Discovery and functional assessment of a novel adipocyte population driven by intracellular Wnt/ß-catenin signaling in mammals.

Wnt/ß-catenin signaling has been well established as a potent inhibitor of adipogenesis. Here, we identified a population of adipocytes that exhibit persistent activity of Wnt/ß-catenin signaling, as revealed by the Tcf/Lef-GFP reporter allele, in embryonic and adult mouse fat depots, named as Wnt+ adipocytes. We showed that this ß-catenin-mediated signaling activation in these cells is Wnt ligand- and receptor-independent but relies on AKT/mTOR pathway and is essential for cell survival. Such adipocytes are distinct from classical ones in transcriptomic and genomic signatures and can be induced from various sources of mesenchymal stromal cells including human cells. Genetic lineage-tracing and targeted cell ablation studies revealed that these adipocytes convert into beige adipocytes directly and are also required for beige fat recruitment under thermal challenge, demonstrating both cell autonomous and non-cell autonomous roles in adaptive thermogenesis. Furthermore, mice bearing targeted ablation of these adipocytes exhibited glucose intolerance, while mice receiving exogenously supplied such cells manifested enhanced glucose utilization. Our studies uncover a unique adipocyte population in regulating beiging in adipose tissues and systemic glucose homeostasis.

Race and socioeconomic effect on sarcopenia and sarcopenic obesity in the Louisiana Osteoporosis Study (LOS).

BACKGROUND: Sarcopenia and sarcopenic obesity are emerging public health issues. True prevalence rates are unknown and estimates differ substantially between studies. No large-scale single study has compared prevalence rates between whites, blacks, Asians, and Hispanics, as we intend to do here. This study also examined the effects of race and socioeconomic factors on sarcopenia and sarcopenic obesity. METHODS: This study included 10,325 participants from Louisiana. Appendicular lean mass (ASM), measured through dual energy x-ray absorptiometry (DXA) scans, was divided by height squared (ASM/h2) to define sarcopenia. Sarcopenic obesity was defined as sarcopenia plus obesity (waist-to-hip ratio). RESULTS: Overall sarcopenia and sarcopenic obesity rates were 17.6% and 7.0% for males, and 13.7% and 2.5% for females, respectively. The highest sarcopenia and sarcopenic obesity rates were found in Asian males (40.6%, 14.4%) and females (30.1%, 8.0%). The lowest sarcopenic obesity rates were observed in black males (3.7%) and females (0.9%). We found significant associations with sarcopenic obesity in males for age, race, and income; in females, for age, race, and education. CONCLUSIONS: Under one diagnostic definition, the prevalence of sarcopenia and sarcopenic obesity is highest among Asians and lowest amongst blacks. Income and education had significant associations with sarcopenia and sarcopenic obesity, in males and females, respectively.

FISH: fast and accurate diploid genotype imputation via segmental hidden Markov model.

MOTIVATION: Fast and accurate genotype imputation is necessary for facilitating gene-mapping studies, especially with the ever increasing numbers of both common and rare variants generated by high-throughput-sequencing experiments. However, most of the existing imputation approaches suffer from either inaccurate results or heavy computational demand. RESULTS: In this article, aiming to perform fast and accurate genotype-imputation analysis, we propose a novel, fast and yet accurate method to impute diploid genotypes. Specifically, we extend a hidden Markov model that is widely used to describe haplotype structures. But we model hidden states onto single reference haplotypes rather than onto pairs of haplotypes. Consequently the computational complexity is linear to size of reference haplotypes. We further develop an algorithm 'merge-and-recover (MAR)' to speed up the calculation. Working on compact representation of segmental reference haplotypes, the MAR algorithm always calculates an exact form of transition probabilities regardless of partition of segments. Both simulation studies and real-data analyses demonstrated that our proposed method was comparable to most of the existing popular methods in terms of imputation accuracy, but was much more efficient in terms of computation. The MAR algorithm can further speed up the calculation by several folds without loss of accuracy. The proposed method will be useful in large-scale imputation studies with a large number of reference subjects. AVAILABILITY: The implemented multi-threading software FISH is freely available for academic use at https://sites.google.com/site/lzhanghomepage/FISH.

Critical assessment of coalescent simulators in modeling recombination hotspots in genomic sequences.

BACKGROUND: Coalescent simulation is pivotal for understanding population evolutionary models and demographic histories, as well as for developing novel analytical methods for genetic association studies for DNA sequence data. A plethora of coalescent simulators are developed, but selecting the most appropriate program remains challenging. RESULTS: We extensively compared performances of five widely used coalescent simulators - Hudson's ms, msHOT, MaCS, Simcoal2, and fastsimcoal, to provide a practical guide considering three crucial factors, 1) speed, 2) scalability and 3) recombination hotspot position and intensity accuracy. Although ms represents a popular standard coalescent simulator, it lacks the ability to simulate sequences with recombination hotspots. An extended program msHOT has compensated for the deficiency of ms by incorporating recombination hotspots and gene conversion events at arbitrarily chosen locations and intensities, but remains limited in simulating long stretches of DNA sequences. Simcoal2, based on a discrete generation-by-generation approach, could simulate more complex demographic scenarios, but runs comparatively slow. MaCS and fastsimcoal, both built on fast, modified sequential Markov coalescent algorithms to approximate standard coalescent, are much more efficient whilst keeping salient features of msHOT and Simcoal2, respectively. Our simulations demonstrate that they are more advantageous over other programs for a spectrum of evolutionary models. To validate recombination hotspots, LDhat 2.2 rhomap package, sequenceLDhot and Haploview were compared for hotspot detection, and sequenceLDhot exhibited the best performance based on both real and simulated data. CONCLUSIONS: While ms remains an excellent choice for general coalescent simulations of DNA sequences, MaCS and fastsimcoal are much more scalable and flexible in simulating a variety of demographic events under different recombination hotspot models. Furthermore, sequenceLDhot appears to give the most optimal performance in detecting and validating cross-over hotspots.

Efficient utilization of rare variants for detection of disease-related genomic regions.

When testing association between rare variants and diseases, an efficient analytical approach involves considering a set of variants in a genomic region as the unit of analysis. One factor complicating this approach is that the vast majority of rare variants in practical applications are believed to represent background neutral variation. As a result, analyzing a single set with all variants may not represent a powerful approach. Here, we propose two alternative strategies. In the first, we analyze the subsets of rare variants exhaustively. In the second, we categorize variants selectively into two subsets: one in which variants are overrepresented in cases, and the other in which variants are overrepresented in controls. When the proportion of neutral variants is moderate to large we show, by simulations, that the both proposed strategies improve the statistical power over methods analyzing a single set with total variants. When applied to a real sequencing association study, the proposed methods consistently produce smaller p-values than their competitors. When applied to another real sequencing dataset to study the difference of rare allele distributions between ethnic populations, the proposed methods detect the overrepresentation of variants between the CHB (Chinese Han in Beijing) and YRI (Yoruba people of Ibadan) populations with small p-values. Additional analyses suggest that there is no difference between the CHB and CHD (Chinese Han in Denver) datasets, as expected. Finally, when applied to the CHB and JPT (Japanese people in Tokyo) populations, existing methods fail to detect any difference, while it is detected by the proposed methods in several regions.

Bayesian mapping of quantitative trait loci for multiple complex traits with the use of variance components.

Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.

Incorporating single-locus tests into haplotype cladistic analysis in case-control studies.

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Assessment of genetic linkage and parent-of-origin effects on obesity.

CONTEXT: Obesity is a growing health care problem worldwide and is a major underlying risk factor for common diseases such as diabetes. Parent-of-origin effect has been reported to be involved in the development of obesity. But the genes with imprinting effects related to obesity are largely unknown. OBJECTIVE: The objective of the study was to identify obesity-related genetic loci, both with and without imprinting effects. DESIGN AND SUBJECTS: We conducted genome-wide linkage analyses for obesity with and without consideration of imprinting effects in a large sample including more than 4000 individuals. In addition to body mass index (BMI), we also used a more stringent and accurate obesity definition, which simultaneously considers BMI and percentage of fat mass (PFM) in a gender-specific manner. Simulations were performed to identify the genome-wide significant and suggestive significant thresholds. RESULTS: In nonimprinted linkage analyses, we detected suggestive linkage at 2q31 (LOD = 2.23) and 16q22 (LOD = 1.87) for BMI and 2q37 (LOD = 2.23) for BMI and PFM. Interestingly, 2q37 also achieved a significant maternal linkage with BMI and PFM (LOD=3.34) in imprinted linkage analyses. Imprinted linkage analyses revealed suggestive linkage evidence for BMI at three additional genomic regions, including 3p14 (LOD = 2.20, paternal), 3q24 (LOD = 1.97, maternal), and 19q13 (LOD = 1.81, maternal). CONCLUSION: We reported linkage and imprinting effects for obesity on several chromosome regions and suggested the potential importance of parent-of-origin effects and phenotype definition of obesity in delineating the genetic basis of obesity.

The genetics of osteoporosis.

Osteoporosis is a common health problem among the elderly, especially postmenopausal women. It is characterized by fragile bones susceptible to low-trauma fractures. Osteoporosis is a complex disease determined by genetic and environmental factors, as well as the possible interactions among these factors. Twin and family studies have shown that genetic factors play an important role in osteoporosis. Numerous genetic studies have been performed to hunt for genes underlying the disease risk. In this review, we briefly summarize and discuss the current status of knowledge about osteoporosis, with special emphasis on the progress achieved recently in: 1) heritability and choice of osteoporosis study phenotypes; 2) the approaches for gene mapping and identification for osteoporosis; 3) the candidate gene association studies; 4) the linkage mapping studies for osteoporosis; 5) some potential explanations for the inconsistent results; and 6) the pharmacogenetic studies for osteoporosis.