The environmental and genetic architectures of ageing and CKD.

Obtaining accurate predictions of unobserved genetic or phenotypic values for complex traits in animal, plant, and human populations is possible through whole-genome prediction (WGP), a combined analysis of genotypic and phenotypic data. Because the underlying genetic architecture of the trait of interest is an important factor affecting model selection, we propose a new strategy, termed BLUP|GA (BLUP-given genetic architecture), which can use genetic architecture information within the dataset at hand rather than from public sources. This is achieved by using a trait-specific covariance matrix (T), which is a weighted sum of a genetic architecture part (S matrix) and the realized relationship matrix (G). The algorithm of BLUP|GA (BLUP-given genetic architecture) is provided and illustrated with real and simulated datasets. Predictive ability of BLUP|GA was validated with three model traits in a dairy cattle dataset and 11 traits in three public datasets with a variety of genetic architectures and compared with GBLUP and other approaches. Results show that BLUP|GA outperformed GBLUP in 20 of 21 scenarios in the dairy cattle dataset and outperformed GBLUP, BayesA, and BayesB in 12 of 13 traits in the analyzed public datasets. Further analyses showed that the difference of accuracies for BLUP|GA and GBLUP significantly correlate with the distance between the T and G matrices. The new strategy applied in BLUP|GA is a favorable and flexible alternative to the standard GBLUP model, allowing to account for the genetic architecture of the quantitative trait under consideration when necessary. This feature is mainly due to the increased similarity between the trait-specific relationship matrix (T matrix) and the genetic relationship matrix at unobserved causal loci. Applying BLUP|GA in WGP would ease the burden of model selection.

The control of gene expression is an important tool for metabolic engineering, the design of synthetic gene networks, and protein manufacturing. The most successful approaches to date are based on modulating mRNA synthesis via an inducible coupling to transcriptional effectors. Here we present a biological programming structure that leverages a system of engineered transcription factors and complementary genetic architectures. We use a modular design strategy to create 27 non-natural and non-synonymous transcription factors using the lactose repressor topology as a guide. To direct systems of engineered transcription factors we employ parallel and series genetic (DNA) architectures and confer fundamental and combinatorial logical control over gene expression. Here we achieve AND, OR, NOT, and NOR logical controls in addition to two non-canonical half-AND operations. The basic logical operations and corresponding parallel and series genetic architectures represent the building blocks for subsequent combinatorial programs, which display both digital and analog performance.

Whole genome evaluation of quantitative traits using suitable statistical methods enables researchers to predict genomic breeding values (GEBVs) more accurately. Recent studies suggested that the ability of methods in terms of predictive performance may depend on the genetic architecture of traits. Therefore, when choosing a statistical method, it is essential to consider the genetic architecture of the target traits. Herein, the performance of parametric methods i.e. GBLUP and BayesB and non-parametric methods i.e. Bagging GBLUP and Random Forest (RF) were compared for traits with different genetic architecture. Three scenarios of genetic architecture, including purely Additive (Add), purely Epistasis (Epis) and Additive-Dominance-Epistasis (ADE) were considered. To this end, an animal genome composed of five chromosomes, each chromosome harboring 1000 SNPs and four QTL was simulated. Predictive accuracies in the first generation of testing set under Additive genetic architectures for GBLUP, BayesB, Baging GBLUP and RF were 0.639, 0.731, 0.633 and 0.548, respectively, and were 0.278, 0.330, 0.275 and 0.444 under purely Epistatic genetic architectures. Corresponding values for the Additive-Dominance-Epistatic structure also were 0.375, 0.448, 0.369 and 0.458, respectively. The results showed that genetic architecture has a great impact on prediction accuracy of genomic evaluation methods. When genetic architecture was purely Additive, parametric methods and Bagging GBLUP were better than RF, whereas under Epistatic and Additive-Dominance-Epistatic genetic architectures, RF delivered better predictive performance than the other statistical methods.

The definition of heritability has been unique and clear, but its estimation and estimates vary across studies. Linear mixed model (LMM) and Haseman-Elston (HE) regression analyses are commonly used for estimating heritability from genome-wide association data. This study provides an analytical resolution that can be used to reconcile the differences between LMM and HE in the estimation of heritability given the genetic architecture, which is responsible for these differences. The genetic architecture was classified into three forms via thought experiments: (i) coupling genetic architecture that the quantitative trait loci (QTLs) in the linkage disequilibrium (LD) had a positive covariance; (ii) repulsion genetic architecture that the QTLs in the LD had a negative covariance; (iii) and neutral genetic architecture that the QTLs in the LD had a covariance with a summation of zero. The neutral genetic architecture is so far most embraced, whereas the coupling and the repulsion genetic architecture have not been well investigated. For a quantitative trait under the coupling genetic architecture, HE overestimated the heritability and LMM underestimated the heritability; under the repulsion genetic architecture, HE underestimated but LMM overestimated the heritability for a quantitative trait. These two methods gave identical results under the neutral genetic architecture. A general analytical result for the statistic estimated under HE is given regardless of genetic architecture. In contrast, the performance of LMM remained elusive, such as further depended on the ratio between the sample size and the number of markers, but LMM converged to HE with increased sample size.

Parametric and nonparametric methods have been developed for purposes of predicting phenotypes. These methods are based on retrospective analyses of empirical data consisting of genotypic and phenotypic scores. Recent reports have indicated that parametric methods are unable to predict phenotypes of traits with known epistatic genetic architectures. Herein, we review parametric methods including least squares regression, ridge regression, Bayesian ridge regression, least absolute shrinkage and selection operator (LASSO), Bayesian LASSO, best linear unbiased prediction (BLUP), Bayes A, Bayes B, Bayes C, and Bayes Cπ. We also review nonparametric methods including Nadaraya-Watson estimator, reproducing kernel Hilbert space, support vector machine regression, and neural networks. We assess the relative merits of these 14 methods in terms of accuracy and mean squared error (MSE) using simulated genetic architectures consisting of completely additive or two-way epistatic interactions in an F2 population derived from crosses of inbred lines. Each simulated genetic architecture explained either 30% or 70% of the phenotypic variability. The greatest impact on estimates of accuracy and MSE was due to genetic architecture. Parametric methods were unable to predict phenotypic values when the underlying genetic architecture was based entirely on epistasis. Parametric methods were slightly better than nonparametric methods for additive genetic architectures. Distinctions among parametric methods for additive genetic architectures were incremental. Heritability, i.e., proportion of phenotypic variability, had the second greatest impact on estimates of accuracy and MSE.

All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.

The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimationemployed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/

BackgroundDickeya dadantii and Pectobacterium atrosepticum are phytopathogenic enterobacteria capable of facultative anaerobic growth in a wide range of O2 concentrations found in plant and natural environments. The transcriptional response to O2 remains under-explored for these and other phytopathogenic enterobacteria although it has been well characterized for animal-associated genera including Escherichia coli and Salmonella enterica. Knowledge of the extent of conservation of the transcriptional response across orthologous genes in more distantly related species is useful to identify rates and patterns of regulon evolution. Evolutionary events such as loss and acquisition of genes by lateral transfer events along each evolutionary branch results in lineage-specific genes, some of which may have been subsequently incorporated into the O2-responsive stimulon. Here we present a comparison of transcriptional profiles measured using densely tiled oligonucleotide arrays for two phytopathogens, Dickeya dadantii 3937 and Pectobacterium atrosepticum SCRI1043, grown to mid-log phase in MOPS minimal medium (0.1% glucose) with and without O2.ResultsMore than 7% of the genes of each phytopathogen are differentially expressed with greater than 3-fold changes under anaerobic conditions. In addition to anaerobic metabolism genes, the O2 responsive stimulon includes a variety of virulence and pathogenicity-genes. Few of these genes overlap with orthologous genes in the anaerobic stimulon of E. coli. We define these as the conserved core, in which the transcriptional pattern as well as genetic architecture are well preserved. This conserved core includes previously described anaerobic metabolic pathways such as fermentation. Other components of the anaerobic stimulon show variation in genetic content, genome architecture and regulation. Notably formate metabolism, nitrate/nitrite metabolism, and fermentative butanediol production, differ between E. coli and the phytopathogens. Surprisingly, the overlap of the anaerobic stimulon between the phytopathogens is also relatively small considering that they are closely related, occupy similar niches and employ similar strategies to cause disease. There are cases of interesting divergences in the pattern of transcription of genes between Dickeya and Pectobacterium for virulence-associated subsystems including the type VI secretion system (T6SS), suggesting that fine-tuning of the stimulon impacts interaction with plants or competing microbes.ConclusionsThe small number of genes (an even smaller number if we consider operons) comprising the conserved core transcriptional response to O2 limitation demonstrates the extent of regulatory divergence prevalent in the Enterobacteriaceae. Our orthology-driven comparative transcriptomics approach indicates that the adaptive response in the eneterobacteria is a result of interaction of core (regulators) and lineage-specific (structural and regulatory) genes. Our subsystems based approach reveals that similar phenotypic outcomes are sometimes achieved by each organism using different genes and regulatory strategies.

Defining the Genetic, Genomic, Cellular, and Diagnostic Architectures of Psychiatric Disorders

Both schizophrenia (SCZ) and Alzheimer’s disease (AD) are highly heritable brain disorders. Despite of the observed comorbidity and shared psychosis and cognitive decline between the two disorders, the genetic risk architecture shared by SCZ and AD remains largely unknown. Based on summary statistics of the currently available largest genome-wide association studies for SCZ (n = 130,644) and AD (n = 455,258) in individuals of European ancestry, we conducted conditional/conjunctional false discovery rate (FDR) analysis to enhance the statistical power for discovering more genetic associations with SCZ or AD and to detect the common genetic variants shared by both disorders. We found shared genetic architecture in SCZ conditioned on AD and vice versa across different levels of significance, indicating polygenic overlap. We found 268 (78 novel) SCZ-only and 125 (55 novel) AD-only SNPs at conditional FDR < 0.01, and 16 lead SNPs shared by SCZ and AD at conjunctional FDR < 0.05. Only half of the shared SNPs showed concordant effect direction, which was consistent with the modest genetic correlation (r = 0.097; P = 0.026) between the two disorders. This study provides evidence for polygenic overlap between SCZ and AD, suggesting the existence of the shared molecular genetic mechanisms, which may inform therapeutic targets that are applicable for both disorders.

The gray short-tailed opossum (Monodelphis domestica) is an established laboratory-bred marsupial model for biomedical research. It is a critical species for comparative genomics research, providing the pivotal phylogenetic outgroup for studies of derived vs ancestral states of genomic/epigenomic characteristics for eutherian mammal lineages. To characterize the current genetic profile of this laboratory marsupial, we examined 79 individuals from eight established laboratory strains. Double digest restriction site-associated DNA sequencing and whole-genome resequencing experiments were performed to investigate the genetic architecture in these strains. A total of 66,640 high-quality single nucleotide polymorphisms (SNPs) were identified. We analyzed SNP density, average heterozygosity, nucleotide diversity, and population differentiation parameter Fst within and between the eight strains. Principal component and population structure analysis clearly resolve the strains at the level of their ancestral founder populations, and the genetic architecture of these strains correctly reflects their breeding history. We confirmed the successful establishment of the first inbred laboratory opossum strain LSD (inbreeding coefficient F > 0.99) and a nearly inbred strain FD2M1 (0.98 < F < 0.99), each derived from a different ancestral background. These strains are suitable for various experimental protocols requiring controlled genetic backgrounds and for intercrosses and backcrosses that can generate offspring with informative SNPs for studying a variety of genetic and epigenetic processes. Together with recent advances in reproductive manipulation and CRISPR/Cas9 techniques for Monodelphis domestica, the existence of distinctive inbred strains will enable genome editing on different genetic backgrounds, greatly expanding the utility of this marsupial model for biomedical research.

The relationship between genetic variation in the T-type calcium channel gene CACNA1H and childhood absence epilepsy is well established. The purpose of this study was to investigate the range of epilepsy syndromes for which CACNA1H variants may contribute to the genetic susceptibility architecture and determine the electrophysiological effects of these variants in relation to proposed mechanisms underlying seizures. Exons 3 to 35 of CACNA1H were screened for variants in 240 epilepsy patients (167 unrelated) and 95 control subjects by single-stranded conformation analysis followed by direct sequencing. Cascade testing of families was done by sequencing or single-stranded conformation analysis. Selected variants were introduced into the CACNA1H protein by site-directed mutagenesis. Constructs were transiently transfected into human embryo kidney cells, and electrophysiological data were acquired. More than 100 variants were detected, including 19 novel variants leading to amino acid changes in subjects with phenotypes including childhood absence, juvenile absence, juvenile myoclonic and myoclonic astatic epilepsies, as well as febrile seizures and temporal lobe epilepsy. Electrophysiological analysis of 11 variants showed that 9 altered channel properties, generally in ways that would be predicted to increase calcium current. Variants in CACNA1H that alter channel properties are present in patients with various generalized epilepsy syndromes. We propose that these variants contribute to an individual's susceptibility to epilepsy but are not sufficient to cause epilepsy on their own. The genetic architecture is dominated by rare functional variants; therefore, CACNA1H would not be easily identified as a susceptibility gene by a genome-wide case-control study seeking a statistical association.

BackgroundThe genetic architecture of a quantitative trait influences the phenotypic response to natural or artificial selection. One of the main objectives of genetic mapping studies is to identify the genetic factors underlying complex traits and understand how they contribute to phenotypic expression. Presently, we are good at identifying and locating individual loci with large effects, but there is a void in describing more complex genetic architectures. Although large networks of connected genes have been reported, there is an almost complete lack of information on how polymorphisms in these networks contribute to phenotypic variation and change. To date, most of our understanding comes from theoretical, model-based studies, and it remains difficult to assess how realistic their conclusions are as they lack empirical support.ResultsA previous study provided evidence that nearly half of the difference in eight-week body weight between two divergently selected lines of chickens was a result of four loci organized in a 'radial' network (one central locus interacting with three 'radial' loci that, in turn, only interacted with the central locus). Here, we study the relationship between phenotypic change and genetic polymorphism in this empirically detected network. We use a model-free approach to study, through individual-based simulations, the dynamic properties of this polymorphic and epistatic genetic architecture. The study provides new insights to how epistasis can modify the selection response, buffer and reveal effects of major loci leading to a progressive release of genetic variation. We also illustrate the difficulty of predicting genetic architecture from observed selection response, and discuss mechanisms that might lead to misleading conclusions on underlying genetic architectures from quantitative trait locus (QTL) experiments in selected populations.ConclusionConsidering both molecular (QTL) and phenotypic (selection response) data, as suggested in this work, provides additional insights into the genetic mechanisms involved in the response to selection. Such dissection of genetic architectures and in-depth studies of their ability to contribute to short- or long-term selection response represents an important step towards a better understanding of the genetic bases of complex traits and, consequently, of the evolutionary properties of populations.

Predicting complex phenotypes from genomic data is a fundamental aim of animal and plant breeding, where we wish to predict genetic merits of selection candidates; and of human genetics, where we wish to predict disease risk. While genomic prediction models work well with populations of related individuals and high linkage disequilibrium (LD) (e.g., livestock), comparable models perform poorly for populations of unrelated individuals and low LD (e.g., humans). We hypothesized that low prediction accuracies in the latter situation may occur when the genetics architecture of the trait departs from the infinitesimal and additive architecture assumed by most prediction models. We used simulated data for 10,000 lines based on sequence data from a population of unrelated, inbred Drosophila melanogaster lines to evaluate this hypothesis. We show that, even in very simplified scenarios meant as a stress test of the commonly used Genomic Best Linear Unbiased Predictor (G-BLUP) method, using all common variants yields low prediction accuracy regardless of the trait genetic architecture. However, prediction accuracy increases when predictions are informed by the genetic architecture inferred from mapping the top variants affecting main effects and interactions in the training data, provided there is sufficient power for mapping. When the true genetic architecture is largely or partially due to epistatic interactions, the additive model may not perform well, while models that account explicitly for interactions generally increase prediction accuracy. Our results indicate that accounting for genetic architecture can improve prediction accuracy for quantitative traits.

Coronary heart disease (CHD) and type 2 diabetes (T2D) are two complex diseases with complex interrelationships. However, the genetic architecture of the two diseases is often studied independently by the individual single-nucleotide polymorphism (SNP) approach. Here, we presented a genotypic-phenotypic framework for deciphering the genetic architecture underlying the disease patterns of CHD and T2D. A data-driven SNP-set approach was performed in a genome-wide association study consisting of subpopulations with different disease patterns of CHD and T2D (comorbidity, CHD without T2D, T2D without CHD and all none). We applied nonsmooth nonnegative matrix factorization (nsNMF) clustering to generate SNP sets interacting the information of SNP and subject. Relationships between SNP sets and phenotype sets harboring different disease patterns were then assessed, and we further co-clustered the SNP sets into a genetic network to topologically elucidate the genetic architecture composed of SNP sets. We identified 23 non-identical SNP sets with significant association with CHD or T2D (SNP-set based association test, P < 3.70 × [Formula: see text]). Among them, disease patterns involving CHD and T2D were related to distinct SNP sets (Hypergeometric test, P < 2.17 × [Formula: see text]). Accordingly, numerous genes (e.g., KLKs, GRM8, SHANK2) and pathways (e.g., fatty acid metabolism) were diversely implicated in different subtypes and related pathophysiological processes. Finally, we showed that the genetic architecture for disease patterns of CHD and T2D was composed of disjoint genetic networks (heterogeneity), with common genes contributing to it (pleiotropy). The SNP-set approach deciphered the complexity of both genotype and phenotype as well as their complex relationships. Different disease patterns of CHD and T2D share distinct genetic architectures, for which lipid metabolism related to fibrosis may be an atherogenic pathway that is specifically activated by diabetes. Our findings provide new insights for exploring new biological pathways.