scholarly journals Archaic adaptive introgression in TBX15/WARS2

2015 ◽  
Author(s):  
Fernando Racimo ◽  
David Gokhman ◽  
Matteo Fumagalli ◽  
Amy Ko ◽  
Torben Hansen ◽  
...  

AbstractA recent study conducted the first genome-wide scan for selection in Inuit from Greenland using SNP chip data. Here, we report that selection in the region with the second most extreme signal of positive selection in Greenlandic Inuit favored a deeply divergent haplotype that is closely related to the sequence in the Denisovan genome, and was likely introgressed from an archaic population. The region contains two genes, WARS2 and TBX15, and has previously been associated with adipose tissue differentiation and body-fat distribution in humans. We show that the adaptively introgressed allele has been under selection in a much larger geographic region than just Greenland. Furthermore, it is associated with changes in expression of WARS2 and TBX15 in multiple tissues including the adrenal gland and subcutaneous adipose tissue, and with regional DNA methylation changes in TBX15.

2019 ◽  
Vol 28 (24) ◽  
pp. 4161-4172 ◽  
Author(s):  
Ying Wu ◽  
K Alaine Broadaway ◽  
Chelsea K Raulerson ◽  
Laura J Scott ◽  
Calvin Pan ◽  
...  

Abstract Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist–hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.


2018 ◽  
Vol 1 (4) ◽  
Author(s):  
Na Wu ◽  
Xiangyu Zhai ◽  
Xiao Tan ◽  
Petri Wiklund ◽  
Sulin Cheng

Objective To study whether diet and exercise intervention affect sleep and obesity-related genes’ DNA methylation in overweight and obese men with insomnia symptoms Methods The study participants were a subgroup of a large intervention and consisted of 10 overweight or obesity men aged 34-65 years with insomnia symptoms. They participated in a 6-month progressive aerobic exercise training and individualized dietary consoling program and were randomly selected from diet (n=4), exercise (n=3) and control (n=3) groups. Body composition included fat mass and lean mass in the whole body and abdominal android region were assessed by dual-energy X-ray densitometry. The fitness level (VO2max) was determined by 2-km walk test using a standard protocol. Blood samples from venous were taken at fasted state in the morning. Total cholesterol, high density lipid cholesterol, low density lipid cholesterol, triglycerides, glucose, insulin, non-esterified fatty acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamyltransferase were assessed by conventional methods. Subcutaneous adipose tissue was taken from abdominal region before and after the intervention. DNA was extracted from subcutaneous adipose tissue using a QIAamp DNeasy Tissue Kit. Whole genome-wide DNA methylation was obtained using MethylRAD-Seq. MethylRAD library preparation started from DNA digestion by FspEI, then digested products were run on agarose gel to verify digestion and DNA ligase was added to the digestion solution. After ligation products amplication, PCR was conducted by MyCycler thermal cycler (Bio-Rad). The target fragment was excised from polyacrylamide gel and DNA was diffused from the gel in nuclease-free water. For relative quantification of MethylRAD data, DNA methylation levels were determined using the normalized read depth (reads per million, RPM) for each site. For each restriction site, its methylation level was estimated by dividing the log-transformed depth of each site by the log-transformed maximum depth (representing 100% methylation; i.e. M-index ¼ log(depth site)/ log(depth max)), where depth max was summarized from the top 2% of sites (approx. 500 for the standard library) with the highest sequencing coverage. Heat map images are generated with Matlab 7.0 software and pathways are analysed by WEB-based Gene SeT AnaLysis Toolket. A statistical significance for methylated CpGs and pathways were set to p=0.001 and p=0.05, respectively. Results No significant group differences by time were found in sleep-related variables, body composition, lifestyle factors nor with measured lipid and glucose biomarkers. However, whole genome-wide DNA methylation was decreased after dietary intervention, but was increased after exercise intervention, respectively. Correspondingly, 1253 and 708 differentially methylated loci were found in diet and exercise groups by contrast to the control group. Among them, the overlap genes between diet and exercise had multiple differentially methylated CpGs, including e.g. MYT1L (4 CpGs), CAMTA1 (3 CpGs), NRXN1 (3 CpGs), RPS6KA2 (3 CpGs), SEMA4D (3 CpGs). DNA methylation in PCDH8 was negatively correlated with wake after sleep onset after exercise intervention and MYRIP associated with sleep duration showed lower methylation after the dietary intervention. Further, 13 (DIO1, GCK, GYS1, LMNA, LY86, PNMT, PPARA, PPARD, SERPINE1, TH, TMEM18, TNFRSF1B and UBL5) and 2 (SDCCAG8 and TNF) obesity-related genes’ DNA methylation profile changed in response to diet and exercise, respectively. Percentage changes of CpGs within KLHDC8A, ANKS1A, FGFRL1 and KDM3B were correlated with energy yield fat and carbohydrate, HOMA-IR and VO2max, respectively. Conclusions We found that both exercise and dietary interventions have impacts on these genes related to sleep indicating by DNA methylation in PCDH8 and MYRIP, respectively. Further diet may be more effective than aerobic exercise intervention since greater number of modified obesity-related genes observed after dietary intervention. Our results indicate that reduce insomnia symptoms may need to more focus on control obesity.


2019 ◽  
Author(s):  
Anil K Giri ◽  
Gauri Prasad ◽  
Khushdeep Bandesh ◽  
Vaisak Parekatt ◽  
Anubha Mahajan ◽  
...  

AbstractObesity, a risk factor for various human diseases originates through complex interactions between genes and prevailing environment that varies across populations. Indians exhibit a unique obesity phenotype likely attributed by specific gene pool and environmental factors. Here, we present genome-wide association study (GWAS) of 7,259 Indians to understand the genetic architecture of body mass index (BMI) in the population. Our study revealed novel association of variants in BAI3 (rs6913677) and SLC22A11 (rs2078267) at GWAS significance, and of ZNF45 (rs8100011) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped discovered genetic signatures with DNA methylation patterns of 236 Indian individuals, and analyzed expression of the candidate genes using publicly available data. The variants in BAI3 and SLC22A11 were found to dictate methylation patterns at unique CpGs harboring critical cis- regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were found to overlie repressive chromatin, active enhancer, and active chromatin regions, in that order, in human subcutaneous adipose tissue in ENCODE database. Besides, the identified genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, rs8100011 (ZNF45) acted as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue in GTEx portal, and ZNF45 gene expression showed an inverse correlation with BMI in skeletal muscle of Indian subjects. Further, gene-based GWAS analysis revealed CPS1 and UPP2 as additional leads regulating BMI in Indians. Our study decodes potential genomic mechanisms underlying obesity phenotype in Indians.


Metabolism ◽  
2014 ◽  
Vol 63 (2) ◽  
pp. 263-271 ◽  
Author(s):  
Linn Gillberg ◽  
Stine C. Jacobsen ◽  
Tina Rönn ◽  
Charlotte Brøns ◽  
Allan Vaag

2003 ◽  
Vol 228 (6) ◽  
pp. 710-716 ◽  
Author(s):  
E. Tafeit ◽  
R. Möller ◽  
S. Rackl ◽  
A. Giuliani ◽  
W. Urdl ◽  
...  

The new optical device, Lipometer, permits the noninvasive, quick, safe, and precise measurement of the thickness of subcutaneous adipose tissue (SAT) layers at any given site of the human body. Fifteen anatomically well-defined body sites from neck to calf describe the SAT topography (SAT-Top) like an individual “fingerprint.” SAT-Top was examined in 33 women with polycystic ovary syndrome (PCOS), in 87 age-matched healthy controls and in 20 Type-II diabetic women. SAT-Top differences of these three groups were described, and, based on a hierarchical cluster analysis, two distinctly different groups of PCOS women, a lean (PCOSL) and an obese (PCOSO) cluster, were found. For visual comparison of the different types of body fat distribution, the 15-dimensional body fat information was condensed to a two-dimensional factor plot by factor analysis. For comparison of the PCOS like body fat distribution with the “healthy” fat pattern, the (previously published) SAT-Top results of 590 healthy women and men (20-70 years old) and 162 healthy girls and boys (7-11 years old) were added to the factor plot. PCOSO women showed a SAT-Top pattern very similar to that of women with Type-II diabetes, even though the diabetic women were on average 30 years older. Compared with their healthy controls, SAT-Top of these PCOSO patients was strongly skewed into the android direction, providing significantly decreased leg SAT development and significantly higher upper body obesity. Compared with healthy women, PCOSL patients had significantly lower total SAT development (even though height, weight, and body mass index did not deviate significantly), showing a slightly lowered amount of body fat in the upper region and a highly significant leg SAT reduction. This type of fat pattern is the same as found in girls and boys before developing their sex specific body fat distribution. We conclude that women with PCOS develop an android SAT-Top, but compared in more detail, we found two typical types of body fat distribution: the “childlike” SAT pattern in lean PCOS patients, and the “diabetic” body fat distribution in obese PCOS women.


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