Academic journal article Genetics

Allelic Variation, Aneuploidy, and Nongenetic Mechanisms Suppress a Monogenic Trait in Yeast

Academic journal article Genetics

Allelic Variation, Aneuploidy, and Nongenetic Mechanisms Suppress a Monogenic Trait in Yeast

Article excerpt

MEDICAL genetics is based on the assumption that the sequence of a gene (or complete genome) can be used to predict an individual's phenotype(s), including traits related to the prevention, diagnosis, and treatment of human disease. The most success in this regard has come from the analysis of monogenic (Mendelian) traits, i.e., those linked to polymorphisms in a single gene. The Online Mendelian Inheritance in Man database currently contains ~3000 human genes "with a phenotype-causing mutation" (http://omim. org/), including many diseases caused by complete or partial loss of gene function. Inborn errors of metabolism (IEMs), such as cystic fibrosis and galactosemia, are often monogenic diseases that are individually rare but common as a class (in the United States at least 1 in 5000 live births is affected by an IEM) (Gupta 2007). In contrast, many common diseases such as asthma, diabetes, heart disease, and cancer show heritability patterns that suggest the involvement of large numbers of genes and environmental factors (Akhabir and Sandford 2011; Hindorffet al. 2011; Marian and Belmont 2011; Polychronakos and Li 2011). While the fundamental concepts of mapping these so-called complex traits, or quantitative trait loci (QTL), were introduced decades ago (Lander and Botstein 1989), attempts to identify and understand the underlying genes have met with limited success. QTL analysis in humans and model organisms typically detects only a fraction of the predicted number of loci. A commonly cited example is that of human height in which all genetic loci identified to date explain only 10% of the phenotypic variation (Lango Allen et al. 2010).

When examined closely, the division between monogenic and complex traits quickly breaks down. Clinically relevant features of monogenic diseases, including severity of symptoms and age of onset, vary widely in response to environmental differences and the presence of genetic modifiers that affect the penetrance and expressivity of the trait (Dipple and McCabe 2000b; Nadeau 2001; Weatherall 2001; Genin et al. 2008). Thus, monogenic diseases can be viewed as a special class of complex trait in which allelic variation in one gene has an overwhelmingly strong effect over one or more independently inherited modifier gene(s) that exacerbate or ameliorate the primary disease gene's phenotype. Because some modifiers confer protective effects against the underlying disease allele or causative agent, understanding them may reveal means of reducing the adverse effects of deleterious polymorphisms (Nadeau 2003).

Like other complex traits, identifying modifiers of monogenic diseases has generally been "a frustration and disappointment to clinical geneticists, who hoped that knowledge of a patient's genotype would predict disease and optimize prevention" (Dipple and McCabe 2000a). Because Mendelian diseases are often rare in the human population and individuals with similar sets of modifiers are (presumably) even rarer (Knowles 2006; Weiler and Drumm 2013), some of these failings may be due to the lack of statistical power (sufficiently large numbers of individuals). Studies in model organisms have the potential to overcome this limitation, particularly through the use of large-scale crosses. Saccharomyces cerevisiae is ideally suited to this type of analysis due to its ease of propagation, genetic manipulability, high meiotic recombination rate, well-characterized genome, and abundance of genetic resources and large-scale data sets (Botstein and Fink 2011). The conservation of core cellular pathways, such as metabolism between yeast and humans, provides a powerful system for the study of human disease gene orthologs. Thus, the application of QTL mapping methods to large-scale crosses of yeast with specific gene deletions could identify loci capable of modifying medically relevant monogenic traits.

Large-scale trait-mapping methods that have been developed for S. cerevisiae generally differ from one another in two respects: (1) whether the entire population of recombinant progeny or an extreme tail of the phenotype distribution is recovered and (2) whether genotyping is performed on individuals or in pools. …

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