Genome wide association studies (GWAS) and their subsequent meta-analysis have identified a large number of susceptibility variants for Type 2 diabetes (T2D) risk. However, the familial aggregation seen in this disease is not yet fully explained. The sibling relative risk (λ_s) due to all known variants is ~1.104 which is well below the epidemiological estimates of λ_s of ~3.0. There has therefore been great interest in the potential role of variants that would have been largely invisible to the initial wave of GWAS and linkage approaches. Low frequency (minor allele frequency 1-5%), incompletely penetrant (odds ratio 2-4) variants (LFIP), are one such group of potential susceptibility variants. The overall objective of this project (designed and implemented in 2007-2010) was to evaluate the contribution of LFIP variants to the inherited susceptibility to T2D. I tested the specific hypothesis that genes already-implicated in diabetes pathogenesis (due to an established role in monogenic or multifactorial disease) also harbour LFIP variants, and that those variants may contribute appreciably to the prediction of disease risk. Mutations in exons only encoding isoform-A of HNF1A have been demonstrated to lead to a later age of diagnosis of HNF1A-MODY. This region was therefore felt to be auspicious for harbouring LFIP variants impacting on T2D risk. I have demonstrated that such variants impacting on T2D risk are unlikely to be present in this region by use of Sanger sequencing in a sample enriched for young onset, familial T2D. The role in T2D risk of candidate LFIP variants across 5 genes (HNF1A, HNF4A, PDX1, KCNJ15 and LARS2), was evaluated by large scale association studies. For one variant, T130I of HNF4A, a modest association (p=5x10^-4) with T2D was seen in UK samples and the strength of association was marginally improved by incorporation of all previous studies of this variant in T2D in a meta-analysis (p=2.1x10^-5). This study demonstrated the difficulties encountered in confirming the association of low frequency variants to complex diseases, especially for those with modest effect sizes. At the time of project design and inception “next-generation” sequencing platforms were in their infancy and the study design I planned (that of pooled, targeted sequencing) had not been widely applied. It was therefore necessary to design and optimise protocols for sample preparation for sequencing on this platform. I used the Genome Analyzer II platform to sequence ten genes previously implicated in T2D or monogenic diabetes pathogenesis in pooled DNA samples. This approach yielded in excess of 2900 variants, a large portion being novel. As part of this project I have highlighted heuristics that can be used in the follow-up of potential susceptibility variants discovered using high throughput sequencing. I have also established protocols and pathways for sample preparation that can be utilised across several next generation sequencing platforms for future studies in the host institution and beyond.

京都大学 / 新制・課程博士 / 博士(医学) / 甲第23772号 / 医博第4818号 / 新制||医||1056(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 村川 泰裕, 教授 松田 文彦, 教授 篠原 隆司 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

Indiana University-Purdue University Indianapolis (IUPUI) / Reliable detection of low-frequency single nucleotide variants (SNVs) carries great significance in many applications. In cancer genetics, the frequencies of somatic variants from tumor biopsies tend to be low due to contamination with normal tissue and tumor heterogeneity. Circulating tumor DNA monitoring also faces the challenge of detecting low-frequency variants due to the small percentage of tumor DNA in blood. Moreover, in population genetics, although pooled sequencing is cost-effective compared with individual sequencing, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by multiple sources of errors, especially next-generation sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 5%; most fail to consider differential, context-specific sequencing artifacts. To face this challenge, we developed a computational and experimental framework, RareVar, to reliably identify low-frequency SNVs from high-throughput sequencing data. For optimized performance, RareVar utilized a supervised learning framework to model artifacts originated from different components of a specific sequencing pipeline. This is enabled by a customized, comprehensive benchmark data enriched with known low-frequency SNVs from the sequencing pipeline of interest. Genomic-context-specific sequencing error model was trained on the benchmark data to characterize the systematic sequencing artifacts, to derive the position-specific detection limit for sensitive low-frequency SNV detection. Further, a machine-learning algorithm utilized sequencing quality features to refine SNV candidates for higher specificity. RareVar outperformed existing approaches, especially at 0.5% to 5% frequency. We further explored the influence of statistical modeling on position specific error modeling and showed zero-inflated negative binomial as the best-performed statistical distribution. When replicating analyses on an Illumina MiSeq benchmark dataset, our method seamlessly adapted to technologies with different biochemistries. RareVar enables sensitive detection of low-frequency SNVs across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.

Ankylosing spondylitis (AS) is a common inflammatory arthritis of the spine and other affected joints, which is highly heritable, being strongly influenced by the HLA-B27 status, as well as hundreds of mostly unknown genetic variants of smaller effect. The aim of my research was to confirm some of the previously observed genetic associations and to identify new associations, many of which are in biological pathways relevant to AS pathogenesis, most notably the IL-23/T_H17 axis (IL23R) and antigen presentation (ERAP1 and ERAP2). Studies presented in this thesis include replication and refinement of several potential associations initially identified by earlier GWAS (WTCCC-TASC, 2007 and TASC, 2010). I conducted an extended study of IL23R association with AS and undertook a meta-analysis, confirming the association between AS and IL23R (non-synonymous SNP rs11209026, p=1.5 x 10-9, OR=0.61). An extensive re-sequencing and fine mapping project, including a meta-analysis, to replicate and refine the association of TNFRSF1A with AS was also undertaken; a novel variant in intron 6 was identified and a weak association with a low frequency variant, rs4149584 (p=0.01, OR=1.58), was detected. Somewhat stronger associations were seen with rs4149577 (p=0.002, OR=0.91) and rs4149578 (p=0.015, OR=1.14) in the meta-analysis. Associations at several additional loci had been identified by a more recent GWAS (WTCCC2-TASC, 2011). I used in silico techniques, including imputation using a denser panel of variants from the 1000 Genomes Project, conditional analysis and rare/low frequency variant analysis, to refine these associations. Imputation analysis (1782 cases/5167 controls) revealed novel associations with ERAP2 (rs4869313, p=7.3 x 10-8, OR=0.79) and several additional candidate loci including IL6R, UBE2L3 and 2p16.3. Ten SNPs were then directly typed in an independent sample (1804 cases/1848 controls) to replicate selected associations and to determine the imputation accuracy. I established that imputation using the 1000 Genomes Project pilot data was largely reliable, specifically for common variants (genotype concordence~97%). However, more accurate imputation of low frequency variants may require larger reference populations, like the most recent 1000 Genomes reference panels. The results of my research provide a better understanding of the complex genetics of AS, and help identify future targets for genetic and functional studies.