University of Geneva

About: University of Geneva is a education organization based out in Geneva, Switzerland. It is known for research contribution in the topics: Population & Planet. The organization has 26887 authors who have published 65265 publications receiving 2931373 citations. The organization is also known as: Geneva University & Universite de Geneve.

Sex differences in Alzheimer disease — the gateway to precision medicine

Abstract: Alzheimer disease (AD) is characterized by wide heterogeneity in cognitive and behavioural syndromes, risk factors and pathophysiological mechanisms. Addressing this phenotypic variation will be crucial for the development of precise and effective therapeutics in AD. Sex-related differences in neural anatomy and function are starting to emerge, and sex might constitute an important factor for AD patient stratification and personalized treatment. Although the effects of sex on AD epidemiology are currently the subject of intense investigation, the notion of sex-specific clinicopathological AD phenotypes is largely unexplored. In this Review, we critically discuss the evidence for sex-related differences in AD symptomatology, progression, biomarkers, risk factor profiles and treatment. The cumulative evidence reviewed indicates sex-specific patterns of disease manifestation as well as sex differences in the rates of cognitive decline and brain atrophy, suggesting that sex is a crucial variable in disease heterogeneity. We discuss critical challenges and knowledge gaps in our current understanding. Elucidating sex differences in disease phenotypes will be instrumental in the development of a 'precision medicine' approach in AD, encompassing individual, multimodal, biomarker-driven and sex-sensitive strategies for prevention, detection, drug development and treatment.

Topology of mammalian developmental enhancers and their regulatory landscapes

Abstract: How a complex animal can arise from a fertilized egg is one of the oldest and most fascinating questions of biology, the answer to which is encoded in the genome. Body shape and organ development, and their integration into a functional organism all depend on the precise expression of genes in space and time. The orchestration of transcription relies mostly on surrounding control sequences such as enhancers, millions of which form complex regulatory landscapes in the non-coding genome. Recent research shows that high-order chromosome structures make an important contribution to enhancer functionality by triggering their physical interactions with target genes.

Using allele frequencies and geographic subdivision to reconstruct gene trees within a species: molecular variance parsimony.

Abstract: We formalize the use of allele frequency and geographic information for the construction of gene trees at the intraspecific level and extend the concept of evolutionary parsimony to molecular variance parsimony. The central principle is to consider a particular gene tree as a variable to be optimized in the estimation of a given population statistic. We propose three population statistics that are related to variance components and that are explicit functions of phylogenetic information. The methodology is applied in the context of minimum spanning trees (MSTs) and human mitochondrial DNA restriction data, but could be extended to accommodate other tree-making procedures, as well as other data types. We pursue optimal trees by heuristic optimization over a search space of more than 1.29 billion MSTs. This very large number of equally parsimonious trees underlines the lack of resolution of conventional parsimony procedures. This lack of resolution is highlighted by the observation that equally parsimonious trees yield very different estimates of population genetic diversity and genetic structure, as shown by null distributions of the population statistics, obtained by evaluation of 10,000 random MSTs. We propose a non-parametric test for the similarity between any two trees, based on the distribution of a weighted coevolutionary correlation. The ability to test for tree relatedness leads to the definition of a class of solutions instead of a single solution. Members of the class share virtually all of the critical internal structure of the tree but differ in the placement of singleton branch tips.