Non-replication of functional connectivity differences in autism spectrum disorder across multiple sites and denoising strategies, bioRxiv, 2019-05-18
AbstractA rapidly growing number of studies on autism spectrum disorder (ASD) have used resting-state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there is therefore a pressing need to determine the primary factors influencing replicability. Here, we used resting-state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group-average functional connectomes and group-level differences (ASD vs. control) across 33 denoising pipelines and four independently-acquired datasets. The group-average connectomes were highly consistent across pipelines (r = 0.92±0.06) and sites (r = 0.88±0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76±0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07±0.04), suggesting lack of replication may be strongly influenced by site andor cohort differences. Across-site similarity remained low even when considering the data at a large-scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
biorxiv neuroscience 100-200-users 2019Assortative mating in hybrid zones is remarkably ineffective in promoting speciation, bioRxiv, 2019-05-17
AbstractAssortative mating and other forms of partial prezygotic isolation are often viewed as being more important than partial postzygotic isolation (low fitness of hybrids) early in the process of speciation. Here we simulate secondary contact between two populations (‘species’) to examine effects of pre- and postzygotic isolation in preventing blending. A small reduction in hybrid fitness (e.g., 10%) produces a narrower hybrid zone than a strong but imperfect mating preference (e.g., 10x stronger preference for conspecific over heterospecific mates). This is because, in the latter case, rare F1 hybrids find each other attractive (due to assortative mating), leading to the gradual buildup of a full continuum of intermediates between the two species. The cline is narrower than would result from purely neutral diffusion over the same number of generations, but this effect is due to the frequency-dependent mating disadvantage of individuals of rare mating types. Hybrids tend to pay this cost of rarity more than pure individuals, meaning there is an induced postzygotic isolation effect of assortative mating. When this induced mating disadvantage is removed, partial assortative mating does not prevent eventual blending of the species. These results prompt a questioning of the concept of partial prezygotic isolation, since it is not very isolating unless there is also postzygotic isolation.
biorxiv evolutionary-biology 0-100-users 2019Bayesian multivariate reanalysis of large genetic studies identifies many new associations, bioRxiv, 2019-05-17
AbstractGenome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is de-spite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.1Author SummaryGenome-wide association studies (GWAS) have become a common and powerful tool for identifying significant correlations between markers of genetic variation and physical traits of interest. Often these studies are conducted by comparing genetic variation against single traits one at a time (‘univariate’); however, it has previously been shown that it is possible to increase your power to detect significant associations by comparing genetic variation against multiple traits simultaneously (‘multivariate’). Despite this apparent increase in power though, researchers still rarely conduct multivariate GWAS, even when studies have multiple traits readily available. Here, we reanalyze 13 previously published GWAS using a multivariate method and find >400 additional associations. Our method makes use of univariate GWAS summary statistics and is available as a software package, thus making it accessible to other researchers interested in conducting the same analyses. We also show, using studies that have multiple releases, that our new associations have high rates of replication. Overall, we argue multivariate approaches in GWAS should no longer be overlooked and how, often, there is low-hanging fruit in the form of new associations by running these methods on data already collected.
biorxiv genomics 0-100-users 2019Evidence of large genetic influences on dog ownership in the Swedish Twin Registry has implications for understanding domestication and health associations, Scientific Reports, 2019-05-17
Dogs were the first domesticated animal and, according to the archaeological evidence, have had a close relationship with humans for at least 15,000 years. Today, dogs are common pets in our society and have been linked to increased well-being and improved health outcomes in their owners. A dog in the family during childhood is associated with ownership in adult life. The underlying factors behind this association could be related to experiences or to genetic influences. We aimed to investigate the heritability of dog ownership in a large twin sample including all twins in the Swedish Twin Registry born between 1926 and 1996 and alive in 2006. Information about dog ownership was available from 2001 to 2016 from national dog registers. The final data set included 85,542 twins from 50,507 twin pairs with known zygosity, where information on both twins were available in 35,035 pairs. Structural equation modeling was performed to estimate additive genetic effects (the heritability), commonshared environmental, and uniquenon-shared environmental effects. We found that additive genetic factors largely contributed to dog ownership, with heritability estimated at 57% for females and 51% for males. An effect of shared environmental factors was only observed in early adulthood. In conclusion, we show a strong genetic contribution to dog ownership in adulthood in a large twin study. We see two main implications of this finding (1) genetic variation may have contributed to our ability to domesticate dogs and other animals and (2) potential pleiotropic effects of genetic variation affecting dog ownership should be considered in studies examining health impacts of dog ownership.
scientific reports genetics 500+-users 2019OncoOmics approaches to reveal essential genes in breast cancer a panoramic view from pathogenesis to precision medicine, bioRxiv, 2019-05-17
SUMMARYBreast cancer (BC) is a heterogeneous disease where each OncoOmics approach needs to be fully understood as a part of a complex network. Therefore, the main objective of this study was to analyze genetic alterations, signaling pathways, protein-protein interaction networks, protein expression, dependency maps and enrichment maps in 230 previously prioritized genes by the Consensus Strategy, the Pan-Cancer Atlas, the Pharmacogenomics Knowledgebase and the Cancer Genome Interpreter, in order to reveal essential genes to accelerate the development of precision medicine in BC. The OncoOmics essential genes were rationally filtered to 144, 48 (33%) of which were hallmarks of cancer and 20 (14%) were significant in at least three OncoOmics approaches RAC1, AKT1 CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, PLCG1, GRB2, MED1, TOP2A, GATA3, BCL2, CTNNB1, EGFR and CDK2. According to the Open Targets Platform, there are 111 drugs that are currently being analyzed in 3151 clinical trials in 39 genes. Lastly, there are more than 800 clinical annotations associated with 94 genes in BC pharmacogenomics.
biorxiv genomics 0-100-users 2019Rare microbes from diverse Earth biomes dominate community activity, bioRxiv, 2019-05-17
AbstractMicrobes are the Earth’s most numerous organisms and are instrumental in driving major global biological and chemical processes. Microbial activity is a crucial component of all ecosystems, as microbes have the potential to control any major biochemical process. In recent years, considerable strides have been made in describing the community structure, i.e. diversity and abundance, of microbes from the Earth’s major biomes. In virtually all environments studied, a few highly abundant taxa dominate the structure of microbial communities. Still, microbial diversity is high and is concentrated in the less abundant, or rare, fractions of the community, i.e. the “long tail” of the abundance distribution. The relationship between microbial community structure and activity, specifically the role of rare microbes, and its connection to ecosystem function, is not fully understood. We analyzed 12.3 million metagenomic and metatranscriptomic sequence assemblies and their genes from environmental, human, and engineered microbiomes, and show that microbial activity is dominated by rare microbes (96% of total activity) across all measured biomes. Further, rare microbial activity was comprised of traits that are fundamental to ecosystem and organismal health, e.g. biogeochemical cycling and infectious disease. The activity of rare microbes was also tightly coupled to temperature, revealing a link between basic biological processes, e.g. reaction rates, and community activity. Our study provides a broadly applicable and predictable paradigm that implicates rare microbes as the main microbial drivers of ecosystem function and organismal health.
biorxiv microbiology 100-200-users 2019