Shared Genomic Segment Analysis for Localizing Multiple Myeloma Genes

描述(申请人提供)：多发性骨髓瘤(MM)是一种血液系统恶性肿瘤。它是一种浆细胞癌，其特征是恶性浆细胞在骨髓中聚集，并增加单克隆性免疫球蛋白的产生。它占所有癌症的1%，通常是无法治愈的，中位生存期约为四年。它约占血液系统恶性肿瘤死亡的20%，占所有癌症死亡的2%。最近的研究表明，多发性骨髓瘤的发病率可能会增加，并且发生在更早的年龄。多发性骨髓瘤的病因尚不清楚，但遗传成分的证据是令人信服的。特别是，有强有力的证据表明，存在罕见的风险变种。在人群归因风险方面，大效应大小的罕见风险变异可能相对不显著，但与被发现携带此类变异的个人直接相关。发现一种罕见的多发性骨髓瘤风险变异将立即对临床产生影响。这项提案将重点放在绘制这些罕见的变种上。我们描述了一种高危家系设计和一种新的分析技术，这种技术是专门开发的，具有很好的检测罕见风险变异的能力。相比之下，流行的病例对照关联设计几乎没有或几乎没有影响力。因此，我们的研究将对这一领域有利，并与其他正在进行的将MM常见变体本地化的努力高度互补，因为它拓宽了可能被识别的变体类型的范围。我们将使用独特而强大的犹他州人口数据库(UPDB)，该数据库汇集了基于人口的家谱和癌症登记数据，以识别扩展的高危多发性骨髓瘤家系进行研究。我们将使用我们的新的共享基因组片段方法进行分析。这一分析只考虑了抽样的、远亲的多发性骨髓瘤病例，并提出了一个问题，即任何共享的基因组片段的长度是否偶然比预期的要长。从理论上讲，在远亲之间分享机会是非常不可能的。MM患者之间至少有15个减数分裂的高危家系有能力识别全基因组，具有统计学意义的共享。因此，这些地区很有可能存在罕见的风险变异。我们为多发性骨髓瘤绘制风险变量图的创新方法是高风险的，因为以前从未尝试过，尽管我们相信初步研究清楚地表明，这种方法具有极好的潜力。如果成功，该建议的高回报是显而易见的，无论是在MM的基因定位方面，还是在我们的新方法在一般的基因发现中发挥有价值的作用的原则证明方面。这项建议的直接目标是将MM的罕见风险变量本地化，最终目标是确定特定的风险变量。这些发现的影响将是巨大的，并将导致在治疗和控制这种致命疾病方面取得重大进展。特别是，即使是识别单个罕见的、对MM有较大影响的遗传生物标记物，也将是MM病因学中的一个重要和关键的发现，并可能对检测和诊断具有直接的临床意义。 公共卫生相关性：该项目将使用新的方法来定位多发性骨髓瘤的稀有遗传生物标记物。如果成功，对高危家系中的高危个体的临床意义将是多发性骨髓瘤的早期发现和诊断。一般来说，遗传危险因素的知识有助于增加我们对疾病病因学的理解。归根结底，潜在的公共卫生影响是在MM的检测、诊断、干预、治疗和预防方面的重大改进。

DESCRIPTION (provided by applicant): Multiple myeloma (MM) is a hematologic malignancy. It is a cancer of the plasma cells characterized by the accumulation of malignant plasma cells in the bone marrow and increased production of a monoclonal immunoglobulin. It represents 1% of all cancers and is usually incurable with a median survival of about four years. It accounts for approximately 20% of deaths from hematologic malignancies and 2% of deaths from all cancers. Recent studies indicate that MM may be increasing in incidence and occurring at earlier ages. The etiology of MM is unclear; however evidence for a genetic component is compelling. In particular, there is strong evidence to suggest the existence of rare risk variants. Rare risk variants of large effect size may be relatively insignificant with regard to population attributable risk, but have immediate relevance for individuals identified to carry such variants. The discovery of a single rare risk variant for MM would provide immediate clinical impact. This proposal will focus on mapping these rare variants. We describe a high-risk pedigree design and a novel analysis technique that is specifically developed and has good power to detect rare risk variants. In contrast, the popular case-control association design has little or no power. Our study will therefore be advantageous to the field, and is highly complementary to other on-going efforts to localize common variants for MM, because it broadens the scope of the types of variants that may be identified. We will use the unique and powerful Utah Population Database (UPDB) that brings together population-based genealogical and cancer registry data to identify extended high-risk MM pedigrees to study. We will use our novel shared genomic segment method for analysis. This analysis considers only sampled, distantly related MM cases and poses the question as to whether the length of any shared genomic segment is longer than expected by chance. Theoretically, chance sharing in distant relatives is extremely improbable. High-risk pedigrees with at least 15 meioses between MM cases have the capacity for identifying genome wide statistically significant sharing. These regions are therefore good candidates for harboring rare risk variants. Our innovative approach to mapping risk variants for MM is high-risk because it has not been attempted previously, although we believe our preliminary studies clearly indicate that this approach has excellent potential. If successful, the high pay-off of the proposal is evident both in terms of gene localization for MM and in the proof-of-principal that our novel approach has a valuable role to play in gene finding in general. The immediate goal of this proposal is to localize rare risk variants for MM. The ultimate goal will be to identify the specific risk variants. The impact of such discoveries will be high and will lead to significant advances in treatment and control of this fatal disease. In particular, the identification of even a single rare, genetic biomarker with large effect size for MM, will be an important and critical discovery in the etiology of MM and could have immediate clinical relevance for detection and diagnosis. PUBLIC HEALTH RELEVANCE: This project will use novel methods to localize rare genetic biomarkers for multiple myeloma. If successful, the clinical implication for at-risk individuals in high-risk pedigrees would be the early detection and diagnosis of multiple myeloma. Knowledge of genetic risk factors is informative in general for increasing our understanding of disease etiology. Ultimately, the potential public health implications are significant improvements in detection, diagnosis, intervention, treatment and prevention of MM.