Heritable Phenotypes Research Articles

Association of keel bone morphometry with keel bone damage and skeletal quality in the laying hen

ABSTRACT 1. The aim of this work was to identify a heritable keel bone phenotype with a correlation to keel bone damage and/or skeletal bone strength that could be easily measured in the living hen to aid selection to prevent welfare issues. 2. The scoring system used reflected the observed damage, keel bone dimensions and shape compared to traditional measurements of bone quality. 3. Increased keel bone damage was associated with poor humerus and tibia breaking strengths (p < 0.01). Bone damage was associated with higher whole keel density (p < 0.01) due to the effect of callus formation. 4. Keel bone depth and area was moderately heritable at indices of 0.32 and 0.40, respectively. Keel bone depth was genetically correlated with tibia (0.36) and humerus density (0.68) and keel bone area was correlated with humerus density (0.59). Deeper keel bones and those with larger areas had small, negative phenotypic correlations with keel bone damage (−0.07 and −0.11, respectively). The second principal component of keel bone shape represented the caudal section of the keel bone and cranial edge concavity. The third principal component represented the differences in the caudal tip of the keel bone, the concavity of the dorsal edge and convexity of the ventral edge. Heritability estimates were 0.44 and 0.39, respectively. 5. The results suggested that genetics contribute to morphometric traits. Hens with poorer skeletal quality are likely to accumulate more damage. Some of the traits may be a predictor of damage, although mid keel depth or concavity may simply reflect the effect of damage or deviation.

A "poly-matter network" conception of biological inheritance.

Here we intend to shift the "DNA- and information-centric" conception of biological inheritance, with the accompanying exclusion of any non-DNA matter, to a "poly-matter network" framework which, in addition to DNA, considers the action of other cellular membranous constituents. These cellular structures, in particular organelles and plasma membranes, express "landscapes" of specific topologies at their surfaces, which may become altered in response to certain environmental factors. These so-called "membranous environmental landscapes" (MELs), which replicate by self-organization / autopoiesis rather than self-assembly, are transferred from donor to acceptor cells by various - vesicular and non-vesicular - mechanisms and exert novel features in the acceptor cells. The "DNA-centric" conception may be certainly explanatorily sufficient for the transfer of heritable phenotype variation to acceptor cells following the copying of DNA in donor cells and thereby for the phenomenon of biological inheritance of traits. However, it is not causally sufficient. With the observation of phenotype variation, as initially manifested during bacterial transformation, the impact of environmental factors, such as nutrition and stress, in the differential regulation of gene expression has been widely accepted and resulted in intense efforts to resolve the underlying epigenetic mechanisms. However, these are explained under a conceptual frame where the DNA (and associated proteins) are the only matter of inheritance. In contrast, it is our argumentation that inheritance can only be adequately understood as the transfer of DNA in concert with non-DNA matter in a "poly-matter network" conception. The adequate inclusion of the transfer of non-DNA matter is still a desideratum of future genetic research, which may pave the way for the experimental elucidation not only of how DNA and membrane matter act in concert to enable the inheritance of innate traits, but also whether they interact for that of acquired biological traits. Moreover, the "poly-matter network" conception may open new perspectives for an understanding of the pathogenesis of "common complex" diseases.

Defining heritability, plasticity, and transition dynamics of cellular phenotypes in somatic evolution.

Single-cell sequencing has characterized cell state heterogeneity across diverse healthy and malignant tissues. However, the plasticity or heritability of these cell states remains largely unknown. To address this, we introduce PATH (phylogenetic analysis of trait heritability), a framework to quantify cell state heritability versus plasticity and infer cell state transition and proliferation dynamics from single-cell lineage tracing data. Applying PATH to a mouse model of pancreatic cancer, we observed heritability at the ends of the epithelial-to-mesenchymal transition spectrum, with higher plasticity at more intermediate states. In primary glioblastoma, we identified bidirectional transitions between stem- and mesenchymal-like cells, which use the astrocyte-like state as an intermediary. Finally, we reconstructed a phylogeny from single-cell whole-genome sequencing in B cell acute lymphoblastic leukemia and delineated the heritability of B cell differentiation states linked with genetic drivers. Altogether, PATH replaces qualitative conceptions of plasticity with quantitative measures, offering a framework to study somatic evolution.

Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes.

Most phenotype-associated genetic variants map to non-coding regulatory regions of the human genome. Moreover, variants associated with blood cell phenotypes are enriched in regulatory regions active during hematopoiesis. To systematically explore the nature of these regions, we developed a highly efficient strategy, Perturb-multiome, that makes it possible to simultaneously profile both chromatin accessibility and gene expression in single cells with CRISPR-mediated perturbation of a range of master transcription factors (TFs). This approach allowed us to examine the connection between TFs, accessible regions, and gene expression across the genome throughout hematopoietic differentiation. We discovered that variants within the TF-sensitive accessible chromatin regions, while representing less than 0.3% of the genome, show a ~100-fold enrichment in heritability across certain blood cell phenotypes; this enrichment is strikingly higher than for other accessible chromatin regions. Our approach facilitates large-scale mechanistic understanding of phenotype-associated genetic variants by connecting key cis-regulatory elements and their target genes within gene regulatory networks.

The fecal microbiota of Holstein cows is heritable and genetically correlated to dairy performances.

The fecal microbiota of ruminants constitutes a diversified community that has been phenotypically associated with a variety of host phenotypes, such as production and health. To gain a better understanding of the complex and interconnected factors that drive the fecal bacterial community, we have aimed to estimate the genetic parameters of the diversity and composition of the fecal microbiota, including heritabilities, genetic correlations among taxa, and genetic correlations between fecal microbiota features and host phenotypes. To achieve this, we analyzed a large population of 1,875 Holstein cows originating from 144 French commercial herds and routinely recorded for production, somatic cell score, and fertility traits. Fecal samples were collected from the animals and subjected to 16S rRNA gene sequencing, with reads classified into Amplicon Sequence Variants (ASVs). The estimated α- and β-diversity indices (i.e., Observed Richness, Shannon index, Bray-Curtis and Jaccard dissimilarity matrices) and the abundances of ASVs, genera, families and phyla, normalized by centered-log ratio (CLR), were considered as phenotypes. Genetic parameters were calculated using either univariate or bivariate animal models. Heritabilities estimates, ranging from 0.08 to 0.31 for taxa abundances and β-diversity indices, highlight the influence of the host genetics on the composition of the fecal microbiota. Furthermore, genetic correlations estimated within the microbial community and between microbiota features and host traits reveal the complex networks linking all components of the fecal microbiota together and to their host, thus strengthening the holobiont concept. By estimating the heritabilities of microbiota-associated phenotypes, our study quantifies the impact of the host genetics on the fecal microbiota composition. In addition, genetic correlations between taxonomic groups and between taxa abundances and host performance suggest potential applications for selective breeding to improve host traits or promote a healthier microbiota.

Scalable summary-statistics-based heritability estimation method with individual genotype level accuracy.

SNP heritability, the proportion of phenotypic variation explained by genotyped SNPs, is an important parameter in understanding the genetic architecture underlying various diseases and traits. Methods that aim to estimate SNP heritability from individual genotype and phenotype data are limited by their ability to scale to Biobank-scale data sets and by the restrictions in access to individual-level data. These limitations have motivated the development of methods that only require summary statistics. Although the availability of publicly accessible summary statistics makes them widely applicable, these methods lack the accuracy of methods that utilize individual genotypes. Here we present a SUMmary-statistics-based Randomized Haseman-Elston regression (SUM-RHE), a method that can estimate the SNP heritability of complex phenotypes with accuracies comparable to approaches that require individual genotypes, while exclusively relying on summary statistics. SUM-RHE employs Genome-Wide Association Study (GWAS) summary statistics and statistics obtained on a reference population, which can be efficiently estimated and readily shared for public use. Our results demonstrate that SUM-RHE obtains estimates of SNP heritability that are substantially more accurate compared with other summary statistic methods and on par with methods that rely on individual-level data.

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (&amp;gt; 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals' cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Recent advances in epigenomic techniques: Analysis of DNA base modifications

Epigenetics refers to the study of modifications in DNA bases, histone proteins and/or alterations in non-coding-RNA biogenesis that cause changes in gene expression without affecting the underlying nucleotide sequence. Epigenetic variation is emerging as one of the regulators of developmental processes and stress responses in animals and plants. Identification and interrogation of epigenetic changes provide unprecedented opportunities for resolving some of the enigmas that could not be solved based on genetic principles. Moreover, epigenome editing might become a preferred technique for the manipulation of gene expression in the desired organism. Although epigenetics is still in its infancy, the need of the day is to identify epigenetic marks and associate them with heritable phenotypes. Several techniques have been developed to analyze the epigenetic changes and associate them with molecular/physiological processes. It is necessary to examine them in a site- and cell-specific manner. Though considerable progress has been made in the last decade towards mapping DNA modifications as well as other epigenetic changes, there is a need to improve the specificity, sensitivity, and resolving power of the techniques to conduct epigenetic research in a cell-specific manner. The recent technological advancements are currently driving the field of epigenetics, and facilitating a better understanding of the field of functional genomics. The present review outlines the recent advances in epigenomic tools and techniques and presents the future perspectives of epigenomic studies.

The RNA-DNA world and the emergence of DNA-encoded heritable traits

ABSTRACT The RNA world hypothesis confers a central role to RNA molecules in information encoding and catalysis. Even though evidence in support of this hypothesis has accumulated from both experiments and computational modelling, the transition from an RNA world to a world where heritable genetic information is encoded in DNA remains an open question. Recent experiments show that both RNA and DNA templates can extend complementary primers using free RNA/DNA nucleotides, either non-enzymatically or in the presence of a replicase ribozyme. Guided by these experiments, we analyse protocellular evolution with an expanded set of reaction pathways made possible through the presence of DNA nucleotides. By encapsulating these reactions inside three different types of protocellular compartments, each subject to distinct modes of selection, we show how protocells containing DNA-encoded replicases in low copy numbers and replicases in high copy numbers can dominate the population. This is facilitated by a reaction that leads to auto-catalytic synthesis of replicase ribozymes from DNA templates encoding the replicase after the chance emergence of a replicase through non-enzymatic reactions. Our work unveils a pathway for the transition from an RNA world to a mixed RNA-DNA world characterized by Darwinian evolution, where DNA sequences encode heritable phenotypes.

Advancements in genetic techniques and functional genomics for enhancing crop traits and agricultural sustainability.

Genetic variability is essential for the development of new crop varieties with economically beneficial traits. The traits can be inherited from wild relatives or induced through mutagenesis. Novel genetic elements can then be identified and new gene functions can be predicted. In this study, forward and reverse genetics approaches were described, in addition to their applications in modern crop improvement programs and functional genomics. By using heritable phenotypes and linked genetic markers, forward genetics searches for genes by using traditional genetic mapping and allele frequency estimation. Despite recent advances in sequencing technology, omics and computation, genetic redundancy remains a major challenge in forward genetics. By analyzing close-related genes, we will be able to dissect their functional redundancy and predict possible traits and gene activity patterns. In addition to these predictions, sophisticated reverse gene editing tools can be used to verify them, including TILLING, targeted insertional mutagenesis, gene silencing, gene targeting and genome editing. By using gene knock-down, knock-up and knock-out strategies, these tools are able to detect genetic changes in cells. In addition, epigenome analysis and editing enable the development of novel traits in existing crop cultivars without affecting their genetic makeup by increasing epiallelic variants. Our understanding of gene functions and molecular dynamics of various biological phenomena has been revised by all of these findings. The study also identifies novel genetic targets in crop species to improve yields and stress tolerances through conventional and non-conventional methods. In this article, genetic techniques and functional genomics are specifically discussed and assessed for their potential in crop improvement.

Interindividual differences of dietary fat-inducible Mest in white adipose tissue of C57BL/6J mice are not heritable.

Differences in white adipose tissue (WAT) expression of mesoderm-specific transcript (Mest) in C57BL6/J mice fed a high-fat diet (HFD) are concomitant with and predictive for the development of obesity. However, the basis for differences in WAT Mest among mice is unknown. This study investigated whether HFD-inducible WAT Mest, as well as susceptibility to obesity, is transmissible from parents to offspring. WAT biopsies of mice fed an HFD for 2 weeks identified parents with low and high WAT Mest for breeding. Obesity phenotypes, WAT Mest, hepatic gene expression, and serum metabolites were determined in offspring fed an HFD for 2 weeks. Offspring showed no heritability of obesity or WAT Mest phenotypes from parents but did show hepatic and serum metabolite changes consistent with their WAT Mest. Importantly, retired male breeders showed WAT Mest expression congruent with initial WAT biopsies even though HFD exposure occurred early in life. Disparity of HFD-induced Mest in mice is not heritable but, rather, is reestablished during each generation and remains fixed from an early age to adulthood. Short-term HFD feeding reveals variation of WAT Mest expression within isogenic mice that is positively associated with the development of obesity.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants’ T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes (UDIPs). GWAS of these UDIPs in held-out UKBB subjects (n = 22,880 discovery and n = 12,359/11,265 replication cohorts for T1/T2) identified 9457 significant SNPs organized into 97 independent genetic loci of which 60 loci were replicated. Twenty-six loci were not reported in earlier T1 and T2 IDP-based UK Biobank GWAS. We developed a perturbation-based decoder interpretation approach to show that these loci are associated with UDIPs mapped to multiple relevant brain regions. Our results established unsupervised deep learning can derive robust, unbiased, heritable, and interpretable brain imaging phenotypes.

Genetic underpinnings of arthropod community distributions in Populus trichocarpa.

Community genetics seeks to understand the mechanisms by which natural genetic variation in heritable host phenotypes can encompass assemblages of organisms such as bacteria, fungi, and many animals including arthropods. Prior studies that focused on plant genotypes have been unable to identify genes controlling community composition, a necessary step to predict ecosystem structure and function as underlying genes shift within plant populations. We surveyed arthropods within an association population of Populus trichocarpa in three common gardens to discover plant genes that contributed to arthropod community composition. We analyzed our surveys with traditional single-trait genome-wide association analysis (GWAS), multitrait GWAS, and functional networks built from a diverse set of plant phenotypes. Plant genotype was influential in structuring arthropod community composition among several garden sites. Candidate genes important for higher level organization of arthropod communities had broadly applicable functions, such as terpenoid biosynthesis and production of dsRNA binding proteins and protein kinases, which may be capable of targeting multiple arthropod species. We have demonstrated the ability to detect, in an uncontrolled environment, individual genes that are associated with the community assemblage of arthropods on a host plant, further enhancing our understanding of genetic mechanisms that impact ecosystem structure.

Estimating disease heritability from complex pedigrees allowing for ascertainment and covariates

We propose TetraHer, a method for estimating the liability heritability of binary phenotypes. TetraHer has five key features. First, it can be applied to data from complex pedigrees that contain multiple types of relationships. Second, it can correct for ascertainment of cases or controls. Third, it can accommodate covariates. Fourth, it can model the contribution of common environment. Fifth, it produces a likelihood that can be used for significance testing. We first demonstrate the validity of TetraHer on simulated data. We then use TetraHer to estimate liability heritability for 229 codes from the tenth International Classification of Diseases (ICD-10). We identify 107 codes with significant heritability (p<0.05/229), which can be used in future analyses for investigating the genetic architecture of human diseases.

Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study.

Integrating and analyzing multiple omics data sets, including genomics, proteomics and radiomics, can significantly advance researchers' comprehensive understanding of Alzheimer's disease (AD). However, current methodologies primarily focus on the main effects of genetic variation and protein, overlooking non-additive effects such as genotype-protein interaction (GPI) and correlation patterns in brain imaging genetics studies. Importantly, these non-additive effects could contribute to intermediate imaging phenotypes, finally leading to disease occurrence. In general, the interaction between genetic variations and proteins, and their correlations are two distinct biological effects, and thus disentangling the two effects for heritable imaging phenotypes is of great interest and need. Unfortunately, this issue has been largely unexploited. In this paper, to fill this gap, we propose $\textbf{M}$ulti-$\textbf{T}$ask $\textbf{G}$enotype-$\textbf{P}$rotein $\textbf{I}$nteraction and $\textbf{C}$orrelation disentangling method ($\textbf{MT-GPIC}$) to identify GPI and extract correlation patterns between them. To ensure stability and interpretability, we use novel and off-the-shelf penalties to identify meaningful genetic risk factors, as well as exploit the interconnectedness of different brain regions. Additionally, since computing GPI poses a high computational burden, we develop a fast optimization strategy for solving MT-GPIC, which is guaranteed to converge. Experimental results on the Alzheimer's Disease Neuroimaging Initiative data set show that MT-GPIC achieves higher correlation coefficients and classification accuracy than state-of-the-art methods. Moreover, our approach could effectively identify interpretable phenotype-related GPI and correlation patterns in high-dimensional omics data sets. These findings not only enhance the diagnostic accuracy but also contribute valuable insights into the underlying pathogenic mechanisms of AD.

Strain-Specific Targeting and Destruction of Cells by Prions.

Prion diseases are caused by the disease-specific self-templating infectious conformation of the host-encoded prion protein, PrPSc. Prion strains are operationally defined as a heritable phenotype of disease under controlled conditions. One of the hallmark phenotypes of prion strain diversity is tropism within and between tissues. A defining feature of prion strains is the regional distribution of PrPSc in the CNS. Additionally, in both natural and experimental prion disease, stark differences in the tropism of prions in secondary lymphoreticular system tissues occur. The mechanism underlying prion tropism is unknown; however, several possible hypotheses have been proposed. Clinical target areas are prion strain-specific populations of neurons within the CNS that are susceptible to neurodegeneration following the replication of prions past a toxic threshold. Alternatively, the switch from a replicative to toxic form of PrPSc may drive prion tropism. The normal form of the prion protein, PrPC, is required for prion formation. More recent evidence suggests that it can mediate prion and prion-like disease neurodegeneration. In vitro systems for prion formation have indicated that cellular cofactors contribute to prion formation. Since these cofactors can be strain specific, this has led to the hypothesis that the distribution of prion formation cofactors can influence prion tropism. Overall, there is evidence to support several mechanisms of prion strain tropism; however, a unified theory has yet to emerge.

Tracking the frequency of phytoplankton clonal lineages using multispectral image flow cytometry and neural networks

Abstract Testing fundamental theories of diversity and evolutionary change often requires tracking the frequencies of clonal lineages within a population over time. Current tracking methods in plankton and microbial systems are often labour‐intensive, time‐consuming, expensive and/or unavailable, especially when high temporal resolution of frequencies is required. The combination of multispectral imaging flow cytometry and neural networks (NN) could provide an efficient approach to classify clonal lineages by their heritable phenotypes and thus track their frequency changes. Here, we present a novel method that combines NN and feature values extracted from images to classify six clonal lineages of the green alga Chlamydomonas reinhardtii based on heritable morphological differences when paired in‐silico and in‐vitro. We compared different NN trained on all six clonal lineages or on pairs of two and compared the accuracy of the models. All NN were able to classify clonal lineages with very high accuracy and the NN trained on pairs of clonal lineages achieved the highest accuracy of 97%. Using in‐vitro samples we observed a drop in accuracy to an average of 85%, which was due to the variation occurring between image acquisitions and culturing conditions causing differences in the extracted feature values. This method can be applied to reduce the workload and running costs of long‐term lineage tracking, and it can be applied to a wide range of plankton and microbial organisms and research questions.

Parent-offspring resemblance for educational attainment reduces with increased social class in a global sample: evidence for the compensatory advantage hypothesis.

The degree to which (self-reported) social class predicts parent-offspring resemblance for educational attainment (EA) is examined in a globally representative dataset of 69,116 individuals sourced from 56 countries. A hierarchical general linear model is used to predict participant EA with the two-way interaction between class and parental EA, after controlling for regional effects, the main effects of age, class, parental EA, and interactions among these. Social class-by-parental EA interaction negatively predicts participant EA (semipartial r = -0.04, 95% CI = -0.05 to -0.03), meaning that among those who report belonging to a "higher" social class, the degree of parent-offspring resemblance for EA is reduced, contrary to the Scarr-Rowe hypothesis, which holds that genetic influences on cognitive ability and related phenotypes (captured here in part by parent-offspring resemblance) should be greater among those from higher socioeconomic status (SES) backgrounds. These results replicate using a quantile regression model, where it was found that among those with lower social class ordinals, the strength of the parent-participant EA association is significantly stronger relative to those in the highest ordinal. No significant sex differences are present. These findings are consistent with the compensatory advantage hypothesis, which predicts decreased heritability of EA and related phenotypes among affluent families, as increased access to educational resources should enhance opportunities for cognitive growth in a way that compensates for intrinsic disadvantages.

Menin Reduces Parvalbumin Expression and is Required for the Anti-Depressant Function of Ketamine.

Dysfunction of parvalbumin (PV) neurons is closely involved in depression, however, the detailed mechanism remains unclear. Based on the previous finding that multiple endocrine neoplasia type 1 (Protein: Menin; Gene: Men1) mutation (G503D) is associated with a higher risk of depression, a Menin-G503D mouse model is generated that exhibits heritable depressive-like phenotypes and increases PV expression in brain. This study generates and screens a serial of neuronal specific Men1 deletion mice, and found that PV interneuron Men1 deletion mice (PcKO) exhibit increased cortical PV levels and depressive-like behaviors. Restoration of Menin, knockdown PV expression or inhibition of PV neuronal activity in PV neurons all can ameliorate the depressive-like behaviors of PcKO mice. This study next found that ketamine stabilizes Menin by inhibiting protein kinase A (PKA) activity, which mediates the anti-depressant function of ketamine. These results demonstrate a critical role for Menin in depression, and prove that Menin is key to the antidepressant function of ketamine.