Project description:BackgroundYunnan is located in Southwest China and consists of great cultural, linguistic, and genetic diversity. However, the genomic diversity of ethnic minorities in Yunnan is largely under-investigated. To gain insights into population history and local adaptation of Yunnan minorities, we analyzed 242 whole-exome sequencing data with high coverage (~ 100-150 ×) of Yunnan minorities representing Achang, Jingpo, Dai, and Deang, who were linguistically assumed to be derived from three ancient lineages (the tri-genealogy hypothesis), i.e., Di-Qiang, Bai-Yue, and Bai-Pu.ResultsYunnan minorities show considerable genetic differences. Di-Qiang populations likely migrated from the Tibetan area about 6700 years ago. Genetic divergence between Bai-Yue and Di-Qiang was estimated to be 7000 years, and that between Bai-Yue and Bai-Pu was estimated to be 5500 years. Bai-Pu is relatively isolated, but gene flow from surrounding Di-Qiang and Bai-Yue populations was also found. Furthermore, we identified genetic variants that are differentiated within Yunnan minorities possibly due to the living circumstances and habits. Notably, we found that adaptive variants related to malaria and glucose metabolism suggest the adaptation to thalassemia and G6PD deficiency resulting from malaria resistance in the Dai population.ConclusionsWe provided genetic evidence of the tri-genealogy hypothesis as well as new insights into the genetic history and local adaptation of the Yunnan minorities.
Project description:Cerebral cavernous malformations (CCM) are vascular malformations consisting of collections of enlarged capillaries occurring in the brain or spinal cord. These vascular malformations can occur sporadically or susceptibility to develop these can be inherited as an autosomal dominant trait due to mutation in one of three genes. Over a decade ago, we described a 77.6 Kb germline deletion spanning exons 2-10 in the CCM2 gene found in multiple affected individuals from seemingly unrelated families. Segregation analysis using linked, microsatellite markers indicated that this deletion may have arisen at least twice independently. In the ensuing decades, many more CCM patients have been identified with this deletion. In this present study we examined 27 reportedly unrelated affected individuals with this deletion. To investigate the origin of the deletion at base pair level resolution, we sequenced approximately 10 Kb upstream and downstream from the recombination junction on the deleted allele. All patients showed the identical SNP haplotype across this combined 20 Kb interval. In parallel, genealogical records have traced 11 of these individuals to five separate pedigrees dating as far back as the 1600-1700s. These haplotype and genealogical data suggest that these families and the remaining "unrelated" samples converge on a common ancestor due to a founder mutation occurring centuries ago on the North American continent. We also note that another gene, NACAD, is included in this deletion. Although patient self-reporting does not indicate an apparent phenotypic consequence for heterozygous deletion of NACAD, further investigation is warranted for these patients.
Project description:In 2022, the National Technology Validation and Implementation Collaborative (NTVIC) was established. Its mission is to collaborate across the US on validation, method development, and implementation. The NTVIC is comprised of 13 federal, state and local government crime laboratory leaders, joined by university researchers, and private technology and research companies. One of the NTVIC's first initiatives was to generate this draft policy document. This document provides guidelines and considerations for crime laboratories and investigative agencies exploring the establishment of a forensic investigative genetic genealogy (FIGG) program. While each jurisdiction is responsible for its own program policy, sharing minimum standards and best practices to optimize resources, promote technology implementation and elevate quality is a goal of the NTVIC.
Project description:Downy mildew caused by Plasmopara viticola is one of the most devastating diseases of grapevines worldwide. So far, the genetic diversity and origin of the Chinese P. viticola population are unclear. In the present study, 103 P. viticola isolates were sequenced at four gene regions: internal transcribed spacer one (ITS), large subunit of ribosomal RNA (LSU), actin gene (ACT) and beta-tubulin (TUB). The sequences were analyzed to obtain polymorphism and diversity information of the Chinese population as well as to infer the relationships between Chinese and American isolates. High genetic diversity was observed for the Chinese population, with evidence of sub-structuring based on climate. Phylogenetic analysis and haplotype networks showed evidence of close relationships between some American and Chinese isolates, consistent with recent introduction from America to China via planting materials. However, there is also evidence for endemic Chinese P. viticola isolates. Our results suggest that the current Chinese Plasmopara viticola population is an admixture of endemic and introduced isolates.
Project description:In this paper, we present the academic genealogy of presidents of the Psychometric Society by constructing a genealogical tree, in which Ph.D. students are encoded as descendants of their advisors. Results show that most of the presidents belong to five distinct lineages that can be traced to Wilhelm Wundt, James Angell, William James, Albert Michotte or Carl Friedrich Gauss. Important psychometricians Lee Cronbach and Charles Spearman play only a marginal role. The genealogy systematizes important historical knowledge that can be used to inform studies on the history of psychometrics and exposes the rich and multidisciplinary background of the Psychometric Society.
Project description:Genome-wide association studies (GWASs) have identified numerous loci that influence risk for psychiatric diseases. Genetically engineered mice are often used to characterize genes implicated by GWASs. These studies are based on the assumption that observed genotype-phenotype relationships will generalize to humans, implying that the results would at least generalize to other inbred mouse strains. Given current concerns about reproducibility, we sought to directly test this assumption. We produced F1 crosses between male C57BL/6J mice heterozygous for null alleles of Cacna1c and Tcf7l2 and wild-type females from 30 inbred laboratory strains. We found extremely strong interactions with genetic background that sometimes supported diametrically opposing conclusions. These results do not negate the invaluable contributions of mouse genetics to biomedical science, but they do show that genotype-phenotype relationships cannot be reliably inferred by studying a single genetic background, and thus constitute a major challenge to the status quo. VIDEO ABSTRACT.
Project description:MicroRNAs (miRNAs) expressed in the hypothalamus are capable of regulating energy balance and peripheral metabolism by inhibiting translation of target messenger RNAs (mRNAs). Hypothalamic insulin resistance is known to precede that in the periphery, thus a critical unanswered question is whether central insulin resistance creates a specific hypothalamic miRNA signature that can be identified and targeted. Here we show that miR-1983, a unique miRNA, is upregulated in vitro in 2 insulin-resistant immortalized hypothalamic neuronal neuropeptide Y-expressing models, and in vivo in hyperinsulinemic mice, with a concomitant decrease of insulin receptor β subunit protein, a target of miR-1983. Importantly, we demonstrate that miR-1983 is detectable in human blood serum and that its levels significantly correlate with blood insulin and the homeostatic model assessment of insulin resistance. Levels of miR-1983 are normalized with metformin exposure in mouse hypothalamic neuronal cell culture. Our findings provide evidence for miR-1983 as a unique biomarker of cellular insulin resistance, and a potential therapeutic target for prevention of human metabolic disease.
Project description:Accurate simulation of complex biological processes is an essential component of developing and validating new technologies and inference approaches. As an effort to help contain the COVID-19 pandemic, large numbers of SARS-CoV-2 genomes have been sequenced from most regions in the world. More than 5.5 million viral sequences are publicly available as of November 2021. Many studies estimate viral genealogies from these sequences, as these can provide valuable information about the spread of the pandemic across time and space. Additionally such data are a rich source of information about molecular evolutionary processes including natural selection, for example allowing the identification of new variants with transmissibility and immunity evasion advantages. To our knowledge, there is no framework that is both efficient and flexible enough to simulate the pandemic to approximate world-scale scenarios and generate viral genealogies of millions of samples. Here, we introduce a new fast simulator VGsim which addresses the problem of simulation genealogies under epidemiological models. The simulation process is split into two phases. During the forward run the algorithm generates a chain of population-level events reflecting the dynamics of the pandemic using an hierarchical version of the Gillespie algorithm. During the backward run a coalescent-like approach generates a tree genealogy of samples conditioning on the population-level events chain generated during the forward run. Our software can model complex population structure, epistasis and immunity escape. The code is freely available at https://github.com/Genomics-HSE/VGsim.
Project description:The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.