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An ever-expanding annotation of the human genome sequence continues to promise a new era of precision medicine. Advances in knowledge management and the ability to leverage genetic information to make clinically relevant, predictive, diagnostic, and targeted therapeutic choices offer the ability to improve patient outcomes and reduce the overall cost of healthcare. However, numerous barriers have resulted in a modest start to the clinical use of genetics at scale. Examples of successful deployments include oncologic disease treatment with targeted prescribing; however, even in these cases, genome-informed decision-making has yet to achieve standard of care in most major healthcare systems. In the last two decades, advances in genetic testing, therapeutic coverage, and clinical decision support have resulted in early-stage adoption of pharmacogenomics – the use of genetic information to routinely determine the safety and efficacy profile of specific medications for individuals. Here, through their complicated histories, we review the current state of pharmacogenomic testing technologies, the information tools that can unlock clinical utility, and value-driving implementation strategies that represent the future of pharmacogenomics-enabled healthcare decision-making. We conclude with real-world economic and clinical outcomes from a full-scale deployment and ultimately provide insight into potential tipping points for global adoption, including recent lessons from the rapid scale-up of high-volume test delivery during the global SARS-CoV2 epidemic.

The availability of clinical decision support systems (CDSS) and other methods for personalizing medicine now allows evaluation of their real-world impact on healthcare delivery. For example, addressing issues associated with polypharmacy in older patients using pharmacogenomics (PGx) and comprehensive medication management (CMM) is thought to hold great promise for meaningful improvements across the goals of the Quadruple Aim. However, few studies testing these tools at scale, using relevant system-wide metrics, and under real-world conditions, have been published to date. Here, we document a reduction of ~$7000 per patient in direct medical charges (a total of $37 million over 5288 enrollees compared to 22,357 non-enrolled) in Medicare Advantage patients (≥65 years) receiving benefits through a state retirement system over the first 32 months of a voluntary PGx-enriched CMM program. We also observe a positive shift in healthcare resource utilization (HRU) away from acute care services and toward more sustainable and cost-effective primary care options. Together with improvements in medication risk assessment, patient/provider communication via pharmacist-mediated medication action plans (MAP), and the sustained positive trends in HRU, we suggest these results validate the use of a CDSS to unify PGx and CMM to optimize care for this and similar patient populations.

Introduction: the effects of obesity on health are a concern for the military as they affect the fitness to serve of active service members, increase costs to the Military Health System, and reduce quality of life for veterans and beneficiaries. Although obesity can be influenced by behavioral and environmental factors, it has also been shown to be associated with genetic risk factors that are not fully understood. Materials and Methods: we performed a genome-wide association study of 5,251 participants in the Coriell Personalized Medicine Collaborative, which includes 2,111 Air Force participants. We applied a generalized linear model, using principal component analysis to account for population structure, and analyzed single-variant associations with body mass index (BMI) as a continuous variable, using a Bonferroni-corrected P-value threshold to account for multiplicity. Results: we identified one genome-wide significant locus, rs11670527, upstream of the ZNF264 gene on chromosome 19, associated with BMI. Conclusions: the finding of an association between rs11670527 and BMI adds to the growing body of literature characterizing the complex genetics of obesity. These efforts may eventually inform personalized interventions aimed at achieving and maintaining healthy weight.

Vaginitis is often diagnosed by microscopy and limited to testing for bacterial vaginosis (BV), vulvovaginal candidiasis, and trichomoniasis. Approximately 10% of vaginal swabs are negative but designated “altered flora” by BV Nugent score, leaving clinicians unsure how to treat patients. Accurate and comprehensive vaginitis diagnostics are needed to direct treatment and reduce risks of recurrent or more severe infections. Vaginal swabs were collected from 93 women (mean age, 23.53 years; range, 18 to 42 years) in a cross-sectional study. Microscopy results for BV and Candida were compared to those from two molecular approaches: (i) a comprehensive quantitative PCR (qPCR) assay, including testing for aerobic vaginitis (AV), Candida, sexually transmitted infections (STI), and BV (Applied Biosystems) with an accompanying BV interpretive algorithm (Coriell Life Sciences), and (ii) microbiome profiling of the 16S rRNA gene (Illumina). Microscopy plus BV Nugent score had 76% overall agreement with the qPCR plus BV interpretive algorithm method (24 positive, 47 negative). OF the nine samples designated altered flora by Nugent, five were categorized BV positive and four were BV negative by the qPCR method. Although BV negative, 3/4 of the latter samples had positive AV targets with one also was STI positive. Microscopic identification of Candida versus that by qPCR had 94% agreement (9 positive, 78 negative). The comprehensive qPCR assay revealed alternative etiologies summarized as 38% BV, 10% AV, 5% Candida, 2% STI, 10% mixed infection (positive targets in multiple panels), and 35% negative for all targets. 16S microbiome analysis confirmed the bacterial qPCR results and identified differentiating patterns between AV, BV, and Lactobacillus-dominated vaginal microbiomes.

Pharmacogenomics represents a potentially powerful enhancement to the current standard of care for psychiatric patients. However, a variety of biological and technical challenges must be addressed in order to provide adequate clinical decision support for personalized prescribing and dosing based on genomic data. This is particularly true in the case of CYP2D6, a key drug-metabolizing gene, which not only harbors multiple genetic variants known to affect enzyme function but also shows a broad range of copy-number and hybrid alleles in various patient populations. Here, we describe several challenges in the accurate measurement and interpretation of data from the CYP2D6 locus including the clinical consequences of increased copy number. We discuss best practices for overcoming these challenges and then explore various current and future applications of pharmacogenomic analysis of CYP2D6 in psychiatry.

Objective: The Coriell Personalized Medicine Collaborative (CPMC) research study is designed to evaluate the potential contributions of common genetic risk factors to complex disease prevention, screening, and management. Here we have focused on the impact of personalized risk reports including genetic and non-genetic risk factors for coronary artery disease (CAD) on heart health behaviors.Methods: We analyzed self-reported behavioral outcome data from 683 CPMC participants who received personalized CAD risk reports including: genetic risk, family history risk, and self-reported non-genetic risks based on smoking and diabetes status.Results: Participants with awareness of increased genetic risk for CAD were significantly more likely to report increases in heart health behaviors after viewing their personalized risk report (F-value=14.11, p-value=9.92 x 10−7). This result remained significant after controlling for BMI and gender (eta=0.58, p-value = 6.91 x 10−7).Conclusion: Our study indicates that...

SummaryPharmacogenomics (PGx) guided warfarin dosing, using a comprehensive dosing algorithm, is expected to improve dose optimisation and lower the risk of adverse drug reactions. As a complementary tool, a simple genotype-dosing table, such as in the US Food and Drug Administration (FDA) Coumadin drug label, may be utilised for general risk assessment of likely over- or under-anticoagulation on a standard dose of warfarin. This tool may be used as part of the clinical decision support for the interpretation of genetic data, serving as a first step in the anticoagulation therapy decision making process. Here we used a publicly available warfarin dosing calculator (www.warfarindosing.org) to create an expanded gene-based warfarin dosing table, the CPMC-WD table that includes nine genetic variants in CYP2C9, VKORC1, and CYP4F2. Using two datasets, a European American cohort (EUA, n=73) and the Quebec Warfarin Cohort (QWC, n=769), we show that the CPMC-WD table more accurately predicts therapeutic dose than the FDA table (51 % vs 33 %, respectively, in the EUA, McNemar’s two-sided p=0.02; 52 % vs 37 % in the QWC, pSupplementary Material to this article is available at www.thrombosis-online.com.

The objectives of this study were to determine whether differences in CYP2C8 and CYP2C9 haplotype influence the dose of ibuprofen self-administered by individuals, and to examine the potential relationship between CYP2C8 and CYP2C9 reduced metabolism haplotypes and adverse events.We investigated relationships between genetic variations in CYP2C8 and CYP2C9 and ibuprofen use, dose, and side effects (reported by questionnaire) in 445 participants from the Coriell Personalized Medicine Collaborative.Carriers of reduced metabolism haplotypes for CYP2C8 (*2, *3, *4) and CYP2C9 (*2, *3) were significantly (P=0.0171) more likely than those lacking these variants to take less than the recommended dose of ibuprofen, after controlling for sex, age, race, and cohort. In contrast to ibuprofen dose, there were no differences in ibuprofen use frequency or reported side effects based on haplotype. However, there are often no early signs of acute kidney injury, the most serious side effect of elevated ibuprofen exposure.These results suggest a subset of individuals with genetic variation in CYP2C8 and CYP2C9 recognize that they obtain adequate drug efficacy with lower ibuprofen doses, or take lower doses due to prior side effects. However, most (82.6%) individuals with reduced metabolism haplotypes nonetheless took recommended or higher doses, potentially putting them at increased risk for side effects.

/SummaryBacterial vaginosis is a common condition among women of reproductive age and is associated with potentially serious side-effects, including an increased risk of preterm birth. Recent advancements in microbiome sequencing technologies have produced novel insights into the complicated mechanisms underlying bacterial vaginosis and have given rise to new methods of diagnosis. Here we report on the validation of a quantitative, molecular diagnostic algorithm based on the relative abundances of ten potentially pathogenic bacteria and four commensal Lactobacillus species in research subjects (n = 172) classified as symptomatic (n = 149) or asymptomatic (n = 23). We observe a clear and reinforcing pattern among patients diagnosed by the algorithm that is consistent with the current understanding of biological dynamics and dysregulation of the vaginal microbiome during infection. Using this enhanced assessment of the underlying biology of infection, we demonstrate improved diagnostic sensitivity (93%) and specificity (90%) relative to current diagnostic tools. Our algorithm also appears to provide enhanced diagnostic capabilities in ambiguous classes of patients for whom diagnosis and medical decision-making is complicated, including asymptomatic patients and those deemed “intermediate” by Nugent scoring. Ultimately, we establish CLS2.0q as a quantitative, sensitive, specific, accurate, robust, and flexible algorithm for the clinical diagnosis of bacterial vaginosis – importantly, one that is also ideal for the differential diagnosis of non-BV infections with clinically similar presentations.

Sleep is critical to health and functionality, and several studies have investigated the inherited component of insomnia and other sleep disorders using genome‐wide association studies (GWAS). However, genome‐wide studies focused on sleep duration are less common. Here, we used data from participants in the Coriell Personalized Medicine Collaborative (CPMC) (n = 4,401) to examine putative associations between self‐reported sleep duration, demographic and lifestyle variables, and genome‐wide single nucleotide polymorphism (SNP) data to better understand genetic contributions to variation in sleep duration. We employed stepwise ordered logistic regression to select our model and retained the following predictive variables: age, gender, weight, physical activity, physical activity at work, smoking status, alcohol consumption, ethnicity, and ancestry (as measured by principal components analysis) in our association testing. Several of our strongest candidate genes were previously identified in GWAS related to sleep duration (TSHZ2, ABCC9, FBXO15) and narcolepsy (NFATC2, SALL4). In addition, we have identified novel candidate genes for involvement in sleep duration including SORCS1 and ELOVL2. Our results demonstrate that the self‐reported data collected through the CPMC are robust, and our genome‐wide association analysis has identified novel candidate genes involved in sleep duration. More generally, this study contributes to a better understanding of the complexity of human sleep. © 2015 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

There is currently great interest in using genetic risk estimates for common disease in personalized healthcare. Here we assess melanoma risk-related preventive behavioral change in the context of the Coriell Personalized Medicine Collaborative (CPMC). As part of on-going reporting activities within the project, participants received a personalized risk assessment including information related to their own self-reported family history of melanoma and a genetic risk variant showing a moderate effect size (1.7, 3.0 respectively for heterozygous and homozygous individuals). Participants who opted to view their report were sent an optional outcome survey assessing risk perception and behavioral change in the months that followed. Participants that report family history risk, genetic risk, or both risk factors for melanoma were significantly more likely to increase skin cancer preventive behaviors when compared to participants with neither risk factor (ORs = 2.04, 2.79, 4.06 and p-values = 0.02, 2.86 × 10−5, 4.67 × 10−5, respectively), and we found the relationship between risk information and behavior to be partially mediated by anxiety. Genomic risk assessments appear to encourage positive behavioral change in a manner that is complementary to family history risk information and therefore may represent a useful addition to standard of care for melanoma prevention.

Following several years enrolling disease-specific and otherwise healthy cohorts into the Coriell Personalized Medicine Collaborative, a prospective study aimed at evaluating the clinical utility of personal genomic information for common complex disease and pharmacogenomics, the Coriell Personalized Medicine Collaborative expanded to create a military cohort, specifically, the United States Air Force. Initial recruitment focused on Air Force Medical Service personnel and later expanded to include all Active Duty Air Force members and beneficiaries. Now in its 6th year, the study has produced a wide variety of insights, including optimal study design for military-sponsored genomic research, and discussion on genetic information sharing between and amongst Air Force study participants, civilian and military researchers, and the United States Department of Defense. Over the longer term, analyses will further contribute to the development of policies and processes relevant to clinical decision support and data sharing within the US military, and on-going work with the Air Force Medical Service sub-cohort will generate critical insights into how best to deploy useful genomic information in clinical care. Here we discuss challenges faced and critical success factors for military-civilian collaborations around genomic research.

This study examined whether a CYP2D6 polymorphism (CYP2D6*4) was related to beta-blocker maintenance dose in patients with heart failure. Logistic regression modeling was utilized in a retrospective chart-review analysis of heart-failure patients (60% Male, 90% of European descent) to assess whether CYP2D6*4 (non-functional CYP2D6 allele present in 1 of 5 individuals of European descent) is associated with maintenance dose of carvedilol (n = 65) or metoprolol (n = 33). CYP2D6*4 was associated with lower maintenance dose of metoprolol (OR 0.13 [95% CI 0.02–0.75] p = 0.023), and a trend was observed between CYP2D6*4 and higher maintenance dose of carvedilol (OR 2.94 [95% CI 0.84–10.30] p = 0.093). None of the patients that carried CYP2D6*4 achieved the recommended target dose of metoprolol (200 mg/day). Consistent with the role of CYP2D6 in the metabolism of metoprolol, the tolerated maintenance dose of metoprolol was lower in CYP2D6*4 carriers compared to non-carriers. Consistent with the role of CYP2D6 in activation of carvedilol, tolerated maintenance dose of carvedilol was higher in CYP2D6*4 carriers compared to non-carriers. Further investigation is warranted to ascertain the potential of CYP2D6 as a potential predictive biomarker of beta-blocker maintenance dose in heart failure patients.

It is well known that standard population genetic theory predicts decreased additive genetic variance (V(a) ) following a population bottleneck and that theoretical models including interallelic and intergenic interactions indicate such loss may be avoided. However, few empirical data from multicellular model systems are available, especially regarding variance/covariance (V/CV) relationships. Here, we compare the V/CV structure of seventeen traits related to body size and composition between control (60 mating pairs/generation) and bottlenecked (2 mating pairs/generation; average F = 0.39) strains of mice. Although results for individual traits vary considerably, multivariate analysis indicates that V(a) in the bottlenecked populations is greater than expected. Traits with patterns and amounts of epistasis predictive of enhanced V(a) also show the largest deviations from additive expectations. Finally, the correlation structure of weekly weights is not significantly different between control and experimental lines but correlations between necropsy traits do differ, especially those involving the heart, kidney and tail length.

Genome-wide mapping analyses are now commonplace in many species and several networks of interacting loci have been reported. However, relatively few details regarding epistatic interactions and their contribution to complex trait variation in multicellular organisms are available and the identification of positional candidate loci for epistatic QTL (epiQTL) is hampered, especially in mammals, by the limited genetic resolution inherent in most study designs. Here we further investigate the genetic architecture of reproductive fatpad weight in mice using the F10 generation of the LG,SM advanced intercross (AI) line. We apply multiple mapping techniques including a single-locus model, locus-specific composite interval mapping (CIM), and tests for multiple QTL per chromosome to the 12 chromosomes known to harbor single-locus QTL (slQTL) affecting obesity in this cross. We also perform a genome-wide scan for pairwise epistasis. Using this combination of approaches we detect 199 peaks spread over all 19 autosomes, which potentially contribute to trait variation including all eight original F2 loci (Adip1-8), novel slQTL peaks on chromosomes 7 and 9, and several novel epistatic loci. Extensive epistasis is confirmed involving both slQTL confidence intervals (C.I.) as well as regions that show no significant additive or dominance effects. These results provide important new insights into mapping complex genetic architectures and the role of epistasis in complex trait variation.

The evolutionary effects of epistasis have been primarily explored analytically and most empirical studies have utilized yeast, viral and bacterial populations. Empirical analyses in multi-cellular organisms are rare because of experimental constraints. Here, we report the results of a genome-wide scan for two-way epistasis in 16 traits related to body size and composition in F(2) mice from the LG/J by SM/J intercross. We analyze two-locus genotypic values at quantitative trait loci (QTL), which provides an especially detailed view of epistatic architectures, to evaluate their predicted evolutionary consequences via Monte Carlo simulations. Epistatic profiles vary, but all traits show complicated genetic architectures which are largely hidden in single locus QTL scans. On average, detected epistatic effects are comparable in size to marginal effects. Simulations demonstrate an expected preservation, and often inflation, of heritable variance across several generations of small effective population size for many identified epistatic pairs over a range of starting allele frequencies.

SummaryObesity and osteoporosis affect millions of Americans. While phenotypically, obesity is negatively correlated with fracture risk, research on a genetic basis for this relationship is lacking. We used males and females from 16 LGXSM recombinant inbred (RI) mouse strains to investigate the genetically mediated relationship between obesity and osteoporosis-related traits. First, heritabilities were estimated for (1) bone morphology properties determined by microCT (femoral and radial diaphyseal bone cross-sectional area and moments of inertia, as well as proximal tibial trabecular bone volume, connectivity density, structure model index, trabecular number, trabecular thickness and trabecular separation), (2) mechanical properties determined by bending tests (femoral and radial rigidity, yield moment, ultimate moment, fracture displacement and post-yield displacement), and (3) effective material properties (femoral and radial modulus of elasticity and ultimate tensile strength). All femoral (H2=43–74%) and tibial traits (H2=31–56%) were heritable; as were 8 of 10 radial traits (H2=21–33%). Eighteen significant genetic correlations were discovered between obesity- and osteoporosis-related phenotypes. Genetic correlations indicate that gene effects associated with increased fat mass and leptin levels are also associated with larger, stronger femora. Gene effects associated with larger, stronger radii and with denser tibiae were also associated with increased fat mass but not with leptin levels. Furthermore, quantitative trait loci (QTLs) previously reported for obesity and leptin levels also had effects on bone morphology, mechanical and material properties. Our results support the use of the LG/J-by-SM/J mouse intercross populations as models for normal, complex genetic variation in obesity, bone properties and their interrelationship.

We present here a detailed study of the genetic contributions to adult body size and adiposity in the LG,SM advanced intercross line (AIL), an obesity model. This study represents a first step in fine-mapping obesity quantitative trait loci (QTLs) in an AIL. QTLs for adiposity in this model were previously isolated to chromosomes 1, 6, 7, 8, 9, 12, 13, and 18. This study focuses on heritable contributions and the genetic architecture of fatpad and organ weights. We analyzed both the F(2) and F(3) generations of the LG,SM AIL population single-nucleotide polymorphism (SNP) genotyped with a marker density of approximately 4 cM. We replicate 88% of the previously identified obesity QTLs and identify 13 new obesity QTLs. Nearly half of the single-trait QTLs were sex-specific. Several broad QTL regions were resolved into multiple, narrower peaks. The 113 single-trait QTLs for organs and body weight clustered into 27 pleiotropic loci. A large number of epistatic interactions are described which begin to elucidate potential interacting molecular networks. We present a relatively rapid means to obtain fine-mapping details from AILs using dense marker maps and consecutive generations. Analysis of the complex genetic architecture underlying fatpad and organ weights in this model may eventually help to elucidate not only heritable contributions to obesity but also common gene sets for obesity and its comorbidities.

Genetic literacy is essential for the effective integration of genomic information into healthcare; yet few recent studies have been conducted to assess the current state of this knowledge base. Participants in the Coriell Personalized Medicine Collaborative (CPMC), a prospective study assessing the impact of personalized genetic risk reports for complex diseases and drug response on behavior and health outcomes, completed genetic knowledge questionnaires and other surveys through an online portal. To assess the association between genetic knowledge and genetic education background, multivariate linear regression was performed. 4 062 participants completed a genetic knowledge and genetic education background questionnaire. Most were older (mean age: 50), Caucasian (90 %), female (59 %), highly educated (69 % bachelor's or higher), with annual household income over $100 000 (49 %). Mean percent correct was 76 %. Controlling for demographics revealed that health care providers, participants previously exposed to genetics, and participants with 'better than most' self-rated knowledge were significantly more likely to have a higher knowledge score (p

Maternal effects on offspring phenotypes occur because mothers in many species provide an environment for their developing young. Although these factors are correctly "environmental" with respect to the offspring genome, their variance may have both a genetic and an environmental basis in the maternal generation. Here, reciprocal crosses between C57BL/6J and 10 LGXSM recombinant inbred (RI) strains were performed, and litters were divided at weaning into high-fat and low-fat dietary treatments. Differences between reciprocal litters were used to measure genetic maternal effects on offspring phenotypes. Nearly all traits, including weekly body weights and adult blood serum traits, show effects indicative of genetic variation in maternal effects across RI strains, allowing the quantitative trait loci involved to be mapped. Although much of the literature on maternal effects relates to early life traits, we detect strong and significant maternal effects on traits measured at adulthood (as much as 10% of the trait variance at 17 or more weeks after weaning). We also found an interaction affecting adult phenotype between the effects of maternal care between RI strain mothers and C57BL/6J mothers and a later environmental factor (dietary fat intake) for some age-specific weights.

We previously mapped Adip1, an obesity quantitative trait locus (QTL), to the central portion of murine chromosome 1 containing the calpain-10 (Capn10) gene. Human studies have associated calpain-10 (CAPN10) variants with type 2 diabetes and various metabolic traits. We performed a quantitative hybrid complementation test (QHCT) to determine whether differences attributed to Adip1 are the result of variant Capn10 alleles in LG/J and SM/J mice. We crossed LG/J and SM/J to wild-type (C57BL/6J) and Capn10 knockout (Capn10(-/-)) mice to form four F(1) hybrid groups: LG/J by wild-type, LG/J by Capn10(-/-), SM/J by wild-type, and SM/J by Capn10(-/-). We performed a two-way ANOVA with the experimental strain, tester strain, and their interaction as the factors. Significant interaction indicates a quantitative failure to complement. We found failure to complement for fat, organ, and body weights, and leptin, female free fatty acid, and triglyceride levels. Capn10(-/-) resulted in heavier weights and higher serum levels in LG/J crosses but not in SM/J crosses. For glucose tolerance and insulin response tests, the Capn10(-/-) allele resulted in lower glucose levels in crosses with SM/J but had no effect in the LG/J crosses. Differences between the LG/J and SM/J Capn10 alleles are the likely source of some of the QTL effects mapped to Adip1 in the LG/J-by-SM/J cross. Capn10 plays an important role in regulating obesity and diabetes in mice.

Obesity develops in response to a combination of environmental effects and multiple genes of small effect. Although there has been significant progress in characterizing genes in many pathways contributing to metabolic disease, knowledge about the relationships of these genes to each other and their joint effects upon obesity lags behind. The LG,SM advanced intercross line (AIL) model of obesity has been used to characterize over 70 loci involved in fatpad weight, body weight, and organ weights. Each of these quantitative trait loci (QTLs) encompasses large regions of the genome and require fine-mapping to isolate causative sequence changes and possible mechanisms of action as indicated by the genetic architecture. In this study we fine-map QTLs first identified in the F(2) and F(2/3) populations in the combined F(9/10) advanced intercross generations. We observed significantly narrowed QTL confidence regions, identified many single QTL that resolve into multiple QTL peaks, and identified new QTLs that may have been previously masked due to opposite gene effects at closely linked loci. We also present further characterization of the pleiotropic and epistatic interactions underlying these obesity-related traits.

Quantitative trait locus (QTL) mapping techniques are frequently used to identify genomic regions associated with variation in phenotypes of interest. However, the F(2) intercross and congenic strain populations usually employed have limited genetic resolution resulting in relatively large confidence intervals that greatly inhibit functional confirmation of statistical results. Here we use the increased resolution of the combined F(9) and F(10) generations (n = 1455) of the LG,SM advanced intercross to fine-map previously identified QTL associated with the lengths of the humerus, ulna, femur, and tibia. We detected 81 QTL affecting long-bone lengths. Of these, 49 were previously identified in the combined F(2)-F(3) population of this intercross, while 32 represent novel contributors to trait variance. Pleiotropy analysis suggests that most QTL affect three to four long bones or serially homologous limb segments. We also identified 72 epistatic interactions involving 38 QTL and 88 novel regions. This analysis shows that using later generations of an advanced intercross greatly facilitates fine-mapping of confidence intervals, resolving three F(2)-F(3) QTL into multiple linked loci and narrowing confidence intervals of other loci, as well as allowing identification of additional QTL. Further characterization of the biological bases of these QTL will help provide a better understanding of the genetics of small variations in long-bone length.

Metabolic syndrome (MetS) can be considered a pheno-physiological cluster of metabolically interrelated risk factors for diabetes mellitus and cardiovascular disease. MetS has emerged as a result of complex interactions among environmental stresses and MetS gene networks and their products. In this review we summarize trends in MetS definitions, their associated controversies and possibilities for their refinement. The National Cholesterol Education Program MetS definition with its improvements by the American Heart Association and NHLBI Conference has the potential to become the primary clinical definition of MetS. For the first time, by reviewing a large body of literature, we construct MetS gene networks in humans and in rodents. These MetS gene networks can serve as a budding platform to develop new hypotheses regarding the genetic mechanisms underlying MetS. We also extend the notion of MetS to mouse models. New and improved molecular genomics and proteomic tools have been developed in parallel with the MetS epidemic which in conjunction with improved and novel computational statistical methods have magnified the genetic resolution of MetS analyses. Our results justify the existence of MetS as a meaningful syndrome and suggest that a better understanding of its etiology can benefit the health of human kind.

Quantitative trait locus (QTL) studies of a skeletal trait or a few related skeletal components are becoming commonplace, but as yet there has been no investigation of pleiotropic patterns throughout the skeleton. We present a comprehensive survey of pleiotropic patterns affecting mouse skeletal morphology in an intercross of LG/J and SM/J inbred strains (N = 1040), using QTL analysis on 70 skeletal traits. We identify 798 single-trait QTL, coalescing to 105 loci that affect on average 7–8 traits each. The number of traits affected per locus ranges from only 1 trait to 30 traits. Individual traits average 11 QTL each, ranging from 4 to 20. Skeletal traits are affected by many, small-effect loci. Significant additive genotypic values average 0.23 standard deviation (SD) units. Fifty percent of loci show codominance with heterozygotes having intermediate phenotypic values. When dominance does occur, the LG/J allele tends to be dominant to the SM/J allele (30% vs. 8%). Over- and underdominance are relatively rare (12%). Approximately one-fifth of QTL are sex specific, including many for pelvic traits. Evaluating the pleiotropic relationships of skeletal traits is important in understanding the role of genetic variation in the growth and development of the skeleton.

Background: Genomic analysis of high-altitude populations residing in the Andes and Tibet has revealed several candidate loci for involvement in high-altitude adaptation, a subset of which have also been shown to be associated with hemoglobin levels, including EPAS1, EGLN1, and PPARA, which play a role in the HIF-1 pathway. Here, we have extended this work to high- and low-altitude populations living in Ethiopia, for which we have measured hemoglobin levels. We genotyped the Illumina 1M SNP array and employed several genome-wide scans for selection and targeted association with hemoglobin levels to identify genes that play a role in adaptation to high altitude. Results: We have identified a set of candidate genes for positive selection in our high-altitude population sample, demonstrated significantly different hemoglobin levels between high- and low-altitude Ethiopians and have identified a subset of candidate genes for selection, several of which also show suggestive associations with hemoglobin levels. Conclusions: We highlight several candidate genes for involvement in high-altitude adaptation in Ethiopia, including CBARA1, VAV3, ARNT2 and THRB. Although most of these genes have not been identified in previous studies of high-altitude Tibetan or Andean population samples, two of these genes (THRB and ARNT2) play a role in the HIF-1 pathway, a pathway implicated in previous work reported in Tibetan and Andean studies. These combined results suggest that adaptation to high altitude arose independently due to convergent evolution in high-altitude Amhara populations in Ethiopia.