160
B. F. Voight
7.1
Introduction
The last 15 years has witnessed a paradigm shift in studies of complex phenotypes
in humans. The basic idea—that by surveying a large number of genetic markers
across the entire genome in a large number of individuals, markers linked to genes
that associate with complex traits or disease can be identified—was envisioned
almost a hundred years ago by the scientific giants Hermann Muller, Thomas
Morgan, and Alfred Sturtevant, among others. Despite the transformative features
the approach has had on scientific activity and productivity, the adoption of
genome-wide association studies (GWAS) has not been completely uncontroversial
within scientific and public spheres (Wade 2009; Goldstein 2009; Hirschhorn 2009;
Visscher et al. 2012). While answers to any scientific question often spawn still
deeper levels of inquiry, the debate within the scientific community about the
magnitude of discovery which has been emitted from GWAS certainly should
not be misconstrued by a broader audience to mean that the activity was without
meaning or scientific value. In the midst of this discussion, a retrospective pause
is warranted to consider how the field has evolved and what has been learned.
Such insight will fully advantage the next set of experiments—in addition to and
beyond GWAS—designed to advance the biological and genetic understanding of
these conditions.
Several critical advances and insights have been made in the process of performing these studies. First, the process of deploying genome-wide studies required a
number of critical insights about how to design such studies, statistical approaches
and uniform tools to analyze the data generated from them, and best practices on
how to integrate analysis across multiple data sets into clear queries of association,
variant by variant. After these technical best practices were worked out in detail,
the application of the experimental process has generated a number of key insights
about the architecture of complex traits, the underlying biology, and the potential
mechanisms of action. A fundamental second message is that the application of
these studies has led unambiguously to the conclusion that common disease can
be studied, understood, and characterized through systematic studies of common
genetic variants. A third message is that these common variants are linked to causal
ones and that they explain a fraction of heritability (though not completely); an
observation that underscores the heavily polygenic nature of these phenotypes. And
finally, as specific examples of genes and mechanisms are elucidated (Musunuru
et al. 2010), there is increasing evidence that networks underlying complex traits
are emerging (Cotsapas et al. 2011), demonstrating that mechanisms of disease
etiology and the pathways contributing to them can be identified from genome-wide
association signals.
In the following, I describe the progression of the genome-wide approach toward
the dissection of complex traits and the insights obtained over the past and present.
My intention in these sections is not to build a protective bulwark defending the
genome-wide association approach as somehow the “best value” for the dollar
spent on the approach. Rather, my goal is to highlight specific findings where the
B. F. Voight
7.1
Introduction
The last 15 years has witnessed a paradigm shift in studies of complex phenotypes
in humans. The basic idea—that by surveying a large number of genetic markers
across the entire genome in a large number of individuals, markers linked to genes
that associate with complex traits or disease can be identified—was envisioned
almost a hundred years ago by the scientific giants Hermann Muller, Thomas
Morgan, and Alfred Sturtevant, among others. Despite the transformative features
the approach has had on scientific activity and productivity, the adoption of
genome-wide association studies (GWAS) has not been completely uncontroversial
within scientific and public spheres (Wade 2009; Goldstein 2009; Hirschhorn 2009;
Visscher et al. 2012). While answers to any scientific question often spawn still
deeper levels of inquiry, the debate within the scientific community about the
magnitude of discovery which has been emitted from GWAS certainly should
not be misconstrued by a broader audience to mean that the activity was without
meaning or scientific value. In the midst of this discussion, a retrospective pause
is warranted to consider how the field has evolved and what has been learned.
Such insight will fully advantage the next set of experiments—in addition to and
beyond GWAS—designed to advance the biological and genetic understanding of
these conditions.
Several critical advances and insights have been made in the process of performing these studies. First, the process of deploying genome-wide studies required a
number of critical insights about how to design such studies, statistical approaches
and uniform tools to analyze the data generated from them, and best practices on
how to integrate analysis across multiple data sets into clear queries of association,
variant by variant. After these technical best practices were worked out in detail,
the application of the experimental process has generated a number of key insights
about the architecture of complex traits, the underlying biology, and the potential
mechanisms of action. A fundamental second message is that the application of
these studies has led unambiguously to the conclusion that common disease can
be studied, understood, and characterized through systematic studies of common
genetic variants. A third message is that these common variants are linked to causal
ones and that they explain a fraction of heritability (though not completely); an
observation that underscores the heavily polygenic nature of these phenotypes. And
finally, as specific examples of genes and mechanisms are elucidated (Musunuru
et al. 2010), there is increasing evidence that networks underlying complex traits
are emerging (Cotsapas et al. 2011), demonstrating that mechanisms of disease
etiology and the pathways contributing to them can be identified from genome-wide
association signals.
In the following, I describe the progression of the genome-wide approach toward
the dissection of complex traits and the insights obtained over the past and present.
My intention in these sections is not to build a protective bulwark defending the
genome-wide association approach as somehow the “best value” for the dollar
spent on the approach. Rather, my goal is to highlight specific findings where the