Prediction of Complex Human Traits Using the Genomic Best Linear Unbiased Predictor

Despite important advances from Genome Wide Association Studies (GWAS), for most complex human traits and diseases, a sizable proportion of genetic variance remains unexplained and prediction accuracy (PA) is usually low. Evidence suggests that PA can be improved using Whole-Genome Regression (WGR) models where phenotypes are regressed on hundreds of thousands of variants simultaneously. The Genomic Best Linear Unbiased Prediction (G-BLUP, a ridge-regression type method) is a commonly used WGR method and has shown good predictive performance when applied to plant and animal breeding populations. However, breeding and human populations differ greatly in a number of factors that can affect the predictive performance of G-BLUP. Using theory, simulations, and real data analysis, we study the performance of G-BLUP when applied to data from related and unrelated human subjects. Under perfect linkage disequilibrium (LD) between markers and QTL, the prediction R-squared (R²) of G-BLUP reaches trait-heritability, asymptotically. However, under imperfect LD between markers and QTL, prediction R² based on G-BLUP has a much lower upper bound. We show that the minimum decrease in prediction accuracy caused by imperfect LD between markers and QTL is given by (1−b)², where b is the regression of marker-derived genomic relationships on those realized at causal loci. For pairs of related individuals, due to within-family disequilibrium, the patterns of realized genomic similarity are similar across the genome; therefore b is close to one inducing small decrease in R². However, with distantly related individuals b reaches very low values imposing a very low upper bound on prediction R². Our simulations suggest that for the analysis of data from unrelated individuals, the asymptotic upper bound on R² may be of the order of 20% of the trait heritability. We show how PA can be enhanced with use of variable selection or differential shrinkage of estimates of marker effects.     

The Crystal Structure of Plant ATG12 and its Biological Implication in Autophagy

Atg12 is a post-translational modifier that is activated and conjugated to its single target, Atg5, by a ubiquitin-like conjugation system. The Atg12-Atg5 conjugate is essential for autophagy, the bulk degradation process of cytoplasmic components by the vacuolar/lysosomal system. Here, we demonstrate that the Atg12 conjugation system exists in Arabidopsis and is essential for plant autophagy as well as in yeast and mammals. We also report the crystal structure of Arabidopsis thaliana (At) ATG12 at 1.8 Å resolution. Despite no obvious sequence homology with ubiquitin, the structure of AtATG12 shows a ubiquitin fold strikingly similar to those of mammalian homologs of Atg8, the other ubiquitin-like modifier essential for autophagy, which is conjugated to phosphatidylethanolamine. Two types of hydrophobic patches are present on the surface of AtATG12: one is conserved in both Atg12 and Atg8 orthologs, while the other is unique to Atg12 orthologs. Considering that they share Atg7 as an E1-like enzyme, we suggest that the first hydrophobic patch is responsible for the conjugation reaction, while the latter is involved in Atg12-specific functions.     

Widespread Dysregulation of Peptide Hormone Release in Mice Lacking Adaptor Protein AP-3

The regulated secretion of peptide hormones, neural peptides and many growth factors depends on their sorting into large dense core vesicles (LDCVs) capable of regulated exocytosis. LDCVs form at the trans-Golgi network, but the mechanisms that sort proteins to this regulated secretory pathway and the cytosolic machinery that produces LDCVs remain poorly understood. Recently, we used an RNAi screen to identify a role for heterotetrameric adaptor protein AP-3 in regulated secretion and in particular, LDCV formation. Indeed, mocha mice lacking AP-3 have a severe neurological and behavioral phenotype, but this has been attributed to a role for AP-3 in the endolysosomal rather than biosynthetic pathway. We therefore used mocha mice to determine whether loss of AP-3 also dysregulates peptide release in vivo. We find that adrenal chromaffin cells from mocha animals show increased constitutive exocytosis of both soluble cargo and LDCV membrane proteins, reducing the response to stimulation. We also observe increased basal release of both insulin and glucagon from pancreatic islet cells of mocha mice, suggesting a global disturbance in the release of peptide hormones. AP-3 exists as both ubiquitous and neuronal isoforms, but the analysis of mice lacking each of these isoforms individually and together shows that loss of both is required to reproduce the effect of the mocha mutation on the regulated pathway. In addition, we show that loss of the related adaptor protein AP-1 has a similar effect on regulated secretion but exacerbates the effect of AP-3 RNAi, suggesting distinct roles for the two adaptors in the regulated secretory pathway.     

Structures of Atg7-Atg3 and Atg7-Atg10 reveal noncanonical mechanisms of E2 recruitment by the autophagy E1

Central to most forms of autophagy are two ubiquitin-like proteins (UBLs), Atg8 and Atg12, which play important roles in autophagosome biogenesis, substrate recruitment to autophagosomes, and other aspects of autophagy. Typically, UBLs are activated by an E1 enzyme that (1) catalyzes adenylation of the UBL C terminus, (2) transiently covalently captures the UBL through a reactive thioester bond between the E1 active site cysteine and the UBL C terminus, and (3) promotes transfer of the UBL C terminus to the catalytic cysteine of an E2 conjugating enzyme. The E2, and often an E3 ligase enzyme, catalyzes attachment of the UBL C terminus to a primary amine group on a substrate. Here, we summarize our recent work reporting the structural and mechanistic basis for E1-E2 protein interactions in autophagy.     

Identification of methylated flavonoid regioisomeric metabolites using enzymatic semisynthesis and liquid chromatography-tandem mass spectrometry

Discrimination of isomeric methylated metabolites is an important step toward identifying genes responsible for methylation, but presents substantial challenges because authentic standards are often unavailable and mass spectra of isomers have been considered indistinguishable. In this report, an approach is described for identifying methyl group positions in multiply methylated flavonoid metabolites using combinations of tandem mass spectrometry, liquid chromatography retention, and site-selective methylation by recombinant O-methyltransferases from Solanum habrochaites LA1777. The basis for observed fragment ions in tandem mass spectra of multiply methylated myricetin was further established using enzymatic incorporation of deuterium-labeled methyl groups using S-adenosylmethionine-d ₃ as precursor.     

Multi-species SIR models from a dynamical Bayesian perspective

Multi-species compartment epidemic models, such as the multi-species susceptible–infectious–recovered (SIR) model, are extensions of the classic SIR models, which are used to explore the transient dynamics of pathogens that infect multiple hosts in a large population. In this article, we propose a dynamical Bayesian hierarchical SIR (HSIR) model, to capture the stochastic or random nature of an epidemic process in a multi-species SIR (with recovered becoming susceptible again) dynamical setting, under hidden mass balance constraints. We call this a Bayesian hierarchical multi-species SIR (MSIR_B) model. Different from a classic multi-species SIR model (which we call MSIR_C), our approach imposes mass balance on the underlying true counts rather than, improperly, on the noisy observations. Moreover, the MSIR_B model can capture the discrete nature of, as well as uncertainties in, the epidemic process.     

Evidence That Mast Cells Are Not Required for Healing of Splinted Cutaneous Excisional Wounds in Mice

Wound healing is a complex biological process involving the interaction of many cell types to replace lost or damaged tissue. Although the biology of wound healing has been extensively investigated, few studies have focused on the role of mast cells. In this study, we investigated the possible role of mast cells in wound healing by analyzing aspects of cutaneous excisional wound healing in three types of genetically mast cell-deficient mice. We found that C57BL/6-Kit^W-sh/W-sh, WBB6F₁-Kit^W/W-v, and Cpa3-Cre; Mcl-1^fl/fl mice re-epithelialized splinted excisional skin wounds at rates very similar to those in the corresponding wild type or control mice. Furthermore, at the time of closure, scars were similar in the genetically mast cell-deficient mice and the corresponding wild type or control mice in both quantity of collagen deposition and maturity of collagen fibers, as evaluated by Masson’s Trichrome and Picro-Sirius red staining. These data indicate that mast cells do not play a significant non-redundant role in these features of the healing of splinted full thickness excisional cutaneous wounds in mice.     

Defensive Medicine among Obstetricians and Gynecologists in Tertiary Hospitals

Objective

To describe the daily work practice under the threat of defensive medicine among obstetricians and gynecologists.

Study Design

A prospective cross-sectional survey of obstetricians and gynecologists working at tertiary medical centers in Israel.

Results

Among the 117 obstetricians and gynecologists who participated in the survey, representing 10% of the obstetricians and gynecologists registered by the Israel Medical Association, 113 (97%) felt that their daily work practice is influenced by concern about being sued for medical negligence and not only by genuine medical considerations. As a result, 102 (87%) physicians are more likely to offer the cesarean section option, even in the absence of a clear medical indication, 70 (60%) follow court rulings concerning medical practices, and 85 (73%) physicians mentioned that discussions about medical negligence court rulings are included in their departments'' meetings.

Conclusions

Defensive medicine is a well-embedded phenomenon affecting the medical decision process of obstetricians and gynecologists.     

Neural Sensitivity to Absolute and Relative Anticipated Reward in Adolescents

Adolescence is associated with a dramatic increase in risky and impulsive behaviors that have been attributed to developmental differences in neural processing of rewards. In the present study, we sought to identify age differences in anticipation of absolute and relative rewards. To do so, we modified a commonly used monetary incentive delay (MID) task in order to examine brain activity to relative anticipated reward value (neural sensitivity to the value of a reward as a function of other available rewards). This design also made it possible to examine developmental differences in brain activation to absolute anticipated reward magnitude (the degree to which neural activity increases with increasing reward magnitude). While undergoing fMRI, 18 adolescents and 18 adult participants were presented with cues associated with different reward magnitudes. After the cue, participants responded to a target to win money on that trial. Presentation of cues was blocked such that two reward cues associated with $.20, $1.00, or $5.00 were in play on a given block. Thus, the relative value of the $1.00 reward varied depending on whether it was paired with a smaller or larger reward. Reflecting age differences in neural responses to relative anticipated reward (i.e., reference dependent processing), adults, but not adolescents, demonstrated greater activity to a $1 reward when it was the larger of the two available rewards. Adults also demonstrated a more linear increase in ventral striatal activity as a function of increasing absolute reward magnitude compared to adolescents. Additionally, reduced ventral striatal sensitivity to absolute anticipated reward (i.e., the difference in activity to medium versus small rewards) correlated with higher levels of trait Impulsivity. Thus, ventral striatal activity in anticipation of absolute and relative rewards develops with age. Absolute reward processing is also linked to individual differences in Impulsivity.     

A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.