Lange Kenneth, Papp Jeanette C, Sinsheimer Janet S, Sobel Eric M
Depts of Biomathematics, Human Genetics, and Statistics, UCLA.
Dept of Human Genetics, UCLA.
Annu Rev Stat Appl. 2014 Jan 1;1(1):279-300. doi: 10.1146/annurev-statistics-022513-115638.
Statistical genetics is undergoing the same transition to big data that all branches of applied statistics are experiencing. With the advent of inexpensive DNA sequencing, the transition is only accelerating. This brief review highlights some modern techniques with recent successes in statistical genetics. These include: (a) lasso penalized regression and association mapping, (b) ethnic admixture estimation, (c) matrix completion for genotype and sequence data, (d) the fused lasso and copy number variation, (e) haplotyping, (f) estimation of relatedness, (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.
统计遗传学正在经历与应用统计学所有分支相同的向大数据的转变。随着廉价DNA测序技术的出现，这种转变正在加速。本简要综述重点介绍了一些在统计遗传学中取得近期成功的现代技术。这些技术包括：（a）套索惩罚回归与关联图谱分析，（b）族群混合估计，（c）基因型和序列数据的矩阵补全，（d）融合套索与拷贝数变异，（e）单倍型分型，（f）亲缘关系估计，（g）方差成分模型，以及（h）罕见变异检测。一个多世纪以来，遗传学一直是统计理论与实践的推动者和受益者。这种共生关系在可预见的未来将持续存在。
