In order to get full understanding of how evolution proceeds in natural settings it is necessary to reveal the genetic basis of the phenotypic traits that play a role for individual fitness in different environments. There are a few possible approaches, most of which stem from traditional mapping efforts in domestic animals and other model species. Here we set the stage for gene mapping in natural populations of birds by producing a large number of anchor markers of broad utility for avian genetical research and use these markers to generate a genetic map of the collared flycatcher (Ficedula albicollis). The map reveals a very high degree of synteny and gene order conservation between bird species separated by as much as 100 million years. This is encouraging for later stages of mapping procedures in natural populations since this means that there is a possibility to use the information from already characterized avian genomes to track candidate genes for detailed analysis in non-model species. One interesting aspect of the low degree of rearrangements occurring in the avian genomes is that this could play a role in the low rate of hybridization barriers formed in birds compared to for instance mammals. An analysis of Z-linked gene markers reveals relatively long-range linkage disequilibrium (LD) in collared flycatchers compared to other outbred species but still, LD seems to decay within < 50 kb indicating that > 20.000 markers would be needed to cover the genome in an association scan. A detailed scan of 74 Z-linked genes evenly distributed along the chromosome in both the collared flycatcher and the pied flycatcher (Ficedula hypoleuca) indicates that there are regions that evolve under directional selection, regions that might harbor loci of importance for adaptive divergence and/or hybrid inviability.

No description available.

The genus Vitis (the grapevine) is a group of highly diverse, diploid woody perennial vines consisting of approximately 60 species from across the northern hemisphere. To gain insights into the use of wild Vitis species during the past century of interspecific grape breeding and to provide a foundation for marker- assisted breeding programmes, we present a principal components analysis based ancestry estimation method to calculate admixture proportions of hybrid grapes in the United States Department of Agriculture grape germplasm collection using genome-wide polymorphism data. We find that grape breeders have backcrossed to both the domesticated V. vinifera and wild Vitis species and that reasonably accurate genome-wide ancestry estimation can be performed on interspecific Vitis hybrids using a panel of fewer than 50 ancestry informative genetic markers.

Sex determination in mammals is regulated by the sex-determining region on the Y chromosome (SRY); the presence of SRY activates the male developmental pathway and suppresses the gene network necessary for female gonad development. Mutations in sex determination genes lead to various abnormal sexual phenotypes, including sex reversal syndrome in which the genetic and phenotypic sex do not match. Sex reversal syndrome has been reported in humans, mouse, and several domestic species. In horses, SRY-negative XY sex reversal syndrome has been well described and is caused by deletions on the Y chromosome. However, the molecular causes of the SRY-positive condition in horses and other mammals are not known. This research investigated five horses affected with SRY-positive XY sex reversal syndrome. Sequencing of the coding exon region of the SRY gene in the five cases showed 99-100% alignment with the sequences of normal males. Genotyping of two closely related individuals with 46 normal male controls on an equine SNP50 Beadchip identified two statistically significant SNPs in a ~16 Mb region on the long arm of horse chromosome 3 (ECA3q). The region was analyzed using Gene Ontology (GO) and Gene Relationships Across Implicated Loci (GRAIL) to select functionally relevant candidate genes for sequencing. Further analysis of the entire horse genome was done through array comparative genomic hybridization (aCGH), which investigated possible structural rearrangements, such as copy number variants (CNVs). Deletions of olfactory receptor genes were detected on multiple chromosomes and confirmed through quantitative real-time PCR (qPCR). A homozygous deletion on ECA29 in a region containing genes of the aldo-keto reductase gene family, known to play a role in interconverting sex hormones between active forms and inactive forms, was discovered in two sex reversed animals. The findings were confirmed through qPCR and fluorescence in situ hybridization (FISH), and experiments to define the specific breakpoints of the deletion through PCR have been initiated. This research represents the first systematic search in the horse genome for mutations and CNVs related to sex determination. The findings contribute to better understanding of the molecular mechanisms of sex determination in horses and other mammals, including humans.

Taking the advantage of the high-throughput Single Nucleotide Polymorphism (SNP) genotyping technology, Genome-Wide Association Studies (GWASs) are regarded holding promise for unravelling complex relationships between genotype and phenotype. GWASs aim to identify genetic variants associated with disease by assaying and analyzing hundreds of thousands of SNPs. Traditional single-locus-based and two-locus-based methods have been standardized and led to many interesting findings. Recently, a substantial number of GWASs indicate that, for most disorders, joint genetic effects (epistatic interaction) across the whole genome are broadly existing in complex traits. At present, identifying high-order epistatic interactions from GWASs is computationally and methodologically challenging. My dissertation research focuses on the problem of searching genome-wide association with considering three frequently encountered scenarios, i.e. one case one control, multi-cases multi-controls, and Linkage Disequilibrium (LD) block structure. For the first scenario, we present a simple and fast method, named DCHE, using dynamic clustering. Also, we design two methods, a Bayesian inference based method and a heuristic method, to detect genome-wide multi-locus epistatic interactions on multiple diseases. For the last scenario, we propose a block-based Bayesian approach to model the LD and conditional disease association simultaneously. Experimental results on both synthetic and real GWAS datasets show that the proposed methods improve the detection accuracy of disease-specific associations and lessen the computational cost compared with current popular methods.

Obesity is a complex and a highly individualized disease and the molecular mechanisms behind this disorder need to be better elucidated. Identification of genes and genetic variants that are involved provide opportunities to establish a genetic understanding of the disease. These findings may also provide more rational approaches to therapy, either by identifying underlying causes or point out the need for different treatments. In addition, the timing and severity of obesity may provide insights into the aetiology of obesity and also identify age-specific determinants of weight gain. Recently, genome-wide association studies have led to a rapid progress in our understanding of the genetic basis of various diseases and candidate genes for obesity have been identified. The overall aim of this thesis was to investigate the genetic impact on severity of childhood obesity and the associations between obesity and genetic variants in the fat mass and obesity associated gene, FTO, and MGAT1, the gene encoding mannosyl (α-1,3-)-glycoprotein β-1,2-N-acetyl-glucosaminyltransferase. We show that the impact of parental body mass index (BMI) on the severity of obesity in children is strengthened as the child grows older, whereas the age at obesity onset is of limited importance. By association studies, we show that single nucleotide polymorphisms downstream MGAT1 influence susceptibility to obesity. Moreover, these variants affect the levels of unsaturated fatty acids and desaturase indices, variables previously shown to correlate with obesity. Furthermore, one variant in the first intronic region of FTO is associated with obesity among children but not with BMI or other measures of adiposity at older ages. However, this variant shows a weight-dependent association with cognitive function among elderly men. By direct sequencing, we identified novel variants in FTO, affecting glucose homeostasis in a BMI-independent manner. Furthermore, we found gender specific effects for FTO, both regarding obesity susceptibility and related phenotypes.

Zugl.: Regensburg, Univ., Diss., 2008

2’ -5’ –oligoadenylate synthetase 1 (OAS1) is an antiviral enzyme that in the presence of double-stranded RNA structures, such as viral genomes or single-stranded RNA transcripts with significant double-stranded character, converts ATP to a series of 2’ -5’ –oligoadenylates (2-5A). 2-5A promotes dimerization of latent ribonuclease (RNaseL) to form catalytically active RNaseL, a candidate hereditary prostate cancer (PCa) gene. RNaseL is anti-proliferative and promotes senescence and apoptosis in PCa cells. Genotyping analysis was completed on over 600 genomic DNA samples from African-American and Caucasian, normal and PCa subjects. Genotyping was performed to screen the following SNPs in the last exon of OAS1 (rs10774671, rs1131476, rs1051042 and rs2660) to determine splicing and linkage disequilibrium (LD) or LD decay in relation to PCa. The rs10774671 GG and AA genotypes generate isoform 1 (p46) and isoform 3 (p48), respectively and were distributed equally in the healthy population. However, in cases, the AA genotype (p46) was significantly associated with PCa risk (OR: 1.80, P-value: < 0.0001). The genotypic frequencies of rs1131476, rs1051042 and rs2660 demonstrated significant LD but showed no association to PCa risk. We also identified protective (AACA, OR =0.06612, P < 0.001) and risk (GACA, OR= 2.31, p Additionally, we utilized two genome-wide association studies analyzing OAS1 and variants found on chromosome 12 to determine their relationship with PCa susceptibility for meta-analysis: This was done to elucidate the role of OAS1 SNPs and chromosome 12 variants in a larger population cohort with PCa susceptibility for a greater understanding of gene to gene interactions. The genome wide association studies used were, the Geneva Multiethnic Genome-wide Scan of Prostate Cancer (MEC), containing 2,841 African-American samples (1,343 cases and 1,498 controls) and 1,660 Japanese/Latino samples (834 cases and 826 controls), as well as Cancer Genetic Markers of Susceptibility (CGEMS) Prostate Cancer-Primary Scan (Stage 1) - PLCO which contains 2,841 samples of European ancestry (1,172 cases and 1,157 controls). We used PLINK, a whole genome association analysis toolset, to extract data on SNPs in association with PCa.

Polycystic ovary syndrome is a complex, heterogeneous disease that affects 5-10% of reproductive-aged women. It is characterized by clinical or biochemical hyperandrogenism, oligo-anovulation, and polycystic ovary morphology. Instigating endocrine findings include aberrantly rapid gonadotropin-releasing hormone pulsatile secretion, elevated luteinizing hormone, sub-optimal levels of follicle-stimulating hormone, and hyperandrogenism. Metabolic symptoms are also present including hyperinsulinemia, insulin resistance, and dyslipidemia. These endocrine and metabolic findings are also accompanied by ovarian dysfunction and improper folliculogenesis. Because aberrant functioning of the hypothalamic-pituitary-gonadal axis is central to the pathophysiology of polycystic ovary syndrome, it is beneficial to examine it for abnormalities. Polycystic ovary syndrome has been shown to have both genetic and environmental components. Its strong genetic component has been demonstrated in twin studies, family association studies, candidate gene studies, and genome-wide association studies. Previous genome-wide association studies have found many candidate genes including those for DENND1A (differentially expressed in normal and neoplastic cells domain containing 1A), THADA (thyroid adenoma-associated protein), FSHR (follicle-stimulating hormone receptor), and LHR (luteinizing hormone receptor). This, together with the central endocrine abnormalities, prompted this review on polymorphisms of receptors of the hypothalamic-pituitary-gonadal axis, including those for gonadotropin-releasing hormone, luteinzing hormone, follicle-stimulating hormone, estrogen, and progesterone, as well as anti-müllerian hormone. Studies on single-nucleotide polymorphisms of these receptors were found on PubMed, Web of Science, and Google Scholar and subsequently analyzed. Many different single-nucleotide polymorphisms were studied, but only a handful of them have been subjected to repeated studies. Only rs2293275 of the luteinizing hormone receptor and rs2349415 of the follicle-stimulating hormone receptor, both at 2p16.3, were found to have a possible role in the etiology of polycystic ovary syndrome, but all eight were found to have a possible phenotypic role: rs13405728, rs2293275, and rs4539842 of the luteinizing hormone receptor; rs6165, rs6166, rs2268361, and rs2349415 of the follicle-stimulating hormone receptor; and rs2002555 of the anti-müllerian hormone receptor. The limitations most affecting the results of these studies include small sample sizes, heterogeneous study populations, lack of generalizability due to ethnicity, and lack of control or adjustment for confounders. It is necessary to develop a standardized study protocol and separate polycystic ovary syndrome patients based on phenotype in order to obtain stronger results in the future.

Understanding how genetic variation propagate to differences in phenotypes in individuals is an ongoing challenge in genetics. Genome-wide association studies have allowed for the identification of many trait-associated genomic loci. However, they are limited in their inability to explain the altered cellular mechanism. Genetic variation can drive disease by altering a range of mechanisms, including signalling networks, TF binding, and protein folding. Understanding the impact of variants on such processes has key implications in therapeutics, drug development, and more. This thesis aims to utilise computational predictors to shed light on how cellular mechanisms are altered in the context of genetic variation and better understand how they drive both molecular and organism-level phenotypes. Many binding events in the cell are mediated by short stretches of sequence motifs. The ability to discover these underlying rules of binding could greatly aid our understanding of variant impact. Kinase–substrate phosphorylation is one of the most prominent post-translational modifications (PTMs) which is mediated by such motifs. We first describe a computational method which utilises interaction and phosphorylation data to predict sequence preferences of kinases. Our method was applied to 57% of human kinases capturing known well-characterised and novel kinase specificities. We experimentally validate four understudied kinases to show that predicted models closely resemble true specificities. We further demonstrate that this method can be applied to different organisms and can be used for other phospho-recognition domains. The described approach allows for an extended repertoire of sequence specificities to be generated, particularly in organisms for which little data is available. TF-DNA binding is another mechanism driven by sequence motifs, which is key for the tight regulation of gene expression and can be greatly altered by genetic variation. We have comprehensively benchmarked current methods used to predict non-coding variant effects on TF-DNA binding by employing over 20,000 compiled allele-specific ChIP-seq variants across 94 TFs. We show that machine learning-based approaches significantly outperform more rudimentary methods such as the position weight matrix. We further note that models for many TFs with distinct binding specificities were unable to accurately assess the impact of variants. For these TFs, we explore alternative mechanisms underlying TF-binding, such as methylation, co-operative binding, and DNA shape that drive poor performance. Our results demonstrate the complexity of predicting non-coding variant effects and the importance of incorporating alternative mechanisms into models. Finally, we describe a comprehensive effort to compile and benchmark state-of-the-art sequence and structure-based predictors of mutational consequences and predict the effect of coding and non-coding variants in the reference genomes of human, yeast, and E. coli. Predicted mechanisms include the impact on protein stability, interaction interfaces, and PTMs. These variant effects are provided through mutfunc, a fast and intuitive web tool by which users can interactively explore pre-computed mechanistic variant impact predictions. We validate computed predictions by analysing known pathogenic disease variants and provide mechanistic hypotheses for causal variants of unknown function. We further use our predictions to devise gene-level functionality scores in human and yeast individuals, which we then used to perform gene-phenotype associations and uncover novel gene-phenotype associations.