenic and major loci can be important. In many fishes, males mature before females, enabling the study of associations between male maturation and maturation-unbiased female liability traits. Using 40 Atlantic salmon families, longitudinal common-garden experimentation, and quantitative genetic analyses, we disentangled environmental from polygenic and major locus (vgll3) effects on male maturation, and sex-specific growth and condition. We detected polygenic heritabilities for maturation, growth, and body condition, and vgll3 effects on maturation and body condition but not on growth. Longitudinal patterns for sex-specific phenotypic liability, and for genetic variances and correlations between sexes suggested that early growth and condition indeed positively affected maturation initiation. However, towards spawning time, causality appeared reversed for males whereby maturation affected growth negatively and condition positively via both the environmental and genetic effects. Altogether, the results indicate that growth and condition are useful traits to study liability for maturation initiation, but only until maturation alters their expression, and that vgll3 contributes to maturation initiation via condition.

Evolutionary Sample Size and Consilience in Phylogenetic Comparative Analysis

Phylogenetic comparative methods (PCMs) are commonly used to study evolution and adaptation. However, frequently used PCMs for discrete traits mishandle single evolutionary transitions. They erroneously detect correlated evolution in these situations. For example, hair and mammary glands cannot be said to have evolved in a correlated fashion because each evolved only once in mammals, but a commonly used model (Pagel's Discrete) statistically supports correlated (dependent) evolution. Using simulations, we find that rate parameter estimation, which is central for model selection, is poor in these scenarios due to small effective (evolutionary) sample sizes of independent character state change. Pagel's Discrete model also tends to favor dependent evolution in these scenarios, in part, because it forces evolution through state combinations unobserved in the tip data. This model prohibits simultaneous dual transitions along branches. Models with underlying continuous data distributions (e.g., Threshold and GLMM) are less prone to favor correlated evolution, but are still susceptible when evolutionary sample sizes are small. We provide three general recommendations for researchers who encounter these common situations: 1) Create study designs that evaluate a priori hypotheses and maximize evolutionary sample sizes; 2) assess the suitability of evolutionary models-for discrete traits, we introduce the phylogenetic imbalance ratio; and 3) evaluate evolutionary hypotheses with a consilience of evidence from disparate fields, like biogeography and developmental biology. Consilience plays a central role in hypothesis testing within the historical sciences where experiments are difficult or impossible to conduct, such as many hypotheses about correlated evolution. These recommendations are useful for investigations that employ any type of PCM.

A 44-kb deleted-type copy number variation is associated with decreasing complement component activity and calf mortality in Japanese Black cattle

BACKGROUND

Calf mortality generally occurs in calves prior to weaning, which is a serious problem in cattle breeding. Several causative variants of monogenic Mendelian disorders in calf mortality have been identified, whereas genetic factors affecting the susceptibility of calves to death are not well known. To identify variants associated with calf mortality in Japanese Black cattle, we evaluated calf mortality as a categorical trait with a threshold model and performed a genome-wide copy number variation (CNV) association study on calf mortality.

RESULTS

We identified a 44-kb deleted-type CNV ranging from 103,317,687 to 103,361,802 bp on chromosome 5, which was associated with the mortality of 1-180-day-old calves. The CNV harbored C1RL, a pseudogene, and an IncRNA localized in the C1R and C1S gene cluster, which is a component of the classical complement activation pathway for immune complexes for infectious pathogens. The average complement activity in CNVR_221 homozygotes at postnatal day 7 was significantly lower than that of wild-type animals and heterozygotes. The frequency of the risk allele in dead calves suffering from diarrhea and pneumonia and in healthy cows was 0.35 and 0.28, respectively (odds ratio = 2.2, P = 0.016), suggesting that CNVR_221 was associated with the mortality of Japanese Black calves suffering from an infectious disease.

CONCLUSIONS

This study identified a deleted-type CNV associated with the mortality of 1-180-day-old calves. The complement activity in CNVR_221 homozygotes was significantly lower than that in heterozygotes and wild type animals. The frequency of the risk allele was higher in dead calves suffering from an infectious disease than in healthy cows. These results suggest that the existence of CNVR_221 in calves could be attributed to a reduction in complement activity, which in turn leads to susceptibility to infections. Thus, the risk allele could serve as a useful marker to reduce the mortality of infected Japanese Black calves.

A genome-wide association study reveals a quantitative trait locus for calf mortality on chromosome 9 in Japanese Black cattle

Calf mortality is a major problem affecting cattle production. To identify genetic variants associated with calf mortality in Japanese Black cattle, we evaluated calf mortality as a categorical trait using a threshold model and conducted a GWAS. We identified two SNPs between 32 549 297 and 32 606 924 bp on bovine chromosome 9 that were significantly associated with calf mortality from 61 to 180 days after birth. The SNP showing the highest association was localized at a region 624 bp downstream of exon 4 of the anti-silencing function 1A histone chaperone gene (ASF1A) that promotes DNA damage repair, and the null mice, which exhibit pre- and postnatal lethality. This association was also detected using the breeding value of 334 sires. The frequency of the risk allele in Japanese Black cattle from locations across Japan was 0.013; although the frequency of ASF1A risk allele was low, it is widespread in the Japanese Black cattle population. Thus, it may be necessary to routinely monitor the cattle population for the presence of this allele.

A farewell to arms and legs: a review of limb reduction in squamates

Elongated snake-like bodies associated with limb reduction have evolved multiple times throughout vertebrate history. Limb-reduced squamates (lizards and snakes) account for the vast majority of these morphological transformations, and thus have great potential for revealing macroevolutionary transitions and modes of body-shape transformation. Here we present a comprehensive review on limb reduction, in which we examine and discuss research on these dramatic morphological transitions. Historically, there have been several approaches to the study of squamate limb reduction: (i) definitions of general anatomical principles of snake-like body shapes, expressed as varying relationships between body parts and morphometric measurements; (ii) framing of limb reduction from an evolutionary perspective using morphological comparisons; (iii) defining developmental mechanisms involved in the ontogeny of limb-reduced forms, and their genetic basis; (iv) reconstructions of the evolutionary history of limb-reduced lineages using phylogenetic comparative methods; (v) studies of functional and biomechanical aspects of limb-reduced body shapes; and (vi) studies of ecological and biogeographical correlates of limb reduction. For each of these approaches, we highlight their importance in advancing our understanding, as well as their weaknesses and limitations. Lastly, we provide suggestions to stimulate further studies, in which we underscore the necessity of widening the scope of analyses, and of bringing together different perspectives in order to understand better these morphological transitions and their evolution. In particular, we emphasise the importance of investigating and comparing the internal morphology of limb-reduced lizards in contrast to external morphology, which will be the first step in gaining a deeper insight into body-shape variation.

Flower Diversification Across "Pollinator Climates": Sensory Aspects of Corolla Color Evolution in the Florally Diverse South American Genus Jaborosa (Solanaceae)

Flower phenotype may diverge within plant lineages when moving across "pollinator climates" (geographic differences in pollinator abundance or preference). Here we explored the potential importance of pollinators as drivers of floral color diversification in the nightshade genus Jaborosa, taking into account color perception capabilities of the actual pollinators (nocturnal hawkmoths vs. saprophilous flies) under a geographic perspective. We analyzed the association between transitions across environments and perceptual color axes using comparative methods. Our results revealed two major evolutionary themes in Jaborosa: (1) a "warm subtropical sphingophilous clade" composed of three hawkmoth-pollinated species found in humid lowland habitats, with large white flowers that clustered together in the visual space of a model hawkmoth (Manduca sexta) and a "cool-temperate brood-deceptive clade" composed of largely fly-pollinated species with small dark flowers found at high altitudes (Andes) or latitudes (Patagonian Steppe), that clustered together in the visual space of a model blowfly (Lucilia sp.) and a syrphid fly (Eristalis tenax). Our findings suggest that the ability of plants to colonize newly formed environments during Andean orogeny and the ecological changes that followed were concomitant with transitions in flower color as perceived by different pollinator functional groups. Our findings suggest that habitat and pollination mode are inextricably linked in the history of this South American plant lineage.

Test positivity for Maedi-Visna virus and Mycobacterium avium ssp. paratuberculosis in Sarda ewes: Effects on milk composition and coagulation traits and heritability estimates for susceptibility

Maedi-Visna virus (MVV) and Mycobacterium avium ssp. paratuberculosis (MAP) are two pathogens that cause chronic, production-limiting diseases in dairy sheep. Although they are present worldwide, there are no detailed reports on their actual effects on milk traits in the literature. This study was designed to investigate the effects of test positivity to MVV and MAP on ovine milk yield, composition and coagulation properties, and curd-firming over time (CF_t) variables in clinically healthy animals at the field level. The additive genetic variation and heritabilities of MVV and MAP positivity were also estimated. Milk samples were collected from 1,079 Sarda sheep kept on 23 farms, and pedigree information was obtained from the flock book. Milk yield was also recorded on the sampling date. Positivity for MVV and MAP was determined from milk samples using indirect ELISA test kits. Milk composition traits were measured by spectroscopy, milk coagulation properties were measured with a Formagraph (Foss Italia, Padua, Italy), and CF_t traits were calculated using the data from the Formagraph diagram. The effects of MVV and MAP positivity on milk traits were determined through a set of mixed linear models, which took into account various sources of variation, such as days in milk, parity, and flock effects, and included the effects (positive or negative) of the 2 pathogens. A Bayesian threshold sire model with sire relationship was used to estimate genetic variation and heritability. The overall animal prevalence of MVV-positive ewes was 43.6%; on only 1 farm of the 23 tested were all sampled ewes negative. An overall animal prevalence of 10.6% was recorded for MAP, with 4 farms at 0%. Positivity for MVV significantly affected the logarithmic score of the bacterial count, curd firmness after 30 min and 45 min, and the curd-firming instant rate constant. We found significant effects of MAP infection on milk composition, pH, and rennet coagulation time. The mean of the posterior distributions of heritability estimates on the liability scale was 0.15 for MAP and 0.07 for MVV. Our results demonstrate that only a few traits are negatively affected by MVV and MAP positivity, and that there is exploitable genetic variation in MVV and MAP susceptibility in dairy sheep.

Genetic growth potential, rather than phenotypic size, predicts migration phenotype in Atlantic salmon

Knowledge of the relative importance of genetic versus environmental determinants of major developmental transitions is pertinent to understanding phenotypic evolution. In salmonid fishes, a major developmental transition enables a risky seaward migration that provides access to feed resources. In Atlantic salmon, initiation of the migrant phenotype, and thus age of migrants, is presumably controlled via thresholds of a quantitative liability, approximated by body size expressed long before the migration. However, how well size approximates liability, both genetically and environmentally, remains uncertain. We studied 32 Atlantic salmon families in two temperatures and feeding regimes (fully fed, temporarily restricted) to completion of migration status at age 1 year. We detected a lower migrant probability in the cold (0.42) than the warm environment (0.76), but no effects of male maturation status or feed restriction. By contrast, body length in late summer predicted migrant probability and its control reduced migrant probability heritability by 50-70%. Furthermore, migrant probability and length showed high heritabilities and between-environment genetic correlations, and were phenotypically highly correlated with stronger genetic than environmental contributions. Altogether, quantitative estimates for the genetic and environmental effects predicting the migrant phenotype indicate, for a given temperature, a larger importance of genetic than environmental size effects.

Novel approach to incorporate information about recessive lethal genes increases the accuracy of genomic prediction for mortality traits

The genetic underpinnings of calf mortality can be partly polygenic and partly due to deleterious effects of recessive lethal alleles. Prediction of the genetic merits of selection candidates should thus take into account both genetic components contributing to calf mortality. However, simultaneously modeling polygenic risk and recessive lethal allele effects in genomic prediction is challenging due to effects that behave differently. In this study, we present a novel approach where mortality risk probabilities from polygenic and lethal allele components are predicted separately to compute the total risk probability of an individual for its future offspring as a basis for selection. We present methods for transforming genomic estimated breeding values of polygenic effect into risk probabilities using normal density and cumulative distribution functions and show computations of risk probability from recessive lethal alleles given sire genotypes and population recessive allele frequencies. Simulated data were used to test the novel approach as implemented in probit, logit, and linear models. In the simulation study, the accuracy of predicted risk probabilities was computed as the correlation between predicted mortality probabilities and observed calf mortality for validation sires. The results indicate that our novel approach can greatly increase the accuracy of selection for mortality traits compared with the accuracy of predictions obtained without distinguishing polygenic and lethal gene effects.

Inferring phenotypic causal structures among body weight traits via structural equation modeling in Kurdi sheep

Data collected on 2550 Kurdi lambs originated from 1505 dams and 149 sires during 1991 to 2015 in Hossein Abad Kurdi Sheep Breeding Station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used for inferring causal relationship among the body weights at birth (BW), at weaning (WW), at six-month age (6MW), at nine-month age (9MW) and yearling age (YW). The inductive causation (IC) algorithm was employed to search for causal structure among these traits. This algorithm was applied to the posterior distribution of the residual (co)variance matrix of a standard multivariate model (SMM). The causal structure detected by the IC algorithm coupling with biological prior knowledge provides a temporal recursive causal network among the studied traits. The studied traits were analyzed under three multivariate models including SMM, fully recursive multivariate model (FRM) and IC-based multivariate model (ICM) via a Bayesian approach by 100,000 iterations, thinning interval of 10 and the first 10,000 iterations as burn-in. The three considered multivariate models (SMM, FRM and ICM) were compared using deviance information criterion (DIC) and predictive ability measures including mean square of error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, )) of records. In general, structural equation based models (FRM and ICM) performed better than SMM in terms of lower DIC and MSE and also higher r(y, ). Among the tested models ICM had the lowest (36678.551) and SMM had the highest (36744.107)DIC values. In each case of the traits studied, the lowest MSE and the highest r(y, ) were obtained under ICM. The causal effects of BW on WW, WW on 6MW, 6MW on 9MW and 9MW on YW were statistically significant values of 1.478, 0.737, 0.776 and 0.929 kg, respectively (99% highest posterior density intervals did not include zero).

Revisiting a Landmark Study System: No Evidence for a Punctuated Mode of Evolution in Metrarabdotos

Is speciation generally a "special time" in morphological evolution, or are lineage-splitting events just "more of the same" where the end product happens to be two separate lineages? Data on evolutionary dynamics during anagenetic and cladogenetic events among closely related lineages within a clade are rare, but the fossil record of the bryozoan genus Metrarabdotos is considered a textbook example of a clade where speciation causes rapid evolutionary change against a backdrop of morphological stasis within lineages. Here, we point to some methodological and measurement theoretical issues in the original work on Metrarabdotos. We then reanalyze a subset of the original data that can be meaningfully investigated using quantitative statistical approaches similar to those used in the original studies. We consistently fail to find variation in the evolutionary process during within-lineage evolution compared with cladogenetic events: the rates of evolution, the strength of selection, and the directions traveled in multivariate morphospace are not different when comparing evolution within lineages and at speciation events in Metrarabdotos, and genetic drift cannot be excluded as a sufficient explanation for the morphological differentiation within lineages and during speciation. Although widely considered the best example of a punctuated mode of evolution, morphological divergence and speciation are not linked in Metrarabdotos.

Phylogenetic signal and evolutionary correlates of urban tolerance in a widespread neotropical lizard clade

Urbanization is intensifying worldwide, and while some species tolerate and even exploit urban environments, many others are excluded entirely from this new habitat. Understanding the factors that underlie tolerance of urbanization is thus of rapidly growing importance. Here, we examine urban tolerance across a diverse group of lizards: Caribbean members of the neotropical genus Anolis. Our analyses reveal that urban tolerance has strong phylogenetic signal, suggesting that closely related species tend to respond similarly to urban environments. We propose that this characteristic of urban tolerance in anoles may be used to forecast the possible responses of species to increasing urbanization. In addition, we identified several key ecological and morphological traits that tend to be associated with tolerance in Anolis. Specifically, species experiencing hot and dry conditions in their natural environment and those that maintain higher body temperatures tend to have greater tolerance of urban habitats. We also found that tolerance of urbanization is positively associated with toepad lamella number and negatively associated with ventral scale density and relative hindlimb length. The identification of factors that predispose a species to be more or less urban tolerant can provide a starting point for conservation and sustainable development in our increasingly urbanized world.

Predicting Polygenic Risk of Psychiatric Disorders

Genetics provides two major opportunities for understanding human disease-as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.

Investigating causal biological relationships between reproductive performance traits in high-performing gilts and sows1

Efficient management of swine production systems requires understanding of complex reproductive physiological mechanisms. Our objective in this study was to investigate potential causal biological relationships between reproductive performance traits in high-producing gilts and sows. Data originated from a nutrition experiment and consisted of 200 sows and 440 gilts arranged in body weight blocks and randomly assigned to dietary treatments during late gestation at a commercial swine farm. Reproductive performance traits consisted of weight gain during late gestation, total number born and number born alive in a litter, born alive average birth weight, wean-to-estrous interval, and total litter size born in the subsequent farrowing. Structural equation models combined with the inductive causation algorithm, both adapted to a hierarchical Bayesian framework, were employed to search for, estimate, and infer upon causal links between the traits within each parity group. Results indicated potentially distinct reproductive networks for gilts and for sows. Sows showed sparse connectivity between reproductive traits, whereas the network learned for gilts was densely interconnected, suggesting closely linked physiological mechanisms in younger females, with a potential for ripple effects throughout their productive lifecycle in response to early implementation of tailored managerial interventions. Cross-validation analyses indicated substantial network stability both for the general structure and for individual links, though results about directionality of such links were unstable in this study and will need further investigation. An assessment of relative statistical power in sows and gilts indicated that the observed network discrepancies may be partially explained on a biological basis. In summary, our results suggest distinctly heterogeneous mechanistic networks of reproductive physiology for gilts and sows, consistent with physiological differences between the groups. These findings have potential practical implications for integrated understanding and differential management of gilts and sows to enhance efficiency of swine production systems.

A Systematist's Guide to Estimating Bayesian Phylogenies From Morphological Data

Phylogenetic trees are crucial to many aspects of taxonomic and comparative biology. Many researchers have adopted Bayesian methods to estimate their phylogenetic trees. In this family of methods, a model of morphological evolution is assumed to have generated the data observed by the researcher. These models make a variety of assumptions about the evolution of morphological characters, and these assumptions are translated into mathematics as parameters. The incorporation of prior distributions further allows researchers to quantify their prior beliefs about the value any one parameter can take. How to translate biological knowledge into mathematical language is difficult, and can be confusing to many biologists. This review aims to help systematics researchers understand the biological meaning of common models and assumptions. Using examples from the insect fossil record, I will demonstrate empirically what assumptions mean in concrete terms, and discuss how researchers can use and understand Bayesian methods for phylogenetic estimation.

Meiotic drive shapes rates of karyotype evolution in mammals

Chromosome number is perhaps the most basic characteristic of a genome, yet generalizations that can explain the evolution of this trait across large clades have remained elusive. Using karyotype data from over 1000 mammals, we developed and applied a phylogenetic model of chromosome evolution that links chromosome number changes with karyotype morphology. Using our model, we infer that rates of chromosome number evolution are significantly lower in species with karyotypes that consist of either all bibrachial or all monobrachial chromosomes than in species with a mix of both types of morphologies. We suggest that species with homogeneous karyotypes may represent cases where meiotic drive acts to stabilize the karyotype, favoring the chromosome morphologies already present in the genome. In contrast, rapid bouts of chromosome number evolution in taxa with mixed karyotypes may indicate that a switch in the polarity of female meiotic drive favors changes in chromosome number. We do not find any evidence that karyotype morphology affects rates of speciation or extinction. Furthermore, we document that switches in meiotic drive polarity are likely common and have occurred in most major clades of mammals, and that rapid remodeling of karyotypes may be more common than once thought.

A multispecies coalescent model for quantitative traits

We present a multispecies coalescent model for quantitative traits that allows for evolutionary inferences at micro- and macroevolutionary scales. A major advantage of this model is its ability to incorporate genealogical discordance underlying a quantitative trait. We show that discordance causes a decrease in the expected trait covariance between more closely related species relative to more distantly related species. If unaccounted for, this outcome can lead to an overestimation of a trait's evolutionary rate, to a decrease in its phylogenetic signal, and to errors when examining shifts in mean trait values. The number of loci controlling a quantitative trait appears to be irrelevant to all trends reported, and discordance also affected discrete, threshold traits. Our model and analyses point to the conditions under which different methods should fare better or worse, in addition to indicating current and future approaches that can mitigate the effects of discordance.

Genetic analysis of deep phenotyping projects in common disorders

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

Quantitative genetic methods depending on the nature of the phenotypic trait

A consequence of the assumptions of the infinitesimal model, one of the most important theoretical foundations of quantitative genetics, is that phenotypic traits are predicted to be most often normally distributed (so-called Gaussian traits). But phenotypic traits, especially those interesting for evolutionary biology, might be shaped according to very diverse distributions. Here, I show how quantitative genetics tools have been extended to account for a wider diversity of phenotypic traits using first the threshold model and then more recently using generalized linear mixed models. I explore the assumptions behind these models and how they can be used to study the genetics of non-Gaussian complex traits. I also comment on three recent methodological advances in quantitative genetics that widen our ability to study new kinds of traits: the use of "modular" hierarchical modeling (e.g., to study survival in the context of capture-recapture approaches for wild populations); the use of aster models to study a set of traits with conditional relationships (e.g., life-history traits); and, finally, the study of high-dimensional traits, such as gene expression.

Evolutionary biology today and the call for an extended synthesis

Evolutionary theory has been extended almost continually since the evolutionary synthesis (ES), but except for the much greater importance afforded genetic drift, the principal tenets of the ES have been strongly supported. Adaptations are attributable to the sorting of genetic variation by natural selection, which remains the only known cause of increase in fitness. Mutations are not adaptively directed, but as principal authors of the ES recognized, the material (structural) bases of biochemistry and development affect the variety of phenotypic variations that arise by mutation and recombination. Against this historical background, I analyse major propositions in the movement for an 'extended evolutionary synthesis'. 'Niche construction' is a new label for a wide variety of well-known phenomena, many of which have been extensively studied, but (as with every topic in evolutionary biology) some aspects may have been understudied. There is no reason to consider it a neglected 'process' of evolution. The proposition that phenotypic plasticity may engender new adaptive phenotypes that are later genetically assimilated or accommodated is theoretically plausible; it may be most likely when the new phenotype is not truly novel, but is instead a slight extension of a reaction norm already shaped by natural selection in similar environments. However, evolution in new environments often compensates for maladaptive plastic phenotypic responses. The union of population genetic theory with mechanistic understanding of developmental processes enables more complete understanding by joining ultimate and proximate causation; but the latter does not replace or invalidate the former. Newly discovered molecular phenomena have been easily accommodated in the past by elaborating orthodox evolutionary theory, and it appears that the same holds today for phenomena such as epigenetic inheritance. In several of these areas, empirical evidence is needed to evaluate enthusiastic speculation. Evolutionary theory will continue to be extended, but there is no sign that it requires emendation.

Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome

The popular Genome-wide Complex Trait Analysis (GCTA) software uses the random-effects models for estimating the narrow-sense heritability based on GWAS data of unrelated individuals without knowing and identifying the causal loci. Many methods have since extended this approach to various situations. However, since the proportion of causal loci among the variants is typically very small and GCTA uses all variants to calculate the similarities among individuals, the estimation of heritability may be unstable, resulting in a large variance of the estimates. Moreover, if the causal SNPs are not genotyped, GCTA sometimes greatly underestimates the true heritability. We present a novel narrow-sense heritability estimator, named HERRA, using well-developed ultra-high dimensional machine-learning methods, applicable to continuous or dichotomous outcomes, as other existing methods. Additionally, HERRA is applicable to time-to-event or age-at-onset outcome, which, to our knowledge, no existing method can handle. Compared to GCTA and LDAK for continuous and binary outcomes, HERRA often has a smaller variance, and when causal SNPs are not genotyped, HERRA has a much smaller empirical bias. We applied GCTA, LDAK and HERRA to a large colorectal cancer dataset using dichotomous outcome (4,312 cases, 4,356 controls, genotyped using Illumina 300K), the respective heritability estimates of GCTA, LDAK and HERRA are 0.068 (SE = 0.017), 0.072 (SE = 0.021) and 0.110 (SE = 5.19 x 10⁻³). HERRA yields over 50% increase in heritability estimate compared to GCTA or LDAK.

Genetic analysis for quality of frozen embryos produced by Holstein cattle donors in Canada

The number of embryos produced by Holstein donors has been shown to be heritable, so it could be possible to genetically select for this trait to improve the efficiency of the assisted reproductive technology (ART) in dairy cattle. Another important parameter to consider for achieving good results from ART is embryo quality because embryos of good quality have more chance of producing live offspring. The possibility of using genetic selection for increasing the quality of embryo produced from ART has yet to be assessed. The objective of this study was, therefore, to perform a genetic analysis of embryo quality of Holstein donors in Canada using data recorded by Holstein Canada. The data set used was missing quality score data for embryos transferred fresh into a recipient, so the analyses were only performed for frozen embryos. With most traits in the Canadian dairy industry being evaluated with linear models, embryo quality was also evaluated with this class of models. However, considering the categorical nature of embryo quality, a threshold model was also evaluated. Embryo quality data were analyzed with either a univariate linear animal model or a univariate binomial threshold animal model. Genetic parameters estimated from the different models were comparable. A low heritability was found for the donor (0.04 ± <0.01) and the service sire (0.02 ± <0.01), but the repeatability estimate for the donor was higher (0.17), indicating that it was worthwhile to use a repeated records model. Overall, considering the low genetic parameters estimated, slow genetic progress is expected for the quality of frozen embryos produced by Canadian Holstein donors. Rank correlations were calculated between breeding values estimated from different models. High correlations were found between all models, indicating that no substantial re-ranking of the animals is expected from the different models. So, even though a threshold model is better suited for the analysis of categorical data, a linear model could be used for the analysis of embryo quality because it is less computationally demanding.

Emerging Concepts of Data Integration in Pathogen Phylodynamics

Phylodynamics has become an increasingly popular statistical framework to extract evolutionary and epidemiological information from pathogen genomes. By harnessing such information, epidemiologists aim to shed light on the spatio-temporal patterns of spread and to test hypotheses about the underlying interaction of evolutionary and ecological dynamics in pathogen populations. Although the field has witnessed a rich development of statistical inference tools with increasing levels of sophistication, these tools initially focused on sequences as their sole primary data source. Integrating various sources of information, however, promises to deliver more precise insights in infectious diseases and to increase opportunities for statistical hypothesis testing. Here, we review how the emerging concept of data integration is stimulating new advances in Bayesian evolutionary inference methodology which formalize a marriage of statistical thinking and evolutionary biology. These approaches include connecting sequence to trait evolution, such as for host, phenotypic and geographic sampling information, but also the incorporation of covariates of evolutionary and epidemic processes in the reconstruction procedures. We highlight how a full Bayesian approach to covariate modeling and testing can generate further insights into sequence evolution, trait evolution, and population dynamics in pathogen populations. Specific examples demonstrate how such approaches can be used to test the impact of host on rabies and HIV evolutionary rates, to identify the drivers of influenza dispersal as well as the determinants of rabies cross-species transmissions, and to quantify the evolutionary dynamics of influenza antigenicity. Finally, we briefly discuss how data integration is now also permeating through the inference of transmission dynamics, leading to novel insights into tree-generative processes and detailed reconstructions of transmission trees. [Bayesian inference; birth–death models; coalescent models; continuous trait evolution; covariates; data integration; discrete trait evolution; pathogen phylodynamics.