Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26866487
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26866487
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 BMC+Genet
2016 ; 17 Suppl 2
(Suppl 2
): 6
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Progress in methods for rare variant association
#MMPMID26866487
Santorico SA
; Hendricks AE
BMC Genet
2016[Feb]; 17 Suppl 2
(Suppl 2
): 6
PMID26866487
show ga
Empirical studies and evolutionary theory support a role for rare variants in the
etiology of complex traits. Given this motivation and increasing affordability of
whole-exome and whole-genome sequencing, methods for rare variant association
have been an active area of research for the past decade. Here, we provide a
survey of the current literature and developments from the Genetics Analysis
Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we
present the generalized linear regression framework and associated score
statistic for the 2 major types of methods: burden and variance components
methods. We further show that by simply modifying weights within these frameworks
we arrive at many of the popular existing methods, for example, the cohort
allelic sums test and sequence kernel association test. Meta-analysis techniques
are also described. Next, we describe the 6 contributions from the GAW19
Collapsing Rare Variants working group. These included development of new
methods, such as a retrospective likelihood for family data, a method using
genomic structure to compare cases and controls, a haplotype-based meta-analysis,
and a permutation-based method for combining different statistical tests. In
addition, one contribution compared a mega-analysis of family-based and
population-based data to meta-analysis. Finally, the power of existing
family-based methods for binary traits was compared. We conclude with suggestions
for open research questions.
free
free
free
