Limits to Prediction of Phenotypes from Knowledge of Genotypes. In: Clegg MT, Hecht MK, Macintyre RJ, editors. Evolutionary Biology. Boston: Springer US; 2000. pp. 205-24. [DOI: 10.1007/978-1-4615-4135-6_11]

Clinical, Biochemical, and Genetic Heterogeneity in Glutaric Aciduria Type II Patients

The variants of electron transfer flavoprotein (ETFA, ETFB) and ETF dehydrogenase (ETFDH) are the leading cause of glutaric aciduria type II (GA-II). In this study, we identified 13 patients harboring six variants of two genes associated with GA-II. Out of the six variants, four were missense, and two were frameshift mutations. A missense variant (ETFDH:p.Gln269His) was observed in a homozygous state in nine patients. Among nine patients, three had experienced metabolic crises with recurrent vomiting, abdominal pain, and nausea. In one patient with persistent metabolic acidosis, hypoglycemia, and a high anion gap, the ETFDH:p.Gly472Arg, and ETFB:p.Pro94Thrfs*8 variants were identified in a homozygous, and heterozygous state, respectively. A missense variant ETFDH:p.Ser442Leu was detected in a homozygous state in one patient with metabolic acidosis, hypoglycemia, hyperammonemia and liver dysfunction. The ETFDH:p.Arg41Leu, and ETFB:p.Ile346Phefs*19 variants were observed in a homozygous state in one patient each. Both these variants have not been reported so far. In silico approaches were used to evaluate the pathogenicity and structural changes linked with these six variants. Overall, the results indicate the importance of a newborn screening program and genetic investigations for patients with GA-II. Moreover, careful interpretation and correlation of variants of uncertain significance with clinical and biochemical findings are needed to confirm the pathogenicity of such variants.

Plasmidome AMR screening (PAMRS) workflow: a rapid screening workflow for phenotypic characterization of antibiotic resistance in plasmidomes

Background: Phenotypic characterization of antimicrobial resistance (AMR) in bacteria has remained the gold standard for investigation and monitoring of what resistance is present in an organism. However, the process is laborious and not attractive for screening multiple plasmids from a microbial community (plasmidomes). Instead, genomic tools are used, but a major bottle neck that presence of genes does not always translate into phenotypes. Methods: We designed the plasmidome AMR screening (PAMRS) workflow to investigate the presence of antibiotic resistant phenotypes in a plasmidome using Escherichia coli as a host organism. Plasmidomes were extracted from the faecal matter of chicken, cattle and humans using commercial plasmid extraction kits. Competent E. coli cells were transformed and evaluated using disk diffusion. Thirteen antibiotic resistant phenotypes were screened. Results: Here, we show that multiple antibiotic resistant phenotypes encoded by plasmids can be rapidly screened simultaneously using the PAMRS workflow. E. coli was able to pick up to 7, 5 or 8 resistant phenotypes from a single plasmidome from chicken, cattle or humans, respectively. Resistance to ceftazidime was the most frequently picked up phenotype in humans (52.6%) and cattle (90.5%), whereas in chickens, the most picked up resistant phenotype was resistance to co-trimoxazole, ceftriaxone and ampicillin (18.4% each). Conclusions: This workflow is a novel tool that could facilitate studies to evaluate the occurrence and expression of plasmid-encoded antibiotic resistance in microbial communities and their associated plasmid-host ranges. It could find application in the screening of plasmid-encoded virulence genes.

The Convergence of Systems and Reductionist Approaches in Complex Trait Analysis

Research into the genetic and environmental factors behind complex trait variation has traditionally been segregated into distinct scientific camps. The reductionist approach aims to decrypt phenotypic variability bit by bit, founded on the underlying hypothesis that genome-to-phenome relations are largely constructed from the additive effects of their molecular players. In contrast, the systems approach aims to examine large-scale interactions of many components simultaneously, on the premise that interactions in gene networks can be both linear and non-linear. Both approaches are complementary, and they are becoming increasingly intertwined due to developments in gene editing tools, omics technologies, and population resources. Together, these strategies are beginning to drive the next era in complex trait research, paving the way to improve agriculture and toward more personalized medicine.

Is life law-like?

Genes are generally assumed to be primary biological causes of biological phenotypes and their evolution. In just over a century, a research agenda that has built on Mendel's experiments and on Darwin's theory of natural selection as a law of nature has had unprecedented scientific success in isolating and characterizing many aspects of genetic causation. We revel in these successes, and yet the story is not quite so simple. The complex cooperative nature of genetic architecture and its evolution include teasingly tractable components, but much remains elusive. The proliferation of data generated in our "omics" age raises the question of whether we even have (or need) a unified theory or "law" of life, or even clear standards of inference by which to answer the question. If not, this not only has implications for the widely promulgated belief that we will soon be able to predict phenotypes like disease risk from genes, but also speaks to the limitations in the underlying science itself. Much of life seems to be characterized by ad hoc, ephemeral, contextual probabilism without proper underlying distributions. To the extent that this is true, causal effects are not asymptotically predictable, and new ways of understanding life may be required.

Gene-to-phenotype models and complex trait genetics

The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G→P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene × environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G→P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G→P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene × environment interactions, and pleiotropy. Some promising G→P modelling directions are discussed.

Evidence for Non-additive Influence of Single Nucleotide Polymorphisms within the Apolipoprotein E Gene

We analyzed 13 single nucleotide polymorphisms (SNPs) within the apolipoprotein E (APOE) gene, to identify pairs of SNPs that interact in a non-additive manner to influence genotypic mean levels of the ApoE protein in blood. An overparameterized general linear model of two-SNP genotype means was applied to data from 456 female and 398 male unrelated European Americans from Rochester, MN, USA. We found statistically significant evidence for non-additivity between SNPs within the male sample, but not within the female sample. We observed nine pairs of SNPs with evidence of non-additivity at the alpha=0.05 level of statistical significance within the male sample, when approximately three were expected by chance. Five of the nine pairs involved three SNPs (560, 624 and 1163) that did not have a statistically significant influence when considered separately in a single-site analysis. Three of the nine pairs involving four SNPs (832, 1998, 3937 and 4951) showed significant evidence for non-additivity in at least one of two other male samples from Jackson, MS, USA and North Karelia, Finland. Although all four of these SNPs had a statistically significant influence in Rochester when considered separately, only SNP 3937 gave a significant result in the other male samples. The four SNPs are located in the promoter, intronic and exonic regions, and 3' to the polyadenylation signal in the APOE gene. Our study suggests that analyses that only consider SNPs located in exons and ignore contexts such as those indexed by gender and population, and disregard non-additivity of SNP effects, may inappropriately model the contribution of a gene to the genetic architecture of a trait that has a complex multifactorial etiology.

Rethinking genetic strategies to study complex diseases

Understanding the genetic basis of complex diseases is turning out to be difficult, prompting a widespread (re-)evaluation of the relevant issues. 'Forward' and 'reverse' genetics strategies have been applied arguably in a manner only suitable for much simpler diseases. It would now be beneficial to pay detailed attention to experimental design, and to increase study scales dramatically. Ultimately, this would lead to completely hypothesis-free, truly comprehensive, multi-platform investigations. Such studies would maximize the chances of finding data patterns indicative of real etiology, although many aspects of complex disease causation might simply be too intricate and inconsistent to ever be deciphered. Therefore, considerable technology development is an immediate priority, along with parallel advances in bioinformatics and biostatistics systems aimed at discriminating between marginal signals and background noise within extremely large, diverse and complex data sets. Community standards and open data sharing will be essential ingredients for success in this exciting 21st-century challenge.

Maternal environment and genotype interact to establish diabesity in mice

Obesity, a major risk factor for type II diabetes, is becoming more prevalent in Western populations consuming high calorie diets while expending less energy both at the workplace and at home. Most human obesity, and probably most type II diabetes as well, reflects polygenic rather than monogenic inheritance. We have genetically dissected a polygenic mouse model of obesity-driven type II diabetes by outcrossing the obese, diabetes-prone, NZO (New Zealand Obese)/HlLt strain to the relatively lean NON (Nonobese Nondiabetic)/Lt strain, and then reciprocally backcrossing obese F1 mice to the lean NON/Lt parental strain. A continuous distribution of body weights was observed in a population of 203 first backcross males. The 22% of first backcross males developing overt diabetes showed highest peripubertal weight gains and earliest development of hyperinsulinemia. We report a complex diabetes-predisposing ("diabesity") QTL (Quantitative Trait Loci) on chromosome 1 contributing significant main effects to increases in body weight, plasma insulin, and plasma glucose. NZO contributed QTL with significant main effects on adiposity parameters on chromosomes 12 and 5. A NON QTL on chromosome 14 interacted epistatically with the NZO obesity QTL on chromosome 12 to increase adiposity. Although the main effect of the diabetogenic QTL on chromosome 1 was on rapid growth rather than adiposity, it interacted epistatically with the obesity QTL on chromosome 12 to increase plasma glucose levels. Additional complex epistatic interactions eliciting significant increases in body weight and/or plasma glucose were found between the NZO-contributed QTL on chromosome 1 and other NZO-contributed QTL on chromosomes 15 and 17, as well as with an NON-contributed QTL on chromosome 2. We further show that certain of these intergenic interactions are predicated on, or enhanced by, the maternal postparturitional environment. We show by cross-fostering experiments that the maternal environmental influence in part is because of the presence of early obesity-inducing factors in the milk of obese F1 dams. We also discuss a strategy for using recombinant congenic strains to separate and reassemble interacting QTL for future study.