Schork, Nicholas J.

Tiwari, Hemant K.

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Hemant K Tiwari

Nicholas J Schork

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Frontiers in Genetics

Grand Challenges in Statistical Genetics and Methodology

Hemant K. Tiwari

Nicholas J. Schork

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Grand Challenges in Statistical Genetics/Genomics Methodology

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www.frontiersin.org February 2011 | Volume 2 | Article 5 | 1
Specialty Grand challenGe article
published: 24 February 2011
doi: 10.3389/fgene.2011.00005
Grand challenges in statistical genetics/genomics 
methodology
Hemant K. Tiwari1* and Nicholas J. Schork2*
1 Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
2 The Scripps Translational Science Institute, Department of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, CA, USA
*Correspondence: htiwari@uab.edu; nschork@scripps.edu
Recent developments in genomic technol-
ogies have provided researchers with an 
unprecedented ability to probe the genetic 
basis of complex biological processes as 
well as phenotypic expression. However, 
as impressive as these technologies are, the 
analysis and interpretation of the data they 
generate is exceptionally challenging due 
to the amount and sophistication of these 
data. In fact, it has been said of next-gener-
ation genomic technologies that they often 
result in situations in which researchers are 
“drowning in data, but thirsting for knowl-
edge.” Frontiers in Statistical Genetics and 
Methodology (FSGM) will provide a forum 
for theoretically minded statistical geneti-
cists to publish methodology in an effort to 
help quench this thirst. In this context, as 
the senior editors of FSGM we feel that there 
are a number of important areas in modern 
statistical genomics that have immediate 
needs for methodological developments. 
We outline some of these below, but by no 
means want to convey the sense that FSGM 
will only publish papers dealing with these 
areas, as the field of statistical genomics is 
simply too broad and filled with too many 
challenges and needs to restrict attention of 
FSGM to a few thematic areas.
Methodologies for new data 
types
Newer assays, especially DNA sequencing-
based assays such as variant identification 
sequencing, RNA sequencing (“RNA-seq”) 
for digital gene expression assays, chro-
matin immunoprecipitation sequencing 
(“ChIP-seq”) for DNA–protein interaction 
analysis, antibody sequencing for immuno-
logical diversity analyses, methylation, and 
bisulfate sequencing (“Methyl-seq”) for epi-
genomic assays, and many other assay types 
exploring the transcriptome, proteome, 
metabolome, etc., all require very diligent 
and careful analysis of the raw data that they 
produce. This more “upstream” analysis of 
modern genomic assay results is crucial if 
the more “downstream” analysis of the data 
(e.g., drawing inferences from the data) is 
to not suffer from the proverbial “garbage 
in, garbage out” principle. Better statistical 
analysis methods that take into account dif-
ferent sources of error during the process-
ing stages of genomic assay data are sorely 
needed and will continue to be a needed as 
technologies are improved, extended, and 
replaced with more sophisticated ones.
approaches to integration of 
different data types
Following the singular development, imple-
mentation, and application of specific 
genomic assays, is the combined use of those 
assays to address specific questions. For 
example, one may leverage DNA sequenc-
ing and transcriptomic or proteomic assays 
to identify “expression quantitative trait 
loci (eQTLs)” or “protein QTLs (pQTLs).” 
Drawing appropriate inferences from the 
combined set of assays is not trivial, as it 
requires not only a familiarity with mod-
els and methods for handling sources of 
error associated with each assay, but also 
an ability to model the relevant system as 
a “whole” above its assayable “parts.” Such 
modeling can pose very tricky problems for 
the  statistical geneticists.
Modeling population-level 
phenoMena
Population genetics is a ubiquitous bio-
medical science and not confined to the, 
e.g., ecological or genetic epidemiological 
sciences. For example, understanding how 
populations of cells harboring mutations 
contribute to tumorigenesis, how differ-
ent antibody “species” defined by DNA 
sequence diversity contribute to immune 
responses, and how proteins harboring 
different amino acid sequences may influ-
ence fundamental molecular physiological 
interactions, all require a population per-
spective. Thus, statistical methodologies 
for assembling and studying networks, the 
flow, and transmission of information from, 
e.g., generation to generation, the hierar-
chical functioning of gene regulatory cir-
cuits, and related phenomena will become 
 increasingly important.
predictive Modeling
The availability of genetic assays results 
that pertain to phenotypic categories [e.g., 
genome wide association study (GWAS) 
findings with respect to clinical diagnoses; 
gene expression patterns that differentiate 
more or less aggressive tumors; selection 
studies to produce more fruitful livestock; 
proteomics or metabolite profiles indicative 
of drug response in cell line or clinical stud-
ies; etc.] create a need for the development 
of predictive models and classifiers for the 
phenotypic categories. Such models can be 
used by applications-oriented researchers 
in a wide variety of settings (e.g., select-
ing livestock for breeding; facilitating drug 
discovery; clinical biomarker use; etc.) but 
are complicated by a number of statistical 
issues, such as having more predictors/vari-
ables than units of observations, the need 
to accommodate potential confounding by 
covariates, dealing with mixed longitudinal 
data, etc.
Methodologies for cross-species 
data
Genomic analyses are, in a broad sense, 
rooted in evolutionary concepts and theory, 
given the relationships among species at the 
level of DNA. Explicitly exploiting this fact 
in research paradigms is an important area 
for statistical genetics concentration. For 
example, developing better analysis methods 
for measuring and leveraging conservation 
of DNA sequence in cross-species studies, 
developing statistical methods for lever-
aging pathway and genetic network data 
from one species in the study of another, 
and assessing homologies at levels beyond, 
but maybe dictated to some degree by, DNA 
sequence similarities (e.g.,  physiologic 
Tiwari and Schork Challenges in statistical genetics
Frontiers in Genetics | Statistical Genetics and Methodology  February 2011 | Volume 2 | Article 5 | 2
to the community and believe that FSGM 
will provide a premier vehicle for meeting 
this challenge.
Received: 04 January 2011; accepted: 10 February 2011; 
published online: 24 February 2011.
Citation: Tiwari HK and Schork NJ (2011) Grand chal-
lenges in statistical genetics/genomics methodology. Front. 
Gene. 2:5. doi: 10.3389/fgene.2011.00005
This article was submitted to Frontiers in Statistical Genetics 
and Methodology, a specialty of Frontiers in Genetics.
Copyright © 2011 Tiwari and Schork. This is an open-
access article subject to an exclusive license agreement 
between the authors and Frontiers Media SA, which per-
mits unrestricted use, distribution, and reproduction in 
any medium, provided the original authors and source 
are credited.
for the development of better field designs 
in agricultural genomics, the incorpora-
tion of contrasting strains of model organ-
isms to assess genetic background effects 
in functional genomic studies, the design 
of efficient gene-based clinical trials in 
advancing “personalized medicine” and 
related activities should be emphasized by 
the community.
As noted, the foregoing problem areas 
are but a handful of the problem areas sta-
tistical geneticists currently face and will 
face in the future. We see these problems as 
both a call-to-action and a stimulating, yet 
seemingly daunting, intellectual challenge 
processes, phenotype profiles, etc.) are fast 
becoming target areas for researchers in the 
sequencing era.
design of studies pertinent to 
genetics
The availability of cost–effective genomic 
technologies can often lull scientists into a 
“collect data first and ask questions later” 
mentality that might be detrimental to good 
science. Good study designs should never be 
eschewed for more data-generation horse-
power. In fact, good study designs should go 
hand-in-hand with the use of more power-
ful technologies and assays. Thus, the need 


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