A Strategy analysis for genetic association studies with known inbreeding

Background: Association studies consist in identifying the genetic variants which are related to a specific disease through the use of statistical multiple hypothesis testing or segregation analysis in pedigrees. This type of studies has been very successful in the case of Mendelian monogenic disorders while it has been less successful in identifying genetic variants related to complex diseases where the insurgence depends on the interactions between different genes and the environment. The current technology allows to genotype more than a million of markers and this number has been rapidly increasing in the last years with the imputation based on templates sets and whole genome sequencing. This type of data introduces a great amount of noise in the statistical analysis and usually requires a great number of samples. Current methods seldom take into account gene-gene and gene-environment interactions which are fundamental especially in complex diseases. In this paper we propose to use a non-parametric additive model to detect the genetic variants related to diseases which accounts for interactions of unknown order. Although this is not new to the current literature, we show that in an isolated population, where the most related subjects share also most of their genetic code, the use of additive models may be improved if the available genealogical tree is taken into account. Specifically, we form a sample of cases and controls with the highest inbreeding by means of the Hungarian method, and estimate the set of genes/environmental variables, associated with the disease, by means of Random Forest. Results: We have evidence, from statistical theory, simulations and two applications, that we build a suitable procedure to eliminate stratification between cases and controls and that it also has enough precision in identifying genetic variants responsible for a disease. This procedure has been successfully used for the betathalassemia, which is a well known Mendelian disease, and also to the common asthma where we have identified candidate genes that underlie to the susceptibility of the asthma. Some of such candidate genes have been also found related to common asthma in the current literature. Conclusions: The data analysis approach, based on selecting the most related cases and controls along with the Random Forest model, is a powerful tool for detecting genetic variants associated to a disease in isolated populations. Moreover, this method provides also a prediction model that has accuracy in estimating the unknown disease status and that can be generally used to build kit tests for a wide class of Mendelian diseases

Detour-impact index method and traffic gathering algorithm for assessing alternative paths of disrupted roads

Infrastructure plays a key role in society. Recent collapses of bridges have underlined their importance for road functionality, causing disruptions to commuters and emergency vehicles. Major issues arise on rural roads, where the lack of redundancy leads to the isolation of entire communities. Actual approaches to assess the resilience of countryside roads rely on the availability of specific datasets, limiting their practical application; this issue is typically related to traffic data. This research aims to propose innovative algorithms to assess the road network’s vulnerability in rural areas, including a novel traffic data collection process and its calibration. The aggregate metric is called Detour-Impact Index (DII) and compares user costs before and after a disruptive event. The method uses traditional network-impact metrics in combination with a new algorithm that allows us to gather quantitative traffic data starting from qualitative information. User travel time showed good agreement between the proposed procedure and traditional web-based methods. Furthermore, the paper provides user delay costs functions accounting for traffic composition, trip purposes, vehicle operative costs, nonlinear volume–capacity relation, and average daily traffic. A significant aspect is the adaptability of this framework, as it is designed to be coupled with existing approaches. The method is demonstrated on a case study in Tuscany (Italy).The first, third and sixth authors acknowledge that, this work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI-01-0145- FEDER-007633. This work was supported by the FCT Foundation for Science and Technology under Grant SFRH/ BD/145478/2019

Risk management for bridges: a case study of unforeseen failure mode

Risk management plays a crucial role in the stakeholders’ decision making because it is directly related to safety, serviceability and economy. There is now a growing concern about how to relocate known risks into an acceptance threshold: this implies the evaluation of several options obtained from hazard scenarios considering the related consequences. In parallel, practitioners usually rely on standard tools for risk assessment, and on structural codes to compute performances. Although this approach is currently widely implemented, this research shows that hazardous situations can arise in properly designed infrastructures, due to errors in management. This paper deals with such issue, also highlighting a gap in current codes that could contribute to losses caused by unforeseen failure modes. In this study, a preliminary FMEA assessment was performed to identify the failure modes that required a deeper quantitative analysis. In a second step, a quantitative analysis was implemented, using a modular methodology that combines reliability theory with a risk-based approach. The results evidenced that a wider analysis focused on the identification of vulnerable areas shall be considered in every stage of the asset management. Furthermore, the dynamic of this process is regulated by the established safety level concerning possible damages to people, production sites and commercial activities.This work was partly financed by FEDER funds through the Competitivity Factors Operational Programme - COMPETE and by national funds through FCT Foundation for Science and Technology within the scope of the project POCI01-0145-FEDER-007633. This work was supported by the FCT Foundation for Science and Technology under Grant SFRH/BD/145478/2019. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769255

Sustainable safety evaluation of roads network in case of extreme weather events

Recent failures in road networks highlight their vulnerability towards natural hazards, particularly to extreme weather events. This paper proposes a method to evaluate the safety of road networks in case of collapse of one or more bridges. In addition, relevant consequences in terms of safety of human life, direct and indirect cost are crucial aspects to consider. The framework described here is based on the knowledge of road and river network, of the individual bridges and of the traffic data. However, this approach can be generalized in case of interruption of road network due to other causes. An algorithm has been developed to extract traffic data from Google and elaborate it throughout a procedure based on the application of the USA Highway Capacity Manual. This consents to have a quantitative definition of the road traffic directly from the users and to get updated traffic data. The maps are processed throughout a GIS software and, thanks to the application of a routing algorithm and proper constraints, it is possible to evaluate the effects of the interruption of one or more bridges. The consequences are evaluated in terms of drivers’ delay and time cost. This provides useful information about priority of intervention with the aim of proposing to stakeholders a suitable instrument for disaster prevention and management

Methylenetetrahydrofolate Reductase Gene Polymorphisms in Burkina Faso: Impact on Plasma Fasting Homocysteine and after Methionine Loading Test

SUMMARY In Burkina Faso the levels of plasma homocysteine (Hcy) are lower and the methionine loading tests suggest a more effective Hcy metabolism. The polymorphisms of methylenetetrahydrofolate reductase (MTHFR) showed a relevant difference in the allele frequencies of T MTHFR-677 in young and in old subjects, while the allele frequency of C MTHFR-1298C was comparable in young and old subjects. The aim of this paper was to study the impact of the MTHFR polymorphisms on plasma fasting Hcy and after methionine loading in Burkina Faso. The young subjects with CC MTHFR-677 genotype had levels of Hcy significantly lower than CT and TT subjects. The level of Hcy in subjects who had AA, AC and CC MTHFR-1298 genotypes were comparable. The levels of Hcy after the methionine loading test were significantly higher in CT and TT MTHFR-677 genotype. These results suggest that the genetic situation in Burkina Faso is different from that of other Western countries and this guarantees the maintenance of lower plasma levels of Hcy in young and old Africans. The elevated levels of plasma Hcy in old subjects compared to young subjects, against the low prevalence of the T allele in elderly subjects is discussed. (Clin. Lab. 2007;53:XXX-XXX) KEY WORDS Burkina Faso, homocysteine, methionine loading test, MTHFR, C677T, A1298

Microsatellites and SNPs linkage analysis in a Sardinian genetic isolate confirms several essential hypertension loci previously identified in different populations

Background. A multiplicity of study designs such as gene candidate analysis, genome wide search (GWS) and, recently, whole genome association studies have been employed for the identification of the genetic components of essential hypertension (EH). Several genome-wide linkage studies of EH and blood pressure-related phenotypes demonstrate that there is no single locus with a major effect while several genomic regions likely to contain EH-susceptibility loci were validated by multiple studies. Methods. We carried out the clinical assessment of the entire adult population in a Sardinian village (Talana) and we analyzed 16 selected families with 62 hypertensive subjects out of 267 individuals. We carried out a double GWS using a set of 902 uniformly spaced microsatellites and a high-density SNPs map on the same group of families. Results. Three loci were identified by both microsatellites and SNP scans and the obtained linkage results showed a remarkable degree of similarity. These loci were identified on chromosome 2q24, 11q23.1–25 and 13q14.11–21.33. Further support to these findings is their broad description present in literature associated to EH or related phenotypes. Bioinformatic investigation of these loci shows several potential EH candidate genes, several of whom already associated to blood pressure regulation pathways. Conclusion. Our search for major susceptibility EH genetic factors evidences that EH in the genetic isolate of Talana is due to the contribution of several genes contained in loci identified and replicated by earlier findings in different human populations

Impact of alien insect pests on Sardinian landscape and culture

Geologically Sardinia is a raft which, for just under thirty million years, has been crossing the western Mediterranean, swaying like a pendulum from the Iberian to the Italian Peninsula. An island so large and distant from the other lands, except for its "sister" Corsica, has inevitably developed an autochthonous flora and fauna over such a long period of time. Organisms from other Mediterranean regions have added to this original contingent. These new arrivals were not randomly distributed over time but grouped into at least three great waves. The oldest two correspond with the Messinian salinity crisis about 7 million years ago and with the ice age, when, in both periods, Sardinia was linked to or near other lands due to a fall in sea level. The third, still in progress, is linked to human activity. Man has travelled since ancient times and for many centuries introduced allochthonous species to Sardinia which radically modified the native flora and fauna, but always at a very slow and almost unnoticeable rate. The use of sailing or rowing boats, with their low speeds, hindered the transport of living organisms from one place to another. The use of the steam boat, introduced around 1840 but widely diffuse around 1870-1880, opened the doors to more frequent arrivals and also to organisms from the American Continent. This technical innovation had an influence over the whole world economy, with its well-known grain crisis, and coincided in Sardinia with the arrival of Roman dairymen, producers of pecorino cheese and the beginning of the expansion of sheep farming which would continue uninterrupted until the present day. In this period of sudden social and environmental change, an insect was introduced which would turn out to be probably the most economically devastating agricultural pest in Europe: the Grape Phylloxera. The vineyard and wine business collapsed first in France then in Italy. The Phylloxera arrived in Sardinia in 1883 and wine production crashed a very short time later and only resumed after the distribution of American vine rootstock at the beginning of the 20th Century. From then, vine cultivation in Europe was modified with the essential use of this rootstock. Since then methods of transport have increased enormously in number and speed. The number of allochthonous and invasive species has increased proportionally: some of them along with exotic plants which are cultivated on the island, others following man in his activities. Often these new pests attack and destroy ornamental plants which have become part of the Sardinian landscape, causing it to change; just as often their presence requires methods of pest management which are different from the traditional methods on specific crops; finally in at least one case (the Asian tiger mosquito) they pose a threat to our health

Application of a new method for GWAS in a related case/control sample with known pedigree structure: identification of new loci for nephrolithiasis

In contrast to large GWA studies based on thousands of individuals and large meta-analyses combining GWAS results, we analyzed a small case/control sample for uric acid nephrolithiasis. Our cohort of closely related individuals is derived from a small, genetically isolated village in Sardinia, with well-characterized genealogical data linking the extant population up to the 16(th) century. It is expected that the number of risk alleles involved in complex disorders is smaller in isolated founder populations than in more diverse populations, and the power to detect association with complex traits may be increased when related, homogeneous affected individuals are selected, as they are more likely to be enriched with and share specific risk variants than are unrelated, affected individuals from the general population. When related individuals are included in an association study, correlations among relatives must be accurately taken into account to ensure validity of the results. A recently proposed association method uses an empirical genotypic covariance matrix estimated from genome-screen data to allow for additional population structure and cryptic relatedness that may not be captured by the genealogical data. We apply the method to our data, and we also investigate the properties of the method, as well as other association methods, in our highly inbred population, as previous applications were to outbred samples. The more promising regions identified in our initial study in the genetic isolate were then further investigated in an independent sample collected from the Italian population. Among the loci that showed association in this study, we observed evidence of a possible involvement of the region encompassing the gene LRRC16A, already associated to serum uric acid levels in a large meta-analysis of 14 GWAS, suggesting that this locus might lead a pathway for uric acid metabolism that may be involved in gout as well as in nephrolithiasis

Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability

Peer reviewe

High Differentiation among Eight Villages in a Secluded Area of Sardinia Revealed by Genome-Wide High Density SNPs Analysis

To better design association studies for complex traits in isolated populations it's important to understand how history and isolation moulded the genetic features of different communities. Population isolates should not “a priori” be considered homogeneous, even if the communities are not distant and part of a small region. We studied a particular area of Sardinia called Ogliastra, characterized by the presence of several distinct villages that display different history, immigration events and population size. Cultural and geographic isolation characterized the history of these communities. We determined LD parameters in 8 villages and defined population structure through high density SNPs (about 360 K) on 360 unrelated people (45 selected samples from each village). These isolates showed differences in LD values and LD map length. Five of these villages show high LD values probably due to their reduced population size and extreme isolation. High genetic differentiation among villages was detected. Moreover population structure analysis revealed a high correlation between genetic and geographic distances. Our study indicates that history, geography and biodemography have influenced the genetic features of Ogliastra communities producing differences in LD and population structure. All these data demonstrate that we can consider each village an isolate with specific characteristics. We suggest that, in order to optimize the study design of complex traits, a thorough characterization of genetic features is useful to identify the presence of sub-populations and stratification within genetic isolates