An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA Collapse

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Grants

• R01 HL143885 NHLBI NIH HHS
• R01 HL163262 NHLBI NIH HHS
• R01 HL142302 NHLBI NIH HHS
• R01 HL151152 NHLBI NIH HHS
• N01HC25195 NHLBI NIH HHS
• R01 HG010297 NHGRI NIH HHS
• R01 HL092577 NHLBI NIH HHS
• P20 MD000170 NIMHD NIH HHS
• 75N92019D00031 NHLBI NIH HHS
• R01 HD057194 NICHD NIH HHS
• UL1 TR000371 NCATS NIH HHS
• R01 DK122503 NIDDK NIH HHS

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Affiliation(s)

Hanfei Xu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Shreyash Gupta Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ian Dinsmore Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Abbey Kollu Department of Psychology and Neuroscience, University of North Carolina, 235 E. Cameron Avenue, Chapel Hill, NC, 27599, USA
Anne Marie Cawley Marsico Lung Institute, University of North Carolina, 125 Mason Farm Rd, Chapel Hill, NC, 27599, USA
Mohammad Y. Anwar Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Hung-Hsin Chen Institute of Biomedical Sciences, Academia Sinica, No. 128, Section 2, Academia Rd., Taipei, Nangang District, 115201, Taiwan Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Lauren E. Petty Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Sudha Seshadri Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, UT Health San Antonio, 8300 Floyd Curl Drive, San Antonio, TX, 78229, USA
Misa Graff Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Piper Below Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, 1161 21st Ave S, Nashville, TN, 37232, USA
Jennifer A. Brody Department of Medicine, Cardiovascular Health Research Unit, University of Washington, 1730 Minor Ave, Seattle, WA, 98101, USA
Geetha Chittoor Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Susan P. Fisher-Hoch Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Nancy L. Heard-Costa Framingham Heart Study, 73 Mt Wayte Ave, Framingham, MA, 01702, USA Department of Neurology, Chobanian & Avedisian School of Medicine, Boston University, 72 E Concord St, Boston, MA, 02118, USA
Daniel Levy Population Sciences Branch, National Heart, Lung, and Blood Institute of the National Institutes of Health, 6701 Rockledge Drive, Bethesda, MD, 20892, USA
Honghuang Lin Department of Medicine, University of Massachusetts Chan Medical School, 55 N Lake Ave, Worcester, MA, 01655, USA
Ruth JF. Loos Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark
Joseph B. Mccormick Department of Epidemiology, School of Public Health, UT Health Houston, Regional Academic Health Center, One West University Blvd, Brownsville, TX, 78520, USA
Jerome I. Rotter Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 West Carson Street, Torrance, CA, 90502, USA
Tooraj Mirshahi Department of Genomic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Christopher D. Still Center for Obesity and Metabolic Health, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Anita Destefano Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA Department of Neurology, School of Medicine, Boston University, 85 East Concord Street, Boston, MA, 02118, USA
L. Adrienne Cupples Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA
Karen L Mohlke Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC, 27599, USA
Kari E. North Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 135 Dauer Drive, Chapel Hill, NC, 27599, USA
Anne E. Justice Department of Population Health Sciences, Geisinger, 100 N. Academy Ave., Danville, PA, 17822, USA
Ching-Ti Liu Department of Biostatistics, School of Public Health, Boston University, 801 Massachusettes Ave, Boston, MA, 02118, USA

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TET3 boosts hepatocyte autophagy and impairs non-alcoholic fatty liver disease by increasing ENPP1 promoter hypomethylation

BACKGROUND

Dysregulated ecto-nucleotide pyrophosphatase/phosphodiesterase (ENPP) family occurs in metabolic reprogramming pathological processes. Nonetheless, the epigenetic mechanisms by which ENPP family impacts NAFLD, also known as metabolic dysfunction-associated steatotic liver disease (MASLD), is poorly appreciated.

METHODS

We investigated the causes and consequences of ENPP1 promoter hypomethylation may boost NAFLD using NAFLD clinical samples, as well as revealed the underlying mechanisms using high-fat diet (HFD) + carbon tetrachloride (CCl4) induced mouse model of NAFLD and FFA treatment of cultured hepatocyte.

RESULTS

Herein, we report that the expression level of ENPP1 are increased in patients with NAFLD liver tissue and in mouse model of NAFLD. Hypomethylation of ENPP1, is associated with the perpetuation of hepatocyte autophagy and liver fibrosis in the NAFLD. ENPP1 hypomethylation is mediated by the DNA demethylase TET3 in NAFLD liver fibrosis and hepatocyte autophagy. Additionally, knockdown of TET3 methylated ENPP1 promoter, reduced the ENPP1 expression, ameliorated the experimental NAFLD. Mechanistically, TET3 epigenetically promoted ENPP1 expression via hypomethylation of the promoter. Knocking down TET3 can inhibit the hepatocyte autophagy but an overexpression of ENPP1 showing rescue effect.

CONCLUSIONS

We describe a novel epigenetic mechanism wherein TET3 promoted ENPP1 expression through promoter hypomethylation is a critical mediator of NAFLD. Our findings provide new insight into the development of preventative measures for NAFLD.

Proteomic Profiling of Thigh Meat at Different Ages of Chicken for Meat Quality and Development

Chicken age contributes to the meat characteristics; however, knowledge regarding the pathways and proteins associated with meat quality and muscle development are still scarce, especially in chicken thigh meat. Hence, the objective of this study was to elucidate the intricate relationship between these traits by liquid chromatography mass spectrometry at three different ages. A total of 341 differential expressed proteins (DEPs) were screened out (fold change ≥ 1.50 or ≤0.67 and p < 0.05) among 45 thigh meat samples (15 samples per age) of Beijing-You chicken (BYC), collected at the age of 150, 300, or 450 days (D150, D300, and D450), respectively. Subsequently, based on the protein interaction network and Markov cluster algorithm (MCL) analyses, 91 DEPs were divided into 26 MCL clusters, which were associated with pathways of lipid transporter activity, nutrient reservoir activity, signaling pathways of PPAR and MAPK, focal adhesion, ECM-receptor interaction, the cell cycle, oocyte meiosis, ribosomes, taurine and hypotaurine metabolism, glutathione metabolism, muscle contraction, calcium signaling, nucleic acid binding, and spliceosomes. Overall, our data suggest that the thigh meat of BYC at D450 presents the most desirable nutritional value in the term of free amino acids (FAAs) and intramuscular fat (IMF), and a series of proteins and pathways associated with meat quality and development were identified. These findings also provide comprehensive insight regarding these traits across a wide age spectrum.

RNA-Sequencing Reveals Gene Expression and Pathway Signatures in Umbilical Cord Blood Affected by Birth Delivery Mode

Cesarean section (CS) confers increased risk of type I diabetes, asthma, inflammatory bowel disease, celiac disease, overweight and obesity, etc., in the offspring. However, the underlying mechanism remains unknown. To investigate the influence of CS on gene expression in cord blood, we have performed RNA-sequencing followed by single-gene analysis, gene set enrichment analysis, gene co-expression network analysis, and interacting genes/proteins analysis in eight full-term infants born by elective CS and eight matched vaginally delivered (VD) infants. Crucial genes identified above were further validated in another 20 CS and 20 VD infants. We found for the first time that mRNA expression of genes involved in immune response (IL12A, INFG, IL1B, TNF, MIF, IL4, CA1, IFI27, HLA-DOB and EPHB1) and metabolism (DLK1, CYP2A6 and GATM) were significantly influenced by CS. Notably, serum TNF-α and IFN-γ were remarkably up-regulated in the CS infants (p = 5.0 × 10-4 and 3.0 × 10-3, respectively) compared to the VD infants. It is biologically plausible that CS may exert adverse impacts on offspring health through influencing expression of genes in the above processes. These findings will help understand the potential underlying mechanisms of the adverse health impacts of CS and identify biomarkers for future health of offspring born with different delivery modes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-022-00086-7.

Genome-wide assessment reveals a significant association between ACSS3 and physical activity

Recent genetic studies have identified physical activity (PA)-susceptible loci in European ancestry subjects; however, due to considerable genetic differences, these findings are not likely extendable to East Asian populations. Therefore, the present study aimed to identify significantly associated PA-susceptible loci using genome-wide association studies (GWASs) with East Asian (EAS) subjects and to generalize the findings to European (EUR) ancestries. The mRNA levels of genes located near the genome-wide significantly associated single-nucleotide polymorphisms (SNP) were compared under PA and control conditions. Rs74937256, located in ACSS3 (chromosome 12), which primarily functions in skeletal muscle tissues, was identified as a genome-wide significant variant (P = 6.06 × 10^-9 ) in EAS. Additionally, the rs2525840, also in ACSS3 satisfied the Bonferroni corrected significance (P = 3.77 × 10^-5 ) in EUR. We found that rs74937256 is an expressed trait locus of ACSS3 (P = 10^-4 ), and ACSS3 mRNA expression significantly differs after PA, based on PrediXcan (P = 7 × 10^-8 ) and the gene expression omnibus database (P = 0.043).

Identification of key sex-specific pathways and genes in the subcutaneous adipose tissue from pigs using WGCNA method

Background

Adipose tissues (ATs), including visceral ATs (VATs) and subcutaneous ATs (SATs), are crucial for maintaining energy and metabolic homeostasis. SATs have been found to be closely related to obesity and obesity-induced metabolic disease. Some studies have shown a significant association between subcutaneous fat metabolism and sexes. However, the molecular mechanisms for this association are still unclear. Here, using the pig as a model, we investigated the systematic association between the subcutaneous fat metabolism and sexes, and identified some key sex-specific pathways and genes in the SATs from pigs.

Results

The results revealed that 134 differentially expressed genes (DEGs) were identified in female and male pigs from the obese group. A total of 17 coexpression modules were detected, of which six modules were significantly correlated with the sexes (P < 0.01). Among the significant modules, the greenyellow module (cor = 0.68, P < 9e-06) and green module (cor = 0.49, P < 0.003) were most significantly positively correlated with the male and female, respectively. Functional analysis showed that one GO term and four KEGG pathways were significantly enriched in the greenyellow module while six GO terms and six KEGG pathways were significantly enriched in the green module. Furthermore, a total of five and two key sex-specific genes were identified in the two modules, respectively. Two key sex-specific pathways (Ras-MAPK signaling pathway and type I interferon response) play an important role in the SATs of males and females, respectively.

Conclusions

The present study identified some key sex-specific pathways and genes in the SATs from pigs, which provided some new insights into the molecular mechanism of being involved in fat formation and immunoregulation between pigs of different sexes. These findings may be beneficial to breeding in the pig industry and obesity treatment in medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01054-w.

Johne's Disease in Dairy Cattle: An Immunogenetic Perspective

Johne's disease (JD), also known as paratuberculosis, is a severe production-limiting disease with significant economic and welfare implications for the global cattle industry. Caused by infection with Mycobacterium avium subspecies paratuberculosis (MAP), JD manifests as chronic enteritis in infected cattle. In addition to the economic losses and animal welfare issues associated with JD, MAP has attracted public health concerns with potential association with Crohn's disease, a human inflammatory bowel disease. The lack of effective treatment options, such as a vaccine, has hampered JD control resulting in its increasing global prevalence. The disease was first reported in 1895, but in recognition of its growing economic impact, extensive recent research facilitated by a revolution in technological approaches has led to significantly enhanced understanding of the immunological, genetic, and pathogen factors influencing disease pathogenesis. This knowledge has been derived from a variety of diverse models to elucidate host-pathogen interactions including in vivo and in vitro experimental infection models, studies measuring immune parameters in naturally-infected animals, and by studies conducted at the population level to enable the estimation of genetic parameters, and the identification of genetic markers and quantitative trait loci (QTL) putatively associated with susceptibility or resistance to JD. The main objectives of this review are to summarize these recent developments from an immunogenetics perspective and attempt to extract the principal and common findings emerging from this wealth of recent information. Based on these analyses, and in light of emerging technologies such as gene-editing, we conclude by discussing potential future avenues for effectively mitigating JD in cattle.

Genome-wide identification of major genes and genomic prediction using high-density and text-mined gene-based SNP panels in Hanwoo (Korean cattle)

It was hypothesized that single-nucleotide polymorphisms (SNPs) extracted from text-mined genes could be more tightly related to causal variant for each trait and that differentially weighting of this SNP panel in the GBLUP model could improve the performance of genomic prediction in cattle. Fitting two GRMs constructed by text-mined SNPs and SNPs except text-mined SNPs from 777k SNPs set (exp_777K) as different random effects showed better accuracy than fitting one GRM (Im_777K) for six traits (e.g. backfat thickness: + 0.002, eye muscle area: + 0.014, Warner–Bratzler Shear Force of semimembranosus and longissimus dorsi: + 0.024 and + 0.068, intramuscular fat content of semimembranosus and longissimus dorsi: + 0.008 and + 0.018). These results can suggest that attempts to incorporate text mining into genomic predictions seem valuable, and further study using text mining can be expected to present the significant results.

GWA-based pleiotropic analysis identified potential SNPs and genes related to type 2 diabetes and obesity

Metabolic syndrome is a cluster of symptoms including excessive body fat and insulin resistance which may lead to obesity and type 2 diabetes (T2D). The physiological and pathological cross-talk between T2D and obesity is crucial and complex, meanwhile, the genetic connection between T2D and obesity is largely unknown. The purpose of this study is to identify pleiotropic SNPs and genes between these two associated conditions by applying genetic analysis incorporating pleiotropy and annotation (GPA) on two large genome-wide association studies (GWAS) data sets: a body mass index (BMI) data set containing 339,224 subjects and a T2D data set containing 110,452 subjects. In all, 5182 SNPs showed pleiotropy in both T2D and obesity. After further prioritization based on suggested local false discovery rates (FDR) by the GPA model, 2146 SNPs corresponding to 217 unique genes are significantly associated with both traits (FDR < 0.2), among which 187 are newly identified pleiotropic genes compare with original GWAS in individual traits. Subsequently, gene enrichment and pathway analyses highlighted several pleiotropic SNPs including rs849135 (FDR = 0.0002), rs2119812 (FDR = 0.0018), rs4506565 (FDR = 1.23E-08), rs1558902 (7.23E-10) and corresponding genes JAZF1, SYN2, TCF7L2, FTO which may play crucial rol5es in the etiology of both T2D and obesity. Additional evidences from expression data analysis of pleiotropic genes strongly supports that the pleiotropic genes including JAZF1 (p = 1.39E-05 and p = 2.13E-05), SYN2 (p = 5.49E-03 and p = 5.27E-04), CDKN2C (p = 1.99E-12 and p = 6.27E-11), RABGAP1 (p = 3.08E-03 and p = 7.46E-03), and UBE2E2 (p = 1.83E-04 and p = 8.22E-03) play crucial roles in both obesity and T2D pathogenesis. Pleiotropic analysis integrated with functional network identified several novel and causal SNPs and genes involved in both BMI and T2D which may be ignored in single GWAS.

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Feed efficiency (FE) is a key trait in pig production, as improvement in FE has positive economic and environmental impact. FE is a complex phenotype and testing animals for FE is costly. Therefore, there has been a desire to find functionally relevant single nucleotide polymorphisms (SNPs) as biomarkers, to improve our biological understanding of FE as well as accuracy of genomic prediction for FE. We have performed a cis- and trans- eQTL (expression quantitative trait loci) analysis, in a population of Danbred Durocs (N = 11) and Danbred Landrace (N = 27) using both a linear and ANOVA model based on muscle tissue RNA-seq. We analyzed a total of 1425x19179 or 2.7x10⁷ Gene-SNP combinations in eQTL detection models for FE. The 1425 genes were from RNA-Seq based differential gene expression analyses using 25880 genes related to FE and additionally combined with mitochondrial genes. The 19179 SNPs were from applying stringent quality control and linkage disequilibrium filtering on genotype data using a GGP Porcine HD 70k SNP array. We applied 1000 fold bootstrapping and enrichment analysis to further validate and analyze our detected eQTLs. We identified 13 eQTLs with FDR < 0.1, affecting several genes found in previous studies of commercial pig breeds. Examples include MYO19, CPT1B, ACSL1, IER5L, CPT1A, SUCLA2, CSRNP1, PARK7 and MFF. The bootstrapping results showed statistically significant enrichment (p-value<2.2x10^-16) of eQTLs with p-value < 0.01 in both cis and trans-eQTLs. Enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This included transcription factors (p-value = 1.0x10^-13), DNA-binding (GO:0003677, p-value = 8.9x10^-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10^-16), negative regulation of expression (GO:0010629, p-value<2.2x10^-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans eQTLs.

Integrative omics approaches provide biological and clinical insights: examples from mitochondrial diseases

High-throughput technologies for genomics, transcriptomics, proteomics, and metabolomics, and integrative analysis of these data, enable new, systems-level insights into disease pathogenesis. Mitochondrial diseases are an excellent target for hypothesis-generating omics approaches, as the disease group is mechanistically exceptionally complex. Although the genetic background in mitochondrial diseases is in either the nuclear or the mitochondrial genome, the typical downstream effect is dysfunction of the mitochondrial respiratory chain. However, the clinical manifestations show unprecedented variability, including either systemic or tissue-specific effects across multiple organ systems, with mild to severe symptoms, and occurring at any age. So far, the omics approaches have provided mechanistic understanding of tissue-specificity and potential treatment options for mitochondrial diseases, such as metabolome remodeling. However, no curative treatments exist, suggesting that novel approaches are needed. In this Review, we discuss omics approaches and discoveries with the potential to elucidate mechanisms of and therapies for mitochondrial diseases.

Characterization of eQTLs associated with androstenone by RNA sequencing in porcine testis

Characterization of genetic variants affecting genome-wide gene expression levels (expression quantitative trait loci or eQTLs) in pig testes may improve our understanding of genetic architecture of boar taint (an animal welfare trait) and helps in genome-assisted or genomic selection programs. The aims of this study were to identify eQTLs associated with androstenone, to find candidate eQTLs for low androstenone, and to validate the top eQTL by reverse transcriptase quantitative PCR (RT-qPCR). Gene expression profiles were obtained by RNA sequencing in testis from Danish cross-bred pigs and genotype data by 80K single nucleotide polymorphism panel. A total of 262 eQTLs [false discovery rate (FDR) < 0.05] were identified by using two software packages: Matrix eQTL and Krux eQTL. Of these, 149 cis-acting eQTLs were significantly associated with androstenone concentrations and gene expression (FDR < 0.05). The eQTLs were associated with several genes of boar taint relevance including CYP1A2, CYB5D1, and SPHK2. One eQTL gene, AMPH, was differentially expressed (FDR < 0.05) and affected by chicory. Five candidate eQTLs associated with low androstenone concentrations were discovered, including the top eQTL associated with CYP1A2. RT-qPCR confirmed target gene expression to be significantly (P < 0.05) different based on eQTL genotypes. Furthermore, eQTLs were enriched as QTLs for 15 boar taint related traits from the PigQTLdb. This is the first study to report eQTLs in testes of commercial crossbred pigs used in pork production and to reveal genetic architecture of boar taint. Potential applications include development of a DNA test and in advanced genomic selection models for boar taint.

Effect of single-nucleotide polymorphisms on specific reproduction parameters in Hungarian Large White sows

The aim of this study was to reveal the effect of single-nucleotide polymorphisms (SNPs) on the total number of piglets born (TNB), the litter weight born alive (LWA), the number of piglets born dead (NBD), the average litter weight on the 21st day (M21D) and the interval between litters (IBL). Genotypes were determined on a high-density Illumina Porcine SNP 60K BeadChip. Data screening and data identification were performed by a multi-locus mixed-model. Statistical analyses were carried out to find associations between individual genotypes of 290 Hungarian Large White sows and the investigated reproduction parameters. According to the analysis outcome, three SNPs were identified to be associated with TNB. These loci are located on chromosomes 1, 6 and 13 (-log₁₀P = 6.0, 7.86 and 6.22, the frequencies of their minor alleles, MAF, were 0.298, 0.299 and 0.364, respectively). Two loci showed considerable association (-log₁₀P = 10.35 and 10.46) with LWA on chromosomes 5 and X, the MAF were 0.425 and 0.446, respectively. Seven loci were found to be associated with NBD. These loci are located on chromosomes 5, 6, 13, 14, 15, 16 and 18 (-log₁₀P = 10.95, 5.43, 8.29, 6.72, 6.81, 5.90, and 5.15, respectively). One locus showed association (-log₁₀P = 5.62) with M21D on chromosome 1 (the MAF was 0.461). Another locus was found to be associated with IBL on chromosome 8 (-log₁₀P = 7.56; the MAF was 0.438). The above-mentioned loci provide a straightforward possibility to assist selection by molecular tools and, consequently, to improve the competitiveness of the Hungarian Large White (HLW) breed.

Sequence analysis and structure prediction of ABHD16A and the roles of the ABHD family members in human disease

Abhydrolase domain containing 16A (ABHD16A) is a member of the α/β hydrolase domain-containing (ABHD) protein family and is expressed in a variety of animal cells. Studies have shown that ABHD16A has acylglycerol lipase and phosphatidylserine lipase activities. Its gene location in the main histocompatibility complex (MHC) III gene cluster suggests that this protein may participate in the immunomodulation of the body. The results of studies investigating nearly 20 species of ABHDs reveal that the ABHD proteins are key factors in metabolic regulation and disease occurrence and development. In this paper, we summarize the related progress regarding the function of ABHD16A and other ABHD proteins. A prediction of the active sites and structural domains of ABHD16A and an analysis of the amino acid sites are included. Moreover, we analysed the amino acid sequences of the ABHD16A molecules in different species and provide an overview of the related functions and diseases associated with these proteins. The functions and diseases related to ABHD are systematically summarized and highlighted. Future research directions for studies investigating the functions and mechanisms of these proteins are also suggested. Further studies investigating the function of ABHD proteins may further confirm their positions as important determinants of lipid metabolism and related diseases.

Genetic control of longissimus dorsi muscle gene expression variation and joint analysis with phenotypic quantitative trait loci in pigs

Background

Economically important growth and meat quality traits in pigs are controlled by cascading molecular events occurring during development and continuing throughout the conversion of muscle to meat. However, little is known about the genes and molecular mechanisms involved in this process. Evaluating transcriptomic profiles of skeletal muscle during the initial steps leading to the conversion of muscle to meat can identify key regulators of polygenic phenotypes. In addition, mapping transcript abundance through genome-wide association analysis using high-density marker genotypes allows identification of genomic regions that control gene expression, referred to as expression quantitative trait loci (eQTL). In this study, we perform eQTL analyses to identify potential candidate genes and molecular markers regulating growth and meat quality traits in pigs.

Results

Messenger RNA transcripts obtained with RNA-seq of longissimus dorsi muscle from 168 F2 animals from a Duroc x Pietrain pig resource population were used to estimate gene expression variation subject to genetic control by mapping eQTL. A total of 339 eQTL were mapped (FDR ≤ 0.01) with 191 exhibiting local-acting regulation. Joint analysis of eQTL with phenotypic QTL (pQTL) segregating in our population revealed 16 genes significantly associated with 21 pQTL for meat quality, carcass composition and growth traits. Ten of these pQTL were for meat quality phenotypes that co-localized with one eQTL on SSC2 (8.8-Mb region) and 11 eQTL on SSC15 (121-Mb region). Biological processes identified for co-localized eQTL genes include calcium signaling (FERM, MRLN, PKP2 and CHRNA9), energy metabolism (SUCLG2 and PFKFB3) and redox hemostasis (NQO1 and CEP128), and results support an important role for activation of the PI3K-Akt-mTOR signaling pathway during the initial conversion of muscle to meat.

Conclusion

Co-localization of eQTL with pQTL identified molecular markers significantly associated with both economically important phenotypes and gene transcript abundance. This study reveals candidate genes contributing to variation in pig production traits, and provides new knowledge regarding the genetic architecture of meat quality phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5386-2) contains supplementary material, which is available to authorized users.

Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease

It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.

Co-Expression Network Analysis Identifies miRNA⁻mRNA Networks Potentially Regulating Milk Traits and Blood Metabolites

MicroRNAs (miRNA) regulate mRNA networks to coordinate cellular functions. In this study, we constructed gene co-expression networks to detect miRNA modules (clusters of miRNAs with similar expression patterns) and miRNA–mRNA pairs associated with blood (triacylglyceride and nonesterified fatty acids) and milk (milk yield, fat, protein, and lactose) components and milk fatty acid traits following dietary supplementation of cows’ diets with 5% linseed oil (LSO) (n = 6 cows) or 5% safflower oil (SFO) (n = 6 cows) for 28 days. Using miRNA transcriptome data from mammary tissues of cows for co-expression network analysis, we identified three consensus modules: blue, brown, and turquoise, composed of 70, 34, and 86 miRNA members, respectively. The hub miRNAs (miRNAs with the most connections with other miRNAs) were miR-30d, miR-484 and miR-16b for blue, brown, and turquoise modules, respectively. Cell cycle arrest, and p53 signaling and transforming growth factor–beta (TGF-β) signaling pathways were the common gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for target genes of the three modules. Protein percent (p = 0.03) correlated with the turquoise module in LSO treatment while protein yield (p = 0.003) and milk yield (p = 7 × 10⁻⁰⁴) correlated with the turquoise model, protein and milk yields and lactose percent (p < 0.05) correlated with the blue module and fat percent (p = 0.04) correlated with the brown module in SFO treatment. Several fatty acids correlated (p < 0.05) with the blue (CLA:9,11) and brown (C4:0, C12:0, C22:0, C18:1n9c and CLA:10,12) modules in LSO treatment and with the turquoise (C14:0, C18:3n3 and CLA:9,11), blue (C14:0 and C23:0) and brown (C6:0, C16:0, C22:0, C22:6n3 and CLA:10,12) modules in SFO treatment. Correlation of miRNA and mRNA data from the same animals identified the following miRNA–mRNA pairs: miR-183/RHBDD2 (p = 0.003), miR-484/EIF1AD (p = 0.011) and miR-130a/SBSPON (p = 0.004) with lowest p-values for the blue, brown, and turquoise modules, respectively. Milk yield, protein yield, and protein percentage correlated (p < 0.05) with 28, 31 and 5 miRNA–mRNA pairs, respectively. Our results suggest that, the blue, brown, and turquoise modules miRNAs, hub miRNAs, miRNA–mRNA networks, cell cycle arrest GO term, p53 signaling and TGF-β signaling pathways have considerable influence on milk and blood phenotypes following dietary supplementation of dairy cows’ diets with 5% LSO or 5% SFO.

Post-genomic behavioral genetics: From revolution to routine

What was once expensive and revolutionary-full-genome sequence-is now affordable and routine. Costs will continue to drop, opening up new frontiers in behavioral genetics. This shift in costs from the genome to the phenome is most notable in large clinical studies of behavior and associated diseases in cohorts that exceed hundreds of thousands of subjects. Examples include the Women's Health Initiative (www.whi.org), the Million Veterans Program (www.

RESEARCH

va.gov/MVP), the 100 000 Genomes Project (genomicsengland.co.uk) and commercial efforts such as those by deCode (www.decode.com) and 23andme (www.23andme.com). The same transition is happening in experimental neuro- and behavioral genetics, and sample sizes of many hundreds of cases are becoming routine (www.genenetwork.org, www.mousephenotyping.org). There are two major consequences of this new affordability of massive omics datasets: (1) it is now far more practical to explore genetic modulation of behavioral differences and the key role of gene-by-environment interactions. Researchers are already doing the hard part-the quantitative analysis of behavior. Adding the omics component can provide powerful links to molecules, cells, circuits and even better treatment. (2) There is an acute need to highlight and train behavioral scientists in how best to exploit new omics approaches. This review addresses this second issue and highlights several new trends and opportunities that will be of interest to experts in animal and human behaviors.

Systems genomics study reveals expression quantitative trait loci, regulator genes and pathways associated with boar taint in pigs

Boar taint is an offensive odour and/or taste from a proportion of non-castrated male pigs caused by skatole and androstenone accumulation during sexual maturity. Castration is widely used to avoid boar taint but is currently under debate because of animal welfare concerns. This study aimed to identify expression quantitative trait loci (eQTLs) with potential effects on boar taint compounds to improve breeding possibilities for reduced boar taint. Danish Landrace male boars with low, medium and high genetic merit for skatole and human nose score (HNS) were slaughtered at ~100 kg. Gene expression profiles were obtained by RNA-Seq, and genotype data were obtained by an Illumina 60K Porcine SNP chip. Following quality control and filtering, 10,545 and 12,731 genes from liver and testis were included in the eQTL analysis, together with 20,827 SNP variants. A total of 205 and 109 single-tissue eQTLs associated with 102 and 58 unique genes were identified in liver and testis, respectively. By employing a multivariate Bayesian hierarchical model, 26 eQTLs were identified as significant multi-tissue eQTLs. The highest densities of eQTLs were found on pig chromosomes SSC12, SSC1, SSC13, SSC9 and SSC14. Functional characterisation of eQTLs revealed functions within regulation of androgen and the intracellular steroid hormone receptor signalling pathway and of xenobiotic metabolism by cytochrome P450 system and cellular response to oestradiol. A QTL enrichment test revealed 89 QTL traits curated by the Animal Genome PigQTL database to be significantly overlapped by the genomic coordinates of cis-acting eQTLs. Finally, a subset of 35 cis-acting eQTLs overlapped with known boar taint QTL traits. These eQTLs could be useful in the development of a DNA test for boar taint but careful monitoring of other overlapping QTL traits should be performed to avoid any negative consequences of selection.

Chromosomal microarray analysis in the genetic evaluation of 279 patients with syndromic obesity

BACKGROUND

Syndromic obesity is an umbrella term used to describe cases where obesity occurs with additional phenotypes. It often arises as part of a distinct genetic syndrome with Prader-Willi syndrome being a classical example. These rare forms of obesity provide a unique source for identifying obesity-related genetic changes. Chromosomal microarray analysis (CMA) has allowed the characterization of new genetic forms of syndromic obesity, which are due to copy number variants (CNVs); however, CMA in large cohorts requires more study. The aim of this study was to characterize the CNVs detected by CMA in 279 patients with a syndromic obesity phenotype.

RESULTS

Pathogenic CNVs were detected in 61 patients (22%) and, among them, 35 had overlapping/recurrent CNVs. Genomic imbalance disorders known to cause syndromic obesity were found in 8.2% of cases, most commonly deletions of 1p36, 2q37 and 17p11.2 (5.4%), and we also detected deletions at 1p21.3, 2p25.3, 6q16, 9q34, 16p11.2 distal and proximal, as well as an unbalanced translocation resulting in duplication of the GNB3 gene responsible for a syndromic for of childhood obesity. Deletions of 9p terminal and 22q11.2 proximal/distal were found in 1% and 3% of cases, respectively. They thus emerge as being new putative obesity-susceptibility loci. We found additional CNVs in our study that overlapped with CNVs previously reported in cases of syndromic obesity, including a new case of 13q34 deletion (CHAMP1), bringing to 7 the number of patients in whom such defects have been described in association with obesity. Our findings implicate many genes previously associated with obesity (e.g. PTBP2, TMEM18, MYT1L, POU3F2, SIM1, SH2B1), and also identified other potentially relevant candidates including TAS1R3, ALOX5AP, and GAS6.

CONCLUSION

Understanding the genetics of obesity has proven difficult, and considerable insight has been obtained from the study of genomic disorders with obesity associated as part of the phenotype. In our study, CNVs known to be causal for syndromic obesity were detected in 8.2% of patients, but we provide evidence for a genetic basis of obesity in as many as 14% of cases. Overall, our results underscore the genetic heterogeneity in syndromic forms of obesity, which imposes a substantial challenge for diagnosis.

The contribution of genetics and environment to obesity

BACKGROUND

Obesity is a global health problem mainly attributed to lifestyle changes such as diet, low physical activity or socioeconomics factors. However, several evidences consistently showed that genetics contributes significantly to the weight-gain susceptibility.

SOURCES OF DATA

A systematic literature search of most relevant original, review and meta-analysis, restricted to English was conducted in PubMed, Web of Science and Google scholar up to May 2017 concerning the contribution of genetics and environmental factors to obesity.

AREAS OF AGREEMENT

Several evidences suggest that obesogenic environments contribute to the development of an obese phenotype. However, not every individual from the same population, despite sharing the same obesogenic environment, develop obesity.

AREAS OF CONTROVERSY

After more than 10 years of investigation on the genetics of obesity, the variants found associated with obesity represent only 3% of the estimated BMI-heritability, which is around 47-80%. Moreover, genetic factors per se were unable to explain the rapid spread of obesity prevalence.

GROWING POINTS

The integration of multi-omics data enables scientists having a better picture and to elucidate unknown pathways contributing to obesity.

AREAS TIMELY FOR DEVELOPING RESEARCH

New studies based on case-control or gene candidate approach will be important to identify new variants associated with obesity susceptibility and consequently unveiling its genetic architecture. This will lead to an improvement of our understanding about underlying mechanisms involved in development and origin of the actual obesity epidemic. The integration of several omics will also provide insights about the interplay between genes and environments contributing to the obese phenotype.

Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle

Background

Single nucleotide polymorphism (SNP) arrays for domestic cattle have catalyzed the identification of genetic markers associated with complex traits for inclusion in modern breeding and selection programs. Using actual and imputed Illumina 778K genotypes for 3887 U.S. beef cattle from 3 populations (Angus, Hereford, SimAngus), we performed genome-wide association analyses for feed efficiency and growth traits including average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), and residual feed intake (RFI), with marker-based heritability estimates produced for all traits and populations.

Results

Moderate and/or large-effect QTL were detected for all traits in all populations, as jointly defined by the estimated proportion of variance explained (PVE) by marker effects (PVE ≥ 1.0%) and a nominal P-value threshold (P ≤ 5e-05). Lead SNPs with PVE ≥ 2.0% were considered putative evidence of large-effect QTL (n = 52), whereas those with PVE ≥ 1.0% but < 2.0% were considered putative evidence for moderate-effect QTL (n = 35). Identical or proximal lead SNPs associated with ADG, DMI, MMWT, and RFI collectively supported the potential for either pleiotropic QTL, or independent but proximal causal mutations for multiple traits within and between the analyzed populations. Marker-based heritability estimates for all investigated traits ranged from 0.18 to 0.60 using 778K genotypes, or from 0.17 to 0.57 using 50K genotypes (reduced from Illumina 778K HD to Illumina Bovine SNP50). An investigation to determine if QTL detected by 778K analysis could also be detected using 50K genotypes produced variable results, suggesting that 50K analyses were generally insufficient for QTL detection in these populations, and that relevant breeding or selection programs should be based on higher density analyses (imputed or directly ascertained).

Conclusions

Fourteen moderate to large-effect QTL regions which ranged from being physically proximal (lead SNPs ≤ 3Mb) to fully overlapping for RFI, DMI, ADG, and MMWT were detected within and between populations, and included evidence for pleiotropy, proximal but independent causal mutations, and multi-breed QTL. Bovine positional candidate genes for these traits were functionally conserved across vertebrate species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3754-y) contains supplementary material, which is available to authorized users.

Genomic variants at 20p11 associated with body fat mass in the European population

OBJECTIVE

Body fat mass (BFM) is more homogeneous and accurate than body total mass in measuring obesity but has rarely been studied. Aiming to uncover the genetic basis of fat-induced obesity, a genome-wide association meta-analysis of BFM, after adjustment by body lean mass, was performed in the European population.

METHODS

Three samples of European ancestry were included in the meta-analysis: the Framingham Heart Study (N = 6,004), the Kansas City osteoporosis study (N = 2,207), and the Omaha osteoporosis study (N = 968).

RESULTS

At the genome-wide significance level (α = 5.0×10-8 ), a cluster of 10 single-nucleotide polymorphisms (SNPs) at chromosomal region 20p11 that were associated with BFM (lead SNP rs2069126, P = 1.82×10-9 , closest gene SLC24A3) was identified in 9,179 subjects. One of the top SNPs, rs6046308 (P = 3.74×10-8 ), was found to be nominally significant for body fat percentage in another independent study (P = 0.03, N = 75,888) and was reported to transregulate the expression of the MPZ gene at 1q23.3 (unadjusted P = 9.78×10-6 , N = 1,490). Differential gene expression analysis demonstrated that SLC24A3 and CFAP61 at the identified locus were differentially expressed in tissues of people with versus without obesity (P = 3.40×10-5 and 8.72×10-4 , N = 126 and 70), implying their potential role in fat development.

CONCLUSIONS

These results may provide new insights into the biological mechanism that underlies fat-induced obesity pathology.

Single nucleotide polymorphism discovery in bovine liver using RNA-seq technology

BACKGROUND

RNA-seq is a useful next-generation sequencing (NGS) technology that has been widely used to understand mammalian transcriptome architecture and function. In this study, a breed-specific RNA-seq experiment was utilized to detect putative single nucleotide polymorphisms (SNPs) in liver tissue of young bulls of the Polish Red, Polish Holstein-Friesian (HF) and Hereford breeds, and to understand the genomic variation in the three cattle breeds that may reflect differences in production traits.

RESULTS

The RNA-seq experiment on bovine liver produced 107,114,4072 raw paired-end reads, with an average of approximately 60 million paired-end reads per library. Breed-wise, a total of 345.06, 290.04 and 436.03 million paired-end reads were obtained from the Polish Red, Polish HF, and Hereford breeds, respectively. Burrows-Wheeler Aligner (BWA) read alignments showed that 81.35%, 82.81% and 84.21% of the mapped sequencing reads were properly paired to the Polish Red, Polish HF, and Hereford breeds, respectively. This study identified 5,641,401 SNPs and insertion and deletion (indel) positions expressed in the bovine liver with an average of 313,411 SNPs and indel per young bull. Following the removal of the indel mutations, a total of 195,3804, 152,7120 and 205,3184 raw SNPs expressed in bovine liver were identified for the Polish Red, Polish HF, and Hereford breeds, respectively. Breed-wise, three highly reliable breed-specific SNP-databases (SNP-dbs) with 31,562, 24,945 and 28,194 SNP records were constructed for the Polish Red, Polish HF, and Hereford breeds, respectively. Using a combination of stringent parameters of a minimum depth of ≥10 mapping reads that support the polymorphic nucleotide base and 100% SNP ratio, 4,368, 3,780 and 3,800 SNP records were detected in the Polish Red, Polish HF, and Hereford breeds, respectively. The SNP detections using RNA-seq data were successfully validated by kompetitive allele-specific PCR (KASPTM) SNP genotyping assay. The comprehensive QTL/CG analysis of 110 QTL/CG with RNA-seq data identified 20 monomorphic SNP hit loci (CARTPT, GAD1, GDF5, GHRH, GHRL, GRB10, IGFBPL1, IGFL1, LEP, LHX4, MC4R, MSTN, NKAIN1, PLAG1, POU1F1, SDR16C5, SH2B2, TOX, UCP3 and WNT10B) in all three cattle breeds. However, six SNP loci (CCSER1, GHR, KCNIP4, MTSS1, EGFR and NSMCE2) were identified as highly polymorphic among the cattle breeds.

CONCLUSIONS

This study identified breed-specific SNPs with greater SNP ratio and excellent mapping coverage, as well as monomorphic and highly polymorphic putative SNP loci within QTL/CGs of bovine liver tissue. A breed-specific SNP-db constructed for bovine liver yielded nearly six million SNPs. In addition, a KASPTM SNP genotyping assay, as a reliable cost-effective method, successfully validated the breed-specific putative SNPs originating from the RNA-seq experiments.

Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare

In the past years, there has been a remarkable development of high-throughput omics (HTO) technologies such as genomics, epigenomics, transcriptomics, proteomics and metabolomics across all facets of biology. This has spearheaded the progress of the systems biology era, including applications on animal production and health traits. However, notwithstanding these new HTO technologies, there remains an emerging challenge in data analysis. On the one hand, different HTO technologies judged on their own merit are appropriate for the identification of disease-causing genes, biomarkers for prevention and drug targets for the treatment of diseases and for individualized genomic predictions of performance or disease risks. On the other hand, integration of multi-omic data and joint modelling and analyses are very powerful and accurate to understand the systems biology of healthy and sustainable production of animals. We present an overview of current and emerging HTO technologies each with a focus on their applications in animal and veterinary sciences before introducing an integrative systems genomics framework for analysing and integrating multi-omic data towards improved animal production, health and welfare. We conclude that there are big challenges in multi-omic data integration, modelling and systems-level analyses, particularly with the fast emerging HTO technologies. We highlight existing and emerging systems genomics approaches and discuss how they contribute to our understanding of the biology of complex traits or diseases and holistic improvement of production performance, disease resistance and welfare.

Genome-wide association study identifies loci and candidate genes for meat quality traits in Simmental beef cattle

Improving meat quality is the best way to enhance profitability and strengthen competitiveness in beef industry. Identification of genetic variants that control beef quality traits can help breeders design optimal breeding programs to achieve this goal. We carried out a genome-wide association study for meat quality traits in 1141 Simmental cattle using the Illumina Bovine HD 770K SNP array to identify the candidate genes and genomic regions associated with meat quality traits for beef cattle, including fat color, meat color, marbling score, longissimus muscle area, and shear force. In our study, we identified twenty significant single-nucleotide polymorphisms (SNPs) (p < 1.47 × 10(-6)) associated with these five meat quality traits. Notably, we observed several SNPs were in or near eleven genes which have been reported previously, including TMEM236, SORL1, TRDN, S100A10, AP2S1, KCTD16, LOC506594, DHX15, LAMA4, PREX1, and BRINP3. We identified a haplotype block on BTA13 containing five significant SNPs associated with fat color trait. We also found one of 19 SNPs was associated with multiple traits (shear force and longissimus muscle area) on BTA7. Our results offer valuable insights to further explore the potential mechanism of meat quality traits in Simmental beef cattle.

Omics approaches to probe markers of disease resistance in animal sciences

Disease pathways can be explained into a list of biomarkers at different scales to develop applications.