Advances in endophenotyping schizophrenia

The search for the genetic architecture of schizophrenia has employed multiple, often converging strategies. One such strategy entails the use of tracing the heritability and neurobiology of endophenotypes. Endophenotypes are quantifiable traits not visible to the eye, which are thought to reflect an intermediate place on the path from genes to disorder. Endophenotype abnormalities in domains such as neurophysiology or neurocognition occur in schizophrenia patients as well as their clinically “unaffected” relatives, and reflect polymorphisms in the DNA of schizophrenia spectrum subjects which create vulnerability to developing schizophrenia. By identifying the single nucleotide polymorphisms (SNPs) associated with endophenotypes in schizophrenia, psychiatric neuroscientists can select new strong inference based molecular targets for the treatment of schizophrenia.     

Leveraging Distant Relatedness to Quantify Human Mutation and Gene-Conversion Rates

The rate at which human genomes mutate is a central biological parameter that has many implications for our ability to understand demographic and evolutionary phenomena. We present a method for inferring mutation and gene-conversion rates by using the number of sequence differences observed in identical-by-descent (IBD) segments together with a reconstructed model of recent population-size history. This approach is robust to, and can quantify, the presence of substantial genotyping error, as validated in coalescent simulations. We applied the method to 498 trio-phased sequenced Dutch individuals and inferred a point mutation rate of 1.66 × 10⁻⁸ per base per generation and a rate of 1.26 × 10⁻⁹ for <20 bp indels. By quantifying how estimates varied as a function of allele frequency, we inferred the probability that a site is involved in non-crossover gene conversion as 5.99 × 10⁻⁶. We found that recombination does not have observable mutagenic effects after gene conversion is accounted for and that local gene-conversion rates reflect recombination rates. We detected a strong enrichment of recent deleterious variation among mismatching variants found within IBD regions and observed summary statistics of local sharing of IBD segments to closely match previously proposed metrics of background selection; however, we found no significant effects of selection on our mutation-rate estimates. We detected no evidence of strong variation of mutation rates in a number of genomic annotations obtained from several recent studies. Our analysis suggests that a mutation-rate estimate higher than that reported by recent pedigree-based studies should be adopted in the context of DNA-based demographic reconstruction.     

Eliminating accidental deviations to minimize generalization error and maximize replicability: Applications in connectomics and genomics

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual’s samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error.     

Cell‐to‐cell and type‐to‐type heterogeneity of signaling networks: insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi‐signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time‐course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.     

Phylogenetic tree of vascular plants reveals the origins of aquatic angiosperms

Although aquatic plants are discussed as a unified biological group, they are phylogenetically well dispersed across the angiosperms. In this study, we annotated the aquatic taxa on the tree of vascular plants, and extracted the topology of these aquatic lineages to construct the tree of aquatic angiosperms. We also reconstructed the ancestral areas of aquatic families. We found that aquatic angiosperms could be divided into two different categories: the four aquatic orders and the aquatic taxa in terrestrial orders. Aquatic lineages evolved early in the radiation of angiosperms, both in the orders Nymphaeales and Ceratophyllales and among basal monocots (Acorales and Alismatales). These aquatic orders do not have any extant terrestrial relatives. They originated from aquatic habitats during the Early Cretaceous. Asia would have been one of the centers for early diversification of aquatic angiosperms. The aquatic families within terrestrial orders may originate from other areas besides Asia, such as America or Australia. The lineages leading to extant angiosperms diversified early in underexploited freshwater habitats. The four extant aquatic orders were relicts of an early radiation of angiosperm in aquatic environments. Their extinct ancestors might be aquatic early angiosperms.