Improving Type 2 Diabetes Phenotypic Classification by Combining Genetics and Conventional Risk Factors

Type 2 Diabetes condition is a multifactorial disorder involves the convergence of genetics, environment, diet and lifestyle risk factors. This paper investigates genetic and conventional (clinical, sociodemographic) risk factors and their predictive power in classifying Type 2 Diabetes. Six statistically significant Single Nucleotide Polymorphisms (SNPs) associated with Type 2 Diabetes are derived by conducting logistic association analysis. The derived SNPs in addition to conventional risk factors are used to model supervised machine learning algorithms to classify cases and controls in genome wide association studies (GWAS). Models are trained using genetic variable analysis, genetic and conventional variable analysis, and conventional variable analysis. The results demonstrate of the three models, higher predictive capacity is evident when genetic and conventional predictors are combined. Using a Random Forest classifier, the Area Under the Curve=73.96%, Sensitivity=68.42%, and Specificity=78.67%

Personalized medicine : the impact on chemistry

An effective strategy for personalized medicine requires a major conceptual change in the development and application of therapeutics. In this article, we argue that further advances in this field should be made with reference to another conceptual shift, that of network pharmacology. We examine the intersection of personalized medicine and network pharmacology to identify strategies for the development of personalized therapies that are fully informed by network pharmacology concepts. This provides a framework for discussion of the impact personalized medicine will have on chemistry in terms of drug discovery, formulation and delivery, the adaptations and changes in ideology required and the contribution chemistry is already making. New ways of conceptualizing chemistry’s relationship with medicine will lead to new approaches to drug discovery and hold promise of delivering safer and more effective therapies

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.Siirretty Doriast

Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations

Genome-wide association studies (GWASs) have focused primarily on populations of European descent, but it is essential that diverse populations become better represented. Increasing diversity among study participants will advance our understanding of genetic architecture in all populations and ensure that genetic research is broadly applicable. To facilitate and promote research in multi-ancestry and admixed cohorts, we outline key methodological considerations and highlight opportunities, challenges, solutions, and areas in need of development. Despite the perception that analyzing genetic data from diverse populations is difficult, it is scientifically and ethically imperative, and there is an expanding analytical toolbox to do it well

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine

Breeding Quality Protein Maize (QPM): Protocols for Developing QPM Cultivars

This manual is intended for maize breeders who would like to start developing quality protein maize (QPM) cultivars. It is a compilation and consolidation of several breeding protocols successfully used at CIMMYT over two decades of QPM development and breeding. A brief background and the basic theory of QPM genetics are explained, leading up to detailed methods and procedures of QPM development.Zea mays, Plant breeding, Breeding methods, Genetic resources, Protein quality, Protein content, Application methods, Lysine, Tryptophan, Food composition, Crop Production/Industries, F30, Q04,

Geeniinfo väärtus südame-veresoonkonnahaiguste riski hindamisel

Väitekirja elektrooniline versioon ei sisalda publiktasiooneFakt, et südame-veresoonkonnahaigused on peamiseks suremuse põhjustajaks maailmas, rõhutab vajadust edendada ja täiustada olemasolevaid haiguse ennetus- ja ennustusstrateegiaid. Südame-veresoonkonnahaiguste riski hindamine põhineb tänases kliinilises praktikas klassikalisi fenotüübilisi riskitegureid arvestavatel riski hindamise mudelitel. Kuigi nimetatud strateegia võimaldab kõrge riskiga indiviide suhteliselt hästi tuvastada, jääb pea kolmandiku riski hinnang ebatäpseks ning ravimääramine ebaselgeks. Lisaks eelnevale peegeldub mudelite piiratud kasutus selles, et riskifaktorite loetlemisega hinnatakse tegelikkuses molekulaarsel tasandil juba toimunud muutusi. Seega leevendatakse praeguse strateegia kasutamisel pigem patoloogia progresseerunud kulgu, kui pärsitakse või ennetatakse molekulaarsete mehhanismide häirumist varases staadiumis. Üheks võimalikuks edasiarenduse meetmeks pakutakse haiguse geneetilise informatsiooni arvestamist. Seda eeskätt seetõttu, et südame-veresoonkonnahaiguste geneetiliste seoste uuringutega on täna jõutud hinnanguteni, millel on potentsiaali muuta oluliselt täpsemaks nii tervete indiviidide varast haigusriski hindamist kui ka haigete kliinilist käsitlust. Selle doktoritöö peamiseks eesmärgiks on anda ülevaade tänastest südame-veresoonkonnahaiguste riski hindamise meetmetest ning sellest, kas ja kuidas geneetilise informatsiooni kaasamine igapäeva kliinilistesse otsustesse neid edendada võiks. Lisaks toon näiteid, kuidas kõrge resolutsiooniga genoomi järjestusandmestik võimaldaks tunnusega seotud põhjuslikke geenivariante täpsemini tuvastada ning kuidas populatsiooni-põhise biopanga andmete kasutamine tõhustaks kõrge riskiga indiviidide kliinilist käsitlust.Cardiovascular diseases are the main cause of morbidity and mortality worldwide, underscoring the requisite for improved strategies for disease prevention and risk prediction. The main approach applied in today's clinical practice to identify those at increased cardiovascular risk relies on the utilization of phenotypic risk models that facilitate the estimation of one's disease risk based on traditional risk factors. While this strategy is beneficial for avoiding disease incidence and it does on the whole target individuals at high risk for treatment sufficiently well, a third of individuals, who experience an adverse event, are misclassified into a lower risk category and are therefore advocated treatment ambiguously. Importantly, the current approach lacks in providing accurate estimation for primordial prevention, that is estimating risk before risk factors emerge. To overcome this issue and seek for approaches to enhance risk estimation, attention has now been turned to genetics with the aim of incorporating genetic information into established risk prediction strategies. The scrutiny of the genetic architecture of cardiovascular diseases conducted in recent decades has today resulted in estimates that can be of clinical utility and value. This doctoral thesis aims to give an overview of the status quo of the genomic research on cardiovascular diseases and contemplate on what the advances in molecular technology, computational capacities and large-scale initiatives have enabled, what the progress of these endeavours entail and whether these do bestow incremental value for clinical utility. Furthermore, I will bring examples of how the utilization of high-coverage sequencing data can enhance the search for the genetic underpinnings of cardiovascular disease-associated phenotypes, and how the use of large-scale cohorts and population-based biobanks can enable the anticipated improvement in disease risk estimation, especially when integrated into a national healthcare system.https://www.ester.ee/record=b522706