The NIDDK Central Repository at 8 years—Ambition, Revision, Use and Impact

The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository makes data and biospecimens from NIDDK-funded research available to the broader scientific community. It thereby facilitates: the testing of new hypotheses without new data or biospecimen collection; pooling data across several studies to increase statistical power; and informative genetic analyses using the Repository’s well-curated phenotypic data. This article describes the initial database plan for the Repository and its revision using a simpler model. Among the lessons learned were the trade-offs between the complexity of a database design and the costs in time and money of implementation; the importance of integrating consent documents into the basic design; the crucial need for linkage files that associate biospecimen IDs with the masked subject IDs used in deposited data sets; and the importance of standardized procedures to test the integrity data sets prior to distribution. The Repository is currently tracking 111 ongoing NIDDK-funded studies many of which include genotype data, and it houses over 5 million biospecimens of more than 25 types including serum, plasma, stool, urine, DNA, red blood cells, buffy coat and tissue. Repository resources have supported a range of biochemical, clinical, statistical and genetic research (188 external requests for clinical data and 31 for biospecimens have been approved or are pending). Genetic research has included GWAS, validation studies, development of methods to improve statistical power of GWAS and testing of new statistical methods for genetic research. We anticipate that the future impact of the Repository’s resources on biomedical research will be enhanced by (i) cross-listing of Repository biospecimens in additional searchable databases and biobank catalogs; (ii) ongoing deployment of new applications for querying the contents of the Repository; and (iii) increased harmonization of procedures, data collection strategies, questionnaires etc. across both research studies and within the vocabularies used by different repositories

Genetic Fine-Mapping and Identification of Candidate Genes and Variants for Adiposity Traits in Outbred Rats

OBJECTIVE: Obesity is a major risk factor for multiple diseases and is in part heritable, yet the majority of causative genetic variants that drive excessive adiposity remain unknown. Here, outbred heterogeneous stock (HS) rats were used in controlled environmental conditions to fine‐map novel genetic modifiers of adiposity. METHODS: Body weight and visceral fat pad weights were measured in male HS rats that were also genotyped genome‐wide. Quantitative trait loci (QTL) were identified by genome‐wide association of imputed single‐nucleotide polymorphism (SNP) genotypes using a linear mixed effect model that accounts for unequal relatedness between the HS rats. Candidate genes were assessed by protein modeling and mediation analysis of expression for coding and noncoding variants, respectively. RESULTS: HS rats exhibited large variation in adiposity traits, which were highly heritable and correlated with metabolic health. Fine‐mapping of fat pad weight and body weight revealed three QTL and prioritized five candidate genes. Fat pad weight was associated with missense SNPs in Adcy3 and Prlhr and altered expression of Krtcap3 and Slc30a3, whereas Grid2 was identified as a candidate within the body weight locus. CONCLUSIONS: These data demonstrate the power of HS rats for identification of known and novel heritable mediators of obesity traits

A Critical Review of Biomarkers Used for Monitoring Human Exposure to Lead: Advantages, Limitations, and Future Needs

Lead concentration in whole blood (BPb) is the primary biomarker used to monitor exposure to this metallic element. The U.S. Centers for Disease Control and Prevention and the World Health Organization define a BPb of 10 μg/dL (0.48 μmol/L) as the threshold of concern in young children. However, recent studies have reported the possibility of adverse health effects, including intellectual impairment in young children, at BPb levels < 10 μg/dL, suggesting that there is no safe level of exposure. It appears impossible to differentiate between low-level chronic Pb exposure and a high-level short Pb exposure based on a single BPb measurement; therefore, serial BPb measurements offer a better estimation of possible health outcomes. The difficulty in assessing the exact nature of Pb exposure is dependent not so much on problems with current analytical methodologies, but rather on the complex toxicokinetics of Pb within various body compartments (i.e., cycling of Pb between bone, blood, and soft tissues). If we are to differentiate more effectively between Pb stored in the body for years and Pb from recent exposure, information on other biomarkers of exposure may be needed. None of the current biomarkers of internal Pb dose have yet been accepted by the scientific community as a reliable substitute for a BPb measurement. This review focuses on the limitations of biomarkers of Pb exposure and the need to improve the accuracy of their measurement. We present here only the traditional analytical protocols in current use, and we attempt to assess the influence of confounding variables on BPb levels. Finally, we discuss the interpretation of BPb data with respect to both external and endogenous Pb exposure, past or recent exposure, as well as the significance of Pb determinations in human specimens including hair, nails, saliva, bone, blood (plasma, whole blood), urine, feces, and exfoliated teeth

Haplotype association mapping in mice.

Haplotype Association Mapping (HAM) is a novel phenotype-driven approach to identify genetic loci and was originally developed for mice. This method, which is similar to Genome-Wide Association (GWA) studies in humans, looks for associations between the phenotype and the haplotypes of mouse inbred strains, treating inbred strains as individuals. Although this approach is still in development, we review the current literature, present the different methods and applications that are in use, and provide a glimpse of what is to come in the near future

Genetic factors contributing to obesity and body weight can act through mechanisms affecting muscle weight, fat weight, or both.

Genetic loci for body weight and subphenotypes such as fat weight have been mapped repeatedly. However, the distinct effects of different loci and physiological interactions among different traits are often not accounted for in mapping studies. Here we used the method of structural equation modeling to identify the specific relationships between genetic loci and different phenotypes influencing body weight. Using this technique, we were able to distinguish genetic loci that affect adiposity from those that affect muscle growth. We examined the high body weight-selected mouse lines NMRI8 and DU6i and the intercross populations NMRI8 x DBA/2 and DU6i x DBA/2. Structural models help us understand whether genetic factors affect lean mass and fat mass pleiotropically or nonpleiotropically. Sex has direct effects on both fat and muscle weight but also influences fat weight indirectly via muscle weight. Three genetic loci identified in these two crosses showed exclusive effects on fat deposition, and five loci contributed exclusively to muscle weight. Two additional loci showed pleiotropic effects on fat and muscle weight, with one locus acting in both crosses. Fat weight and muscle weight were influenced by epistatic effects. We provide evidence that significant fat loci in strains selected for body weight contribute to fat weight both directly and indirectly via the influence on lean weight. These results shed new light on the action of genes in quantitative trait locus regions potentially influencing muscle and fat mass and thus controlling body weight as a composite trait

Quantitative trait mapping in a diallel cross of recombinant inbred lines.

A recombinant inbred intercross (RIX) is created by generating diallel F1 progeny from one or more panels of recombinant inbred (RI) strains. This design was originally introduced to extend the power of small RI panels for the confirmation of quantitative trait loci (QTL) provisionally detected in a parental RI set. For example, the set of 13 C x B (C57BL/6ByJ x BALB/cByJ) RI strains can, in principle, be supplemented with 156 isogenic F1s. We describe and test a method of analysis, based on a linear mixed model, that accounts for the correlation structure of RIX populations. This model suggests a novel permutation algorithm that is needed to obtain appropriate threshold values for genome-wide scans of an RIX population. Despite the combinational multiplication of unique genotypes that can be generated using an RIX design, the effective sample size of the RIX population is limited by the number of progenitor RI genomes that are combined. When using small RI panels such as the C x B there appears to be only modest advantage of the RIX design when compared with the original RI panel for detecting QTLs with additive effects. The RIX, however, does have an inherent ability to detect dominance effects, and, unlike RI strains, the RIX progeny are genetically reproducible but are not fully inbred, providing somewhat more natural genetic context. We suggest a breeding strategy, the balanced partial RIX, that balances the advantage of RI and RIX designs. This involves the use of a partial RIX population derived from a large RI panel in which the available information is maximized by minimizing correlations among RIX progeny