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本文引用的文献

1
Statistical analysis strategies for association studies involving rare variants.关联研究中罕见变异体的统计分析策略。
Nat Rev Genet. 2010 Nov;11(11):773-85. doi: 10.1038/nrg2867. Epub 2010 Oct 13.
2
Rare variants create synthetic genome-wide associations.罕见变异导致全基因组关联合成。
PLoS Biol. 2010 Jan 26;8(1):e1000294. doi: 10.1371/journal.pbio.1000294.
3
Analysis of cancer mutation signatures in blood by a novel ultra-sensitive assay: monitoring of therapy or recurrence in non-metastatic breast cancer.新型超敏检测分析血液中的癌症突变特征:监测非转移性乳腺癌的治疗或复发。
PLoS One. 2009 Sep 28;4(9):e7220. doi: 10.1371/journal.pone.0007220.
4
Exploring the unknown: assumptions about allelic architecture and strategies for susceptibility variant discovery.探索未知:等位基因结构的假设和易感性变异发现策略。
Genome Med. 2009 Jul 3;1(7):66. doi: 10.1186/gm66.
5
Discovery of rare variants via sequencing: implications for the design of complex trait association studies.通过测序发现罕见变异:对复杂性状关联研究设计的启示
PLoS Genet. 2009 May;5(5):e1000481. doi: 10.1371/journal.pgen.1000481. Epub 2009 May 15.
6
A groupwise association test for rare mutations using a weighted sum statistic.使用加权和统计量对罕见突变进行分组关联测试。
PLoS Genet. 2009 Feb;5(2):e1000384. doi: 10.1371/journal.pgen.1000384. Epub 2009 Feb 13.
7
Identifying rarer genetic variants for common complex diseases: diseased versus neutral discovery panels.识别常见复杂疾病的罕见基因变异:患病与中性发现面板。
Ann Hum Genet. 2009 Jan;73(1):54-60. doi: 10.1111/j.1469-1809.2008.00483.x.
8
Genetic mapping in human disease.人类疾病中的基因定位
Science. 2008 Nov 7;322(5903):881-8. doi: 10.1126/science.1156409.
9
Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data.检测常见疾病与罕见变异关联的方法:在序列数据分析中的应用。
Am J Hum Genet. 2008 Sep;83(3):311-21. doi: 10.1016/j.ajhg.2008.06.024. Epub 2008 Aug 7.
10
Genome-wide association studies for complex traits: consensus, uncertainty and challenges.复杂性状的全基因组关联研究:共识、不确定性与挑战。
Nat Rev Genet. 2008 May;9(5):356-69. doi: 10.1038/nrg2344.

病例对照关联研究中三种组合基因分型和重测序的方法。

Three ways of combining genotyping and resequencing in case-control association studies.

机构信息

Division of Biostatistics, City of Hope, Duarte, California, United States of America.

出版信息

PLoS One. 2010 Dec 20;5(12):e14318. doi: 10.1371/journal.pone.0014318.

DOI:10.1371/journal.pone.0014318
PMID:21187953
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3004857/
Abstract

We describe three statistical results that we have found to be useful in case-control genetic association testing. All three involve combining the discovery of novel genetic variants, usually by sequencing, with genotyping methods that recognize previously discovered variants. We first consider expanding the list of known variants by concentrating variant-discovery in cases. Although the naive inclusion of cases-only sequencing data would create a bias, we show that some sequencing data may be retained, even if controls are not sequenced. Furthermore, for alleles of intermediate frequency, cases-only sequencing with bias-correction entails little if any loss of power, compared to dividing the same sequencing effort among cases and controls. Secondly, we investigate more strongly focused variant discovery to obtain a greater enrichment for disease-related variants. We show how case status, family history, and marker sharing enrich the discovery set by increments that are multiplicative with penetrance, enabling the preferential discovery of high-penetrance variants. A third result applies when sequencing is the primary means of counting alleles in both cases and controls, but a supplementary pooled genotyping sample is used to identify the variants that are very rare. We show that this raises no validity issues, and we evaluate a less expensive and more adaptive approach to judging rarity, based on group-specific variants. We demonstrate the important and unusual caveat that this method requires equal sample sizes for validity. These three results can be used to more efficiently detect the association of rare genetic variants with disease.

摘要

我们描述了三个在病例对照遗传关联测试中发现有用的统计结果。这三个结果都涉及到将新的遗传变异的发现(通常通过测序)与识别先前发现的变异的基因分型方法结合起来。我们首先考虑通过在病例中集中进行变异发现来扩展已知变异的列表。虽然仅包含病例的测序数据的简单包含会产生偏差,但我们表明,即使未对对照进行测序,也可以保留一些测序数据。此外,对于中等频率的等位基因,与将相同的测序工作分配给病例和对照相比,仅对病例进行测序并进行偏差校正几乎不会损失任何功效。其次,我们研究了更集中的变异发现,以获得更多与疾病相关的变异的富集。我们展示了病例状态、家族史和标记共享如何通过与外显率相乘的增量来丰富发现集,从而能够优先发现高外显率的变异。第三个结果适用于测序是病例和对照中计数等位基因的主要手段,但使用补充的 pooled genotyping 样本来识别非常罕见的变异。我们表明这不会引起有效性问题,并且我们评估了一种更经济且更适应的方法来判断稀有性,基于特定群体的变异。我们证明了一个重要且不寻常的警告,即这种方法需要有效性的相等样本量。这三个结果可以用于更有效地检测罕见遗传变异与疾病的关联。