Using familial information for variant filtering in high-throughput sequencing studies.

Using familial information for variant filtering in high-throughput sequencing studies.
复制标题

DOI:
10.1007/s00439-014-1479-4
复制
发表时间:
2014-11
期刊:
影响因子:
5.3
通讯作者:
Smith, Katherine R.
Smith, Katherine R.
中科院分区:
生物学2区
文献类型:
--
作者:
Bahlo, Melanie;Tankard, Rick;Lukic, Vesna;Oliver, Karen L.;Smith, Katherine R.

文献摘要

参考文献

被引文献

相似文献

高通量测序研究(HTS)在确定人类疾病的遗传原因方面非常成功,特别是那些遵循孟德尔遗传的疾病。迄今为止,许多HTS研究都是在没有利用样本之间可用的家族关系的情况下进行的。在这里,我们讨论了许多优点和偶尔的陷阱,使用身份的血统信息结合HTS研究。这些方法不仅适用于家庭的研究,但也是有用的,在队列中显然无关的,“散发”的情况下,小家庭动力不足的联系,并允许推断个人之间的关系。纳入家族/谱系信息不仅为通常由HTS生成的广泛变异列表提供了强大的过滤选项,而且还允许有价值的质量控制检查、对遗传模型的洞察和感兴趣个体的基因型状态。特别是,这些方法对于HTS分析中具有挑战性的发现场景是有价值的,例如在通常用于过滤的变体数据库中表现不佳的群体的研究中,以及在质量差的HTS数据的情况下。
High-throughput sequencing studies (HTS) have been highly successful in identifying the genetic causes of human disease, particularly those following Mendelian inheritance. Many HTS studies to date have been performed without utilizing available family relationships between samples. Here, we discuss the many merits and occasional pitfalls of using identity by descent information in conjunction with HTS studies. These methods are not only applicable to family studies but are also useful in cohorts of apparently unrelated, ‘sporadic’ cases and small families underpowered for linkage and allow inference of relationships between individuals. Incorporating familial/pedigree information not only provides powerful filtering options for the extensive variant lists that are usually produced by HTS but also allows valuable quality control checks, insights into the genetic model and the genotypic status of individuals of interest. In particular, these methods are valuable for challenging discovery scenarios in HTS analysis, such as in the study of populations poorly represented in variant databases typically used for filtering, and in the case of poor-quality HTS data.
DOI: 10.1038/ng.2555
发表时间: 2013-03-01
期刊: NATURE GENETICS
影响因子: 30.8
作者:
Gratten, Jacob;Visscher, Peter M.;Wray, Naomi R.
通讯作者: Wray, Naomi R.
DOI: 10.1002/gepi.20378
发表时间: 2009-04-01
影响因子: 2.1
作者:
Albrechtsen, Anders;Korneliussen, Thorfinn Sand;Nielsen, Rasmus
通讯作者: Nielsen, Rasmus
DOI: 10.1093/bioinformatics/btu149
发表时间: 2014-07-01
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Gazal, Steven;Sahbatou, Mourad;Leutenegger, Anne-Louise
通讯作者: Leutenegger, Anne-Louise
DOI: 10.1089/cmb.2012.0084
发表时间: 2012-06-01
影响因子: 1.7
作者:
Aguiar, Derek;Istrail, Sorin
通讯作者: Istrail, Sorin
DOI: 10.1016/j.ajhg.2012.07.019
发表时间: 2012-09-07
影响因子: 9.8
作者:
Guergueltcheva, Velina;Azmanov, Dimitar N.;Kalaydjieva, Luba
通讯作者: Kalaydjieva, Luba