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Next Generation Statistical Genetics: Modeling, Penalization, and Optimization in
High-Dimensional Data
#MMPMID24955378
Lange K
; Papp JC
; Sinsheimer JS
; Sobel EM
Annu Rev Stat Appl
2014[Jan]; 1
(1
): 279-300
PMID24955378
show ga
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.
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