Reducing the Burden of Decision in Digital Democracy Applications: A Comparative Analysis of Six Decision-making Software

The more digital democracy applications lower the costs of political participation, allowing ordinary citizens to propose their own policy initiatives, the more they increase the burden of decision for the very same citizens, who are required to debate and vote on many issues. Drawing from this paradox, this article considers how the designers and administrators of six popular decision-making software (DMS) have introduced software features and norms of use whose function is to reduce the aggregate burden of decision for participants in digital democracy initiatives (DDIs). Building upon Andrew Feenberg’s definition of the design code of technology as a technical stabilization of social demands, this article considers how different DMS stabilize the democratic interventions of a plurality of actors, affecting political equality along two axes of the democratic process: the relationship between the exchange of opinions and the synthesis of opinion and the relationship between agenda setting and voting. This article concludes that the design code of digital democracy software reflects an ongoing tension between the need of governing actors to make the democratic process manageable and the pressure of social actors to make it more equal and inclusive.

Oligogenic combinations of rare variants influence specific phenotypes in complex disorders

ABSTRACTGenetic studies of complex disorders such as autism and intellectual disability (ID) are often based on enrichment of individual rare variants or their aggregate burden in affected individuals compared to controls. However, these studies overlook the influence of combinations of rare variants that may not be deleterious on their own due to statistical challenges resulting from rarity and combinatorial explosion when enumerating variant combinations, limiting our ability to study oligogenic basis for these disorders. We present a framework that combines the apriori algorithm and statistical inference to identify specific combinations of mutated genes associated with complex phenotypes. Our approach overcomes computational barriers and exhaustively evaluates variant combinations to identify non-additive relationships between simultaneously mutated genes. Using this approach, we analyzed 6,189 individuals with autism and identified 718 combinations significantly associated with ID, and carriers of these combinations showed lower IQ than expected in an independent cohort of 1,878 individuals. These combinations were enriched for nervous system genes such as NIN and NGF, showed complex inheritance patterns, and were depleted in unaffected siblings. We found that an affected individual can carry many oligogenic combinations, each contributing to the same phenotype or distinct phenotypes at varying effect sizes. We also used this framework to identify combinations associated with multiple comorbid phenotypes, including mutations of COL28A1 and MFSD2B for ID and schizophrenia and ABCA4, DNAH10 and MC1R for ID and anxiety/depression. Our framework identifies a key component of missing heritability and provides a novel paradigm to untangle the genetic architecture of complex disorders.SIGNIFICANCEWhile rare mutations in single genes or their collective burden partially explain the genetic basis for complex disorders, the role of specific combinations of rare variants is not completely understood. This is because combinations of rare variants are rarer and evaluating all possible combinations would result in a combinatorial explosion, creating difficulties for statistical and computational analysis. We developed a data mining approach that overcomes these limitations to precisely quantify the influence of combinations of two or more mutated genes on a specific clinical feature or multiple co-occurring features. Our framework provides a new paradigm for dissecting the genetic causes of complex disorders and provides an impetus for its utility in clinical diagnosis.

Study of the collagen type VI alpha 3 (COL6A3) gene in Parkinson’s disease

Background To date, the genetic contribution to Parkinson’s disease (PD) remains unclear. Mutations in the collagen type VI alpha 3 (COL6A3) gene were recently identified as a cause of isolated dystonia. Since PD and dystonia are closely related disorders with shared clinical and genetic characteristics, we explored the association between COL6A3 and PD in a Chinese cohort. Methods We performed genetic screening of COL6A3 in a Chinese cohort of 173 patients with sporadic PD and 200 healthy controls. We identified variants that are likely to have pathogenic effects based on: 1) a minor allele frequency of < 0.01; and 2) the variant being recognized as deleterious by at least 15 different in silico predicting tools. Finally, we tested the aggregate burden of COL6A3 on PD via SKAT-O analysis. Results First, we found compound heterozygous COL6A3 gene mutations in one early-onset PD patients. Then, we explored whether COL6A3 variants contributed to increased risk of developing PD in a Chinese population. We detected 21 rare non-synonymous variants. Pathogenicity predictions identified 7 novel non-synonymous variants as likely to be pathogenic. SKAT-O analysis further revealed that an aggregate burden of variants in COL6A3 contributes to PD (p = 0.038). Conclusion An increased aggregate burden of the COL6A3 gene was detected in patients with PD.

Study of The Collagen Type VI Alpha 3 (COL6A3) Gene in Parkinson’s Disease

Background To date, the genetic contribution to Parkinson’s disease (PD) remains unclear. Mutations in the collagen type VI alpha 3 (COL6A3) gene were recently identified as a cause of isolated dystonia. Since PD and dystonia are closely related disorders with shared clinical and genetic characteristics, we explored the association between COL6A3 and PD in a Chinese cohort. Methods We performed genetic screening of COL6A3 in a Chinese cohort of 173 patients with sporadic PD and 200 healthy controls. We identified variants that are likely to have pathogenic effects based on: 1) a minor allele frequency of < 0.01; and 2) the variant being recognized as deleterious by at least 15 different in silico predicting tools. Finally, we tested the aggregate burden of COL6A3 on PD via SKAT-O analysis. Results First, we found compound heterozygous COL6A3 gene mutations in one early-onset PD patients. Then, we explored whether COL6A3 variants contributed to increased risk of developing PD in a Chinese population. We detected 21 rare non-synonymous variants. Pathogenicity predictions identified seven novel non-synonymous variants as likely to be pathogenic. SKAT-O analysis further revealed that an aggregate burden of variants in COL6A3 contributes to PD (p = 0.03). Conclusion An increased aggregate burden of the COL6A3 gene was detected in patients with PD.

Enrichment of Rare Variants in E3 Ubiquitin Ligase Genes in Early Onset Parkinson’s Disease

BackgroundDysfunction of the ubiquitination proteasome system (UPS) is important in the pathogenesis of Parkinson’s disease (PD). Patients with early onset PD (EOPD) are more susceptible to genetic factors. We systematically examined the overlaps between E3 ubiquitin ligase genes and EOPD. MethodsA total of 695 EOPD patients were sequenced with whole exome sequencing. Aggregate burden for rare variants (Minor allele frequency <0.001 and <0.0001) in a total of 44 E3 ubiquitin ligase genes causing disorders involved in the nervous system were analyzed.ResultsThere was significant enrichment of the rare and rare damaging variants in the E3 ubiquitin ligase genes in EOPD patients. Detailly, at the gene-based level, the strongest associations were found in HERC1, IRF2BPL, KMT2D, RAPSN, RLIM, RNF168 and RNF216. ConclusionOur findings highlight the importance of the UPS mechanism in the pathogenesis of PD from the genetic perspective. Moreover, our study also expanded the susceptible gene spectrum for PD.

Metabolic subgroups and cardiometabolic multimorbidity in the UK Biobank

Background: Ischemic heart disease (IHD), diabetes, cancer and dementia share features of age-associated metabolic dysfunction. We hypothesized that metabolic diversity explains the diversity of morbidity later in life. Methods: We analyzed data from the UK Biobank (N = 329,908). A self-organizing map (SOM, an artificial neural network) was trained with 51 metabolic traits adjusted for age and sex. The SOM analyses produced six subgroups that summarized the multi-variable metabolic diversity. The subgroup with the lowest adiposity and disease burden was chosen as the reference. Hazard ratios (HR) were modeled by Cox regression (P < 0.0001 unless otherwise indicated). Enrichment of multi-morbidity over random expectation was tested by permutation analysis. Results: The subgroup with the highest sex hormones was not associated with IHD (HR = 1.04, P = 0.14). The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with IHD. The subgroup with high adiposity, inflammation and kidney stress was associated with IHD (HR = 2.11), cancer (HR= 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high triglycerides and liver enzymes was at risk of diabetes (HR = 15.6). Paradoxical enrichment of multimorbidity in young individuals and in favorable subgroups was observed. Conclusions: These results support metabolic diversity as an explanation to diverging morbidity and demonstrate the potential value of population-based metabolic subgroups as public health targets for reducing aggregate burden of chronic diseases in ageing populations.

Abstract 559: Exploiting Macrophage Autophagy-Lysosomal Biogenesis With the Natural Sugar Trehalose as a Therapy for Atherosclerosis

The autophagy-lysosome system is a catabolic cellular mechanism that degrades dysfunctional proteins and organelles. In atherosclerosis, there is mounting evidence that this process is rendered dysfunctional particularly in plaque macrophages and is an important trigger for plaque progression. In an effort to characterize practical inducers of macrophage degradative capacity, we now describe the unique vascular benefits of a natural sugar called trehalose, a recognized autophagy inducer with a currently unknown mechanism of action. Trehalose-treated macrophages display enhanced autophagy via a process that involves lysosomal stress and resultant activation of TFEB, the master transcriptional regulator of autophagy-lysosomal biogenesis. We find an important downstream effect of trehalose to be the induction of p62-dependent selective autophagy of cytotoxic polyubiquitinated protein aggregates and dampening of IL-1β/inflammasome function. We confirm the relevance of these in vitro observations in several pro-atherogenic (ApoE-/-) mouse models. Administration of trehalose during eight weeks of Western diet feeding potently induces autophagy and TFEB in plaque macrophages with concomitant reductions in polyubiquitinated protein aggregate burden along with significantly reduced plaque size and complexity. Importantly, these findings are completely abrogated in mice deficient in macrophage autophagy (ATG5-/-) or the selective autophagy chaperone (p62-/-). Further detailed pharmacokinetic evaluation of trehalose shows that physiologically relevant concentrations are indeed achievable in mice. Taken together, our data support the serious consideration of this safe and natural sugar as a potent inducer of macrophage degradative capacity in the treatment of atherosclerotic vascular disease.

Improved estimation of the health and economic burden of chronic disease risk factors in Manitoba

Introduction There are analytic challenges involved with estimating the aggregate burden of multiple risk factors (RFs) in a population. We describe a methodology to account for overlapping RFs in some sub-populations, a phenomenon that leads to ''double-counting'' the diseases and economic burden generated by those factors. Methods Our method uses an efficient approach to accurately analyze the aggregate economic burden of chronic disease across a multifactorial system. In addition, it involves considering the effect of body weight as a continuous or polytomous exposure that ranges from no excess weight through overweight to obesity. We then apply this method to smoking, physical inactivity and overweight/obesity in Manitoba, a province of Canada. Results The annual aggregate economic burden of the RFs in Manitoba in 2008 is about $1.6 billion ($557 million for smoking, $299 million for physical inactivity and $747 million for overweight/obesity). The total burden represents a 12.6% downward adjustment to account for the effect of multiple RFs in some individuals in the population. Conclusion An improved estimate of the aggregate economic burden of multiple RFs in a given population can assist in prioritizing and gaining support for primary prevention initiatives.