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首页> 外文期刊>Annual Review of Statistics and Its Application >Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data
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Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data

机译：下一代统计遗传学：高维数据的建模，惩罚和优化

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摘要

Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing, and this transition is only accelerating with the advent of inexpensive DNA sequencing technology. This brief review highlights some modern techniques with recent successes in statistical genetics. These include (a) Lasso penalized regression for association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence imputation, (d) the fused Lasso for discovery of copy number variation, (e) haplotyping, (f) relatedness estimation, (g) variance components models, and (h) rare variant testing. For more than a century, genetics has been both a driver and beneficiary of statistical theory and practice. This symbiotic relationship will persist for the foreseeable future.
机译：统计遗传学正在向应用大数据统计的所有分支经历相同的过渡，并且随着廉价DNA测序技术的出现，这种过渡正在加速。这篇简短的综述着重介绍了一些现代技术，这些技术在统计遗传学领域取得了近期成功。其中包括（a）关联映射的套索惩罚回归，（b）种族混合估计，（c）基因型和序列插补的矩阵完成，（d）融合的套索用于发现拷贝数变异，（e）单倍型分析，（f ）相关性估算，（g）方差成分模型和（h）稀有变异测试。一个多世纪以来，遗传学一直是统计理论和实践的推动者和受益者。这种共生关系将在可预见的将来持续下去。

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