Greg Gibson

Finding the missing heritability of complex diseases

Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, ‘missing’ heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models

The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.

Rare and common variants: twenty arguments

Genome-wide association studies have greatly improved our understanding of the genetic basis of disease risk. The fact that they tend not to identify more than a fraction of the specific causal loci has led to divergence of opinion over whether most of the variance is hidden as numerous rare variants of large effect or as common variants of very small effect. Here I review 20 arguments for and against each of these models of the genetic basis of complex traits and conclude that both classes of effect can be readily reconciled.

The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster

Here we present a statistically rigorous approach to quantifying microarray expression data that allows the relative effects of multiple classes of treatment to be compared and incorporates analytical methods that are common to quantitative genetics. From the magnitude of gene effects and contributions of variance components, we find that gene expression in adult flies is affected most strongly by sex, less so by genotype and only weakly by age (for 1- and 6-wk flies); in addition, sex × genotype interactions may be present for as much as 10% of the Drosophila transcriptome. This interpretation is compromised to some extent by statistical issues relating to power and experimental design. Nevertheless, we show that changes in expression as small as 1.2-fold can be highly significant. Genotypic contributions to transcriptional variance may be of a similar magnitude to those relating to some quantitative phenotypes and should be considered when assessing the significance of experimental treatments.

PERSPECTIVE:EVOLUTION AND DETECTION OF GENETIC ROBUSTNESS

Abstract Robustness is the invariance of phenotypes in the face of perturbation. The robustness of phenotypes appears at various levels of biological organization, including gene expression, protein folding, metabolic flux, physiological homeostasis, development, and even organismal fitness. The mechanisms underlying robustness are diverse, ranging from thermodynamic stability at the RNA and protein level to behavior at the organismal level. Phenotypes can be robust either against heritable perturbations (e.g., mutations) or nonheritable perturbations (e.g., the weather). Here we primarily focus on the first kind of robustness—genetic robustness—and survey three growing avenues of research: (1) measuring genetic robustness in nature and in the laboratory; (2) understanding the evolution of genetic robustness; and (3) exploring the implications of genetic robustness for future evolution.

Uncovering cryptic genetic variation.

Cryptic genetic variation is the dark matter of biology: it is variation that is not normally seen, but that might be an essential source of physiological and evolutionary potential. It is uncovered by environmental or genetic perturbations, and is thought to modify the penetrance of common diseases, the response of livestock and crops to artificial selection and the capacity of populations to respond to the emergence of a potentially advantageous macro-mutation. We argue in this review that cryptic genetic variation is pervasive but under-appreciated, we highlight recent progress in determining the nature and identity of genes that underlie cryptic genetic effects and we outline future research directions.

Canalization in evolutionary genetics: a stabilizing theory?

Canalization is an elusive concept. The notion that biological systems ought to evolve to a state of higher stability against mutational and environmental perturbations seems simple enough, but has been exceedingly difficult to prove. Part of the problem has been the lack of a definition of canalization that incorporates an evolutionary genetic perspective and provides a framework for both mathematical and empirical study. After briefly reviewing the importance of canalization in studies of evolution and development, we aim, with this essay, to outline a research program that builds upon the definition of canalization as the reduction in variability of a trait, and uses molecular genetic approaches to shed light on the problems of canalization.

Hints of hidden heritability in GWAS.

Although susceptibility loci identified through genome-wide association studies (GWAS) typically explain only a small proportion of the heritability, a classical quantitative genetic analysis now argues that considering together all common SNPs can explain a large proportion of the heritability of these complex traits. A related study provides recommendations for the sample sizes needed in future GWAS to identify additional susceptibility loci.

Microarrays in ecology and evolution: a preview

Microarray technology provides a new tool with which molecular ecologists and evolutionary biologists can survey genome‐wide patterns of gene expression within and among species. New analytical approaches based on analysis of variance will allow quantification of the contributions of among individual variation, genotype, sex, microenvironment, population structure, and geography to variation in gene expression. Applications of this methodology are reviewed in relation to studies of mechanisms of adaptation and divergence; delineation of developmental and physiological pathways and networks; characterization of quantitative genetic parameters at the level of transcription (‘quantitative genomics’); molecular dissection of parasitism and symbiosis; and studies of the diversification of gene content. Establishment of microarray resources is neither prohibitively expensive nor technologically demanding, and a commitment to development of gene expression profiling methods for nonmodel organisms could have a tremendous impact on molecular and genetic research at the interface of organismal and population biology.

The environmental contribution to gene expression profiles

Microarray analysis provides a bridge between the molecular genetic analysis of model organisms in laboratory settings and studies of physiology, development, and adaptation in the wild. By sampling species across a range of environments, it is possible to gain a broad picture of the genomic response to environmental perturbation. Incorporating estimates of genetic relationships into study designs will facilitate genomic analysis of environmental plasticity by aiding the identification of major regulatory loci in natural populations.