Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting.

More than 100,000 genetic variants are classified as disease causing in public databases. However, the true penetrance of many of these rare alleles is uncertain and might be over-estimated by clinical ascertainment. Here, we use data from 379,768 UK Biobank (UKB) participants of European ancestry to assess the pathogenicity and penetrance of putatively clinically important rare variants. Although rare variants are harder to genotype accurately than common variants, we were able to classify as high quality 1,244 of 4,585 (27%) putatively clinically relevant rare (MAF < 1%) variants genotyped on the UKB microarray. We defined as "clinically relevant" variants that were classified as either pathogenic or likely pathogenic in ClinVar or are in genes known to cause two specific monogenic diseases: maturity-onset diabetes of the young (MODY) and severe developmental disorders (DDs). We assessed the penetrance and pathogenicity of these high-quality variants by testing their association with 401 clinically relevant traits. 27 of the variants were associated with a UKB trait, and we were able to refine the penetrance estimate for some of the variants. For example, the HNF4A c.340C>T (p.Arg114Trp) (GenBank: NM_175914.4) variant associated with diabetes is <10% penetrant by the time an individual is 40 years old. We also observed associations with relevant traits for heterozygous carriers of some rare recessive conditions, e.g., heterozygous carriers of the ERCC4 c.2395C>T (p.Arg799Trp) variant that causes Xeroderma pigmentosum were more susceptible to sunburn. Finally, we refute the previous disease association of RNF135 in developmental disorders. In conclusion, this study shows that very large population-based studies will help refine our understanding of the pathogenicity of rare genetic variants.

Modeling N-Glycosylation: A Systems Biology Approach for Evaluating Changes in the Steady-State Organization of Golgi-Resident Proteins.

The organization of Golgi-resident proteins is crucial for sorting molecules within the secretory pathway and regulating posttranslational modifications. However, evaluating changes to Golgi organization can be challenging, often requiring extensive experimental investigations. Here, we propose a systems biology approach in which changes to Golgi-resident protein sorting and localization can be deduced using cellular N-glycan profiles as the only experimental input.The approach detailed here utilizes the influence of Golgi organization on N-glycan biosynthesis to investigate the mechanisms involved in establishing and maintaining Golgi organization. While N-glycosylation is carried out in a non-template-driven manner, the distribution of N-glycan biosynthetic enzymes within the Golgi ensures this process is not completely random. Therefore, changes to N-glycan profiles provide clues into how altered cell phenotypes affect the sorting and localization of Golgi-resident proteins. Here, we generate a stochastic simulation of N-glycan biosynthesis to produce a simulated glycan profile similar to that obtained experimentally and then combine this with Bayesian fitting to enable inference of changes in enzyme amounts and localizations. Alterations to Golgi organization are evaluated by calculating how the fitted enzyme parameters shift when moving from simulating the glycan profile of one cellular state (e.g., a wild type) to an altered cellular state (e.g., a mutant). Our approach illustrates how an iterative combination of mathematical systems biology and minimal experimental cell biology can be utilized to maximally integrate biological knowledge to gain insightful knowledge of the underlying mechanisms in a manner inaccessible to either alone.

Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes.

Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.

Attribute-based analysis of hunters' lease preferences.

Understanding of hunter preferences for hunting lease attributes is important to landowners because such knowledge provides key information for managing and marketing fee-hunting in order to maximize revenues. Premised on this insight, we used attribute based modeling to investigate how hunter preferences for potential leases were influenced by lease and hunter-specific attributes. A mail survey of Mississippi licensed hunters provided the necessary data. Estimation results based on McFadden conditional logit regression suggested that lease attributes including game diversity, lease location relative to hunter residence, lease size, lease duration and lease rate influenced willingness to pay for additional units of lease attributes. Depending on the specific levels of these attributes, WTP could vary as much as $5.70 per acre. Of the hunter-specific attributes, age and income significantly influenced hunter decision to buy a lease or opt for status quo. Results of this study should assist landowners in increasing financial returns from fee-hunting endeavors through appropriate changes to their hunting access policies and wildlife management activities in response to hunter preferences regarding lease attributes.

A Common Allele in FGF21 Associated with Sugar Intake Is Associated with Body Shape, Lower Total Body-Fat Percentage, and Higher Blood Pressure.

Fibroblast growth factor 21 (FGF21) is a hormone that has insulin-sensitizing properties. Some trials of FGF21 analogs show weight loss and lipid-lowering effects. Recent studies have shown that a common allele in the FGF21 gene alters the balance of macronutrients consumed, but there was little evidence of an effect on metabolic traits. We studied a common FGF21 allele (A:rs838133) in 451,099 people from the UK Biobank study, aiming to use the human allele to inform potential adverse and beneficial effects of targeting FGF21. We replicated the association between the A allele and higher percentage carbohydrate intake. We then showed that this allele is more strongly associated with higher blood pressure and waist-hip ratio, despite an association with lower total body-fat percentage, than it is with BMI or type 2 diabetes. These human phenotypes of variation in the FGF21 gene will inform research into FGF21's mechanisms and therapeutic potential.