Improved branch and bound algorithm for detecting SNP-SNP interactions in breast cancer

BACKGROUND: Single nucleotide polymorphisms (SNPs) in genes derived from distinct pathways are associated with a breast cancer risk. Identifying possible SNP-SNP interactions in genome-wide case–control studies is an important task when investigating genetic factors that influence common complex traits; the effects of SNP-SNP interaction need to be characterized. Furthermore, observations of the complex interplay (interactions) between SNPs for high-dimensional combinations are still computationally and methodologically challenging. An improved branch and bound algorithm with feature selection (IBBFS) is introduced to identify SNP combinations with a maximal difference of allele frequencies between the case and control groups in breast cancer, i.e., the high/low risk combinations of SNPs. RESULTS: A total of 220 real case and 334 real control breast cancer data are used to test IBBFS and identify significant SNP combinations. We used the odds ratio (OR) as a quantitative measure to estimate the associated cancer risk of multiple SNP combinations to identify the complex biological relationships underlying the progression of breast cancer, i.e., the most likely SNP combinations. Experimental results show the estimated odds ratio of the best SNP combination with genotypes is significantly smaller than 1 (between 0.165 and 0.657) for specific SNP combinations of the tested SNPs in the low risk groups. In the high risk groups, predicted SNP combinations with genotypes are significantly greater than 1 (between 2.384 and 6.167) for specific SNP combinations of the tested SNPs. CONCLUSIONS: This study proposes an effective high-speed method to analyze SNP-SNP interactions in breast cancer association studies. A number of important SNPs are found to be significant for the high/low risk group. They can thus be considered a potential predictor for breast cancer association

Discovering Higher-order SNP Interactions in High-dimensional Genomic Data

In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise

The effects of polymorphisms in the CX3CR1 gene on the development of canine hip dysplasia

Hip dysplasia, caused by both environmental and genetic components, is a common disorder characterized by hip instability in humans and dogs. Unfortunately, the genetic mechanisms that cause the disease in both have not been fully determined. The aim of this study was polymorphisms in the exon 2 and 3' UTR regions of the CX3CR1 gene were determined and their effects on the development of Canine Hip Dysplasia (CHD) in three dog breeds (German Shepherd, Belgian Malinois, Labrador Retriever). For this purpose, a case -control study was designed with 172 dogs in Dog Breeding and Training Center (DBTC) in Turkey. Each dog was evaluated according to the Norberg angle by the DBTC veterinarians. One hundred and seventeen dogs (32 German Shepherds, 49 Belgian Malinois, 36 Labrador Retrievers) classified as normal were included in the control group, and fifty - five dogs (24 German Shepherds, 14 Belgian Malinois, 17 Labrador Retrievers) diagnosed with CHD were included in the case group. Molecular genetic analyzes were performed with blood samples taken from each dog. Seven previously identified SNPs (g.8938599_8938600insCC, g.8937121G>A, g.8937137A>G, g.8937319T>G, g.8937441T>C, g.8937450A>G, g.8937590C>T) and a rare novel deletion (g. 8937205_ 8937206del) were identified in the 3' UTR regions of the CX3CR1 gene. The distribution of SNPs alleles in the case and control was compared by means of statistical analysis at allelic, genotypic, haplotypic, and SNP - SNP interaction levels. Single SNP analysis revealed that g.8937121G>A was significantly associated with susceptibility to CHD in Belgian Malinois (p = 0.00049) in the codominant model. Five SNP - SNP interactions were identified to be associated with CHD in Labrador Retrievers and the most suggestive of these was between g.8938599_8938600insCC and g.8937450A>G (p = 0.0004). We found that one haplotype block, consisting of two SNPs (g.8937137A>G and g.8937319T>G) was associated with susceptibility to CHD in Belgian Malinois (p = 0.022). None of the detected polymorphisms was statistically significantly associated with CHD in German Shepherds. © 2022 TUBITAK. All rights reserved.TUBAP-2018-139The present study has been supported by the Scientific Research Projects Commission of Trakya University with the project number TUBAP-2018-139

Molecular genetic analysis of inherited kidney disease in Saudi Arabia

PhD ThesisInherited abnormalities of the kidney are frequently observed and represent a significant cause of morbidity and mortality. The globally increasing number of patients with end- stage renal disease (ESRD) urges the identification of molecular pathways involved in renal pathophysiology, to serve as targets for therapeutic intervention. Data from 2010 estimates the Saudi Arabian population to be 27 million, with one of the highest growth rates in the world. The population is characterized with high consanguinity rate, large family size, and a tribal structure. The consanguinity rate results in a high incidence of autosomal recessive genetic disorders. The population is at high risk of renal failure, with 133 incident cases per million populations per year that require renal replacement therapy. In such a population, characterization of new kidney disease gene loci using homozygosity mapping and positional cloning within consanguineous families is a powerful strategy. This study aimed to adopt this approach in order to search for known and novel molecular causes of inherited kidney diseases in the Saudi population. We studied patients and families with nephrotic syndrome, renal ciliopathies, nephrocalcinosis and renal agenesis. For nephrotic syndrome, we found that the most common genetic cause was a homozygous mutation in the NPHS2 gene. Novel and reported mutations in known nephrosis genes were detected. In a family with Bardet Biedl Syndrome, we utilized zebrafish and renal epithelial cells to determine the functional significance of a novel BBS5 mutation. In another consanguineous family with an autosomal recessive syndrome of distal renal tubular acidosis, small kidneys, and nephrocalcinosis we identified a novel locus on chromosome 2. We also describe the molecular genetic investigation of families with bilateral renal agenesis. In conclusion, in the highly consanguineous Saudi population we have utilized a variety of genetic approaches to identify and characterize novel genetic variants causing inherited renal disease.King Khalid Foundatio

Doctor of Philosophy

dissertationFunctional vitamin B12 (cobalamin) deficiency is a subtle, progressive clinical disorder affecting 6-23% of elderly adults. Its symptoms, including fatigue, mood disturbances, and decreased strength, are vague and erroneously attributed to aging. Detection of cobalamin deficiency in elderly adults is confounded by clinical heterogeneity and lack of standardization in metabolic tests. Whereas some patients are asymptomatic with slightly altered metabolite profiles, others develop severe clinical outcomes. Better understanding of biologic factors contributing to cobalamin deficiency heterogeneity in older adults is needed. This is a candidate gene association study evaluating the relationship between genetic variation in the cobalamin-transport molecules (transcobalamin II and its receptor) with cobalamin-related outcome parameters in 795 research participants of the Women's Health and Aging 1 and 2 Studies. Research participant DNA was whole genome amplified and genotyped using the iPLEX Sequenom mass spectroscopy platform. Relationships between genotypes and clinical parameters were assessed using two-way analysis of variance and two-way analysis of covariance, on the fixed factors, race and Single Nucleotide Polymorphism genotype. Results of the dissertation research generated several genetic associations that are useful for further hypothesis testing and clinical validation research. In the transcobalamin II gene, two missense variants were associated with homocysteine and methylmalonic acid levels (rs9621049, rs35838082), two intronic variants were associated with serum cobalamin and homocysteine levels (rs4820888, rs4820887), and one missense variant was associated with mean corpuscular volume (rs11801198). A cluster of SNPs in the promoter region of the transcobalamin II gene was associated with the physical performance parameters, hand grip strength, and walking speed. In the transcobalamin II-receptor gene, a missense coding SNP (rs2336573) was associated with mean serum cobalamin concentrations. Scientific advances responsible for the technology used in this dissertation are being incorporated into healthcare. The tailoring of treatment to an individual's genetic make-up is termed Personalized Medicine. To assist nursing professionals in understanding and preparing for use of these technologies, four elements of Personalized Medicine are reviewed, including 1) discovery of novel biology that guides clinical translation mechanisms, 2) genetic risk assessment, 3) molecular diagnostic technology, and 4) pharmacogenetics and pharmacogenomics. Opportunities for nursing profession engagement are addressed

The role of common genetic variants for predicting the modulation of cardiovascular outcomes

Attrition is a major issue in the drug development process with 79% of clinical failures due to safety and efficacy concerns. Genetic research can provide supporting evidence of a clear causal relationship between the drug target and disease or reveal unintended effects through associations with non-relevant phenotypes informing on potential drug safety. However, due to the underlying genetic architecture, it is often unclear which gene or variant in the loci identified through genetic analyses is driving the association. Due to recent advancements in CRISPR-Cas9 gene-editing, it is now possible to relatively easily perform whole gene knock-out studies and single base-edits to validate genetic findings of the most likely causal variant and gene. Utilising a combination of genetic approaches and functional studies can provide supporting evidence of the therapeutic profile and potential effects of drug therapies and improve our overall understanding of biological pathways and disease mechanisms. The primary aim of this thesis is to provide genetic data to support the ongoing clinical development of hypoxia-inducible factor (HIF)-prolyl hydroxylase inhibitors (PHIs) for treating anaemia of chronic kidney disease (CKD). Genome-wide association studies (GWAS) were used to identify genetic variants lying within or nearby genes encoding the drug target (prolyl hydroxylase [PHD] enzymes). These identified variants were used in Mendelian Randomisation analysis and phenome-wide association studies to genetically mirror the pharmaceutical effects of PHIs and investigate cardiovascular safety. Functional validation studies were employed to functionally validate a genetic variant for use as a proxy and to obtain a better understanding of the downstream causal pathways and biological mechanisms of the drug target. In summary, this thesis demonstrates how a combination of genetic analyses and functional validation studies is a powerful approach to validate GWAS results and further characterise therapeutic effects. This PhD project identified relevant genetic markers to genetically proxy therapeutic modulation of biomarker levels through PHD inhibition and could potentially inform further research using patient-level clinical data from Phase III trials

Fusion, 2019

https://hsrc.himmelfarb.gwu.edu/smhs_fusion/1011/thumbnail.jp

Genetics of membranous nephropathy

Autoimmune membranous nephropathy (AMN) is a rare kidney disease. The genetics of AMN have been partially elucidated and confirmed the role of phospholipase A2 receptor-1 (PLA2R1) and HLA. The functional effect of the genetic variations is not fully understood. This thesis investigates these unexplored genetic aspects utilising a range of methodologies and unique cohorts. Analysing genomic sequencing data of PLA2R1 in 335 AMN patients identified 109 strongly associated variants; 9 with a very strong association, p-value <10-50. In a larger cohort of 1158 European AMN patients, the findings from previous GWAS were confirmed with a strong association with HLA-DQA1, HLA-DRB1 and PLA2R1. No associations were found on a genome wide scale with clinical correlates of disease such as proteinuria, sex, and age. HLA typing by imputation in 372 anti-PLA2R1 antibody positive and uniquely 32 antithrombospondin type-1 domain-containing 7A (THSD7A) antibody positive AMN confirmed the dominant HLA type in European AMN as HLA-DRB1*03:01 and HLADQA1*05:01; replicating previous studies. No statistically significant HLA type was identified for anti-THSD7A AMN. Anti-PLA2R1 AMN has a different genetic risk than anti-THSD7A and anti-contactin AMN as determined by the genetic risk score (GRS), and this can help differentiate between them. Interestingly, 33% of dual antibody negative AMN is likely to be anti-PLA2R1 AMN. AMN patients with a higher genetic risk have a younger age of onset. In a rare, undescribed cohort of 15 non-familial paediatric cases of AMN the GRS proved that these individuals did not have the same genetic risk factors as anti-PLA2R1 AMN. Finally, the genetic risk of AMN in UK Biobank Europeans is 0.8%. Even though there is a high genetic risk for AMN this does not mean this proportion of individuals will develop AMN. In conclusion, this thesis highlights important differences between antibody status groups, confirms previous GWAS findings and reports unique features about rare AMN cohorts

Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation

In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes

Integrative genetic and network approaches to identify key regulators of cardiac fibrosis

Excessive fibrogenic response is a pathological hallmark of chronic complex diseases, including cardiovascular disease. To date, very few gene targets for cardiac fibrosis that led to effective treatments have been identified in humans. In this thesis I study and dissect the genetic component underlying cardiac fibrosis. This study integrates histomorphometric measurements of fibrosis in the rat left ventricle (LV) with gene expression (RNA-Seq from LV) and genetic data in a panel of recombinant inbred (RI) rat strains (n=30). In addition, I integrated RNA-seq LV and genetic data in humans (n=187, healthy and dilated cardiomyopathy (DCM) patients), as well as DCM genome-wide association studies (GWAS) data. I started by carrying out an unbiased co-expression network analysis in the rat heart. The reconstructed cardiac transcriptional modules were associated with quantitative levels of fibrosis. Co-expression networks were also independently built in the heart of DCM patients and by using the rat data, co-expression networks associated with fibrosis, conserved across rats and humans and not present in control human heart were prioritised. In the prioritised networks, I also analysed their cardiac cell type specificity, differential expression after TGFβ induction, potential driving transcription factors and conservation in other fibrotic diseases by analysing human data collected from other organs. Furthermore, I aimed to identify common genetic regulators of the networks (also called master genetic regulators) by using Bayesian multivariate regression approaches. Finally, I integrated GWAS data in DCM (n=2,287) to dissect the genetic basis of DCM. This systems genetics study evidences that there are transcriptional processes involved in the human cardiac fibrogenic response that are conserved across rats and humans, some of them also underlying DCM aetiology. In an attempt to suggest new gene targets for cardiac fibrosis, I also identified the WWP2 gene as a novel trans-acting genetic regulator of cardiac fibrosis.Open Acces