Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis.

BACKGROUND: Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG. METHODS: By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk. RESULTS: We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk. CONCLUSIONS: This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

Colorable role of interleukin (IL)-6 in obesity hypertension: A hint from a Chinese adult case-control study.

BACKGROUND: Obesity and hypertension are major risk factors for cardiovascular diseases that affect millions of people worldwide. Both conditions are associated with chronic low-grade inflammation, which is mediated by cytokines such as interleukin-6 (IL-6). IL-6 is a multifunctional cytokine that can have pro-inflammatory or anti-inflammatory effects depending on the context. The exact role of IL-6 in obesity-associated hypertension is unclear. OBJECTIVE: To investigate how IL-6 affects blood pressure, inflammation, and metabolic function in obesity-hypertension using a Chinese adult case-control study. METHODS: A total of 153 participants were sorted into four subgroups according to their body mass index (BMI) and blood pressure (BP): normal healthy group (NH), just obesity group (JO), just-hypertension group (JH), and obesity-hypertension group (OH). Serum IL-6 concentrations were measured by Enzyme-linked Immunosorbent Assay (ELISA) and their correlations with anthropometric and laboratory parameters and their differences across the subgroups were examined. Multiple linear regression analysis was performed to identify the predictors of serum IL-6 concentrations in each group. RESULTS: Serum IL-6 concentrations were higher in NH group than in JO group and correlated positively with diastolic blood pressure in NH and JO groups, but not in JH and OH groups. Serum IL-6 concentrations also correlated with albumin in NH group, alkaline phosphatase in JO group, serum creatinine and fasting blood glucose in JH group. The influencing factors of serum IL-6 concentrations varied among the four groups, with gender, diastolic blood pressure and albumin being significant predictors in NH group, alkaline phosphatase in JO group, age and serum creatinine in JH group, and none in OH group. CONCLUSIONS: These results suggest that IL-6 may play diverse effects in the pathogenesis of obesity- hypertension, depending on the presence or absence of obesity and hypertension. Further studies are needed to elucidate the underlying mechanisms of IL-6 signaling and function in these diseases.

Both indirect maternal and direct fetal genetic effects reflect the observational relationship between higher birth weight and lower adult bone mass.

BACKGROUND: Birth weight is considered not only to undermine future growth, but also to induce lifelong diseases; the aim of this study is to explore the relationship between birth weight and adult bone mass. METHODS: We performed multivariable regression analyses to assess the association of birth weight with bone parameters measured by dual-energy X-ray absorptiometry (DXA) and by quantitative ultrasound (QUS), independently. We also implemented a systemic Mendelian randomization (MR) analysis to explore the causal association between them with both fetal-specific and maternal-specific instrumental variables. RESULTS: In the observational analyses, we found that higher birth weight could increase the adult bone area (lumbar spine, ß-coefficient= 0.17, P < 2.00 × 10-16; lateral spine, ß-coefficient = 0.02, P = 0.04), decrease bone mineral content-adjusted bone area (BMCadjArea) (lumbar spine, ß-coefficient= - 0.01, P = 2.27 × 10-14; lateral spine, ß-coefficient = - 0.05, P = 0.001), and decrease adult bone mineral density (BMD) (lumbar spine, ß-coefficient = - 0.04, P = 0.007; lateral spine; ß-coefficient = - 0.03, P = 0.02; heel, ß-coefficient = - 0.06, P < 2.00 × 10-16), and we observed that the effect of birth weight on bone size was larger than that on BMC. In MR analyses, the higher fetal-specific genetically determined birth weight was identified to be associated with higher bone area (lumbar spine; ß-coefficient = 0.15, P = 1.26 × 10-6, total hip, ß-coefficient = 0.15, P = 0.005; intertrochanteric area, ß-coefficient = 0.13, P = 0.0009; trochanter area, ß-coefficient = 0.11, P = 0.03) but lower BMD (lumbar spine, ß-coefficient = - 0.10, P = 0.01; lateral spine, ß-coefficient = - 0.12, P = 0.0003, and heel ß-coefficient = - 0.11, P = 3.33 × 10-13). In addition, we found that the higher maternal-specific genetically determined offspring birth weight was associated with lower offspring adult heel BMD (ß-coefficient = - 0.001, P = 0.04). CONCLUSIONS: The observational analyses suggested that higher birth weight was associated with the increased adult bone area but decreased BMD. By leveraging the genetic instrumental variables with maternal- and fetal-specific effects on birth weight, the observed relationship could be reflected by both the direct fetal and indirect maternal genetic effects.

General and abdominal obesity operate differently as influencing factors of fracture risk in old adults.

To infer the causality between obesity and fracture and the difference between general and abdominal obesity, a prospective study was performed in 456,921 participants, and 10,142 participants developed an incident fracture with follow-up period of 7.96 years. A U-shape relationship was observed between BMI and fracture, with the lowest risk of fracture in overweight participants. The obesity individuals had higher fracture risk when BMD was adjusted, and the protective effect of moderate-high BMI on fracture was mostly mediated by bone mineral density (BMD). However, for abdominal obesity, the higher WCadjBMI (linear) and HCadjBMI (J-shape) were found to be related to higher fracture risk, and less than 30% of the effect was mediated by BMD. By leveraging genetic instrumental variables, it provided additional evidences to support the aforementioned findings. In conclusion, keeping moderate-high BMI might be of benefit to old people in terms of fracture risk, whereas abdominal adiposity might increase risk of fracture.

Genomic analyses of 10,376 individuals in the Westlake BioBank for Chinese (WBBC) pilot project.

We initiate the Westlake BioBank for Chinese (WBBC) pilot project with 4,535 whole-genome sequencing (WGS) individuals and 5,841 high-density genotyping individuals, and identify 81.5 million SNPs and INDELs, of which 38.5% are absent in dbSNP Build 151. We provide a population-specific reference panel and an online imputation server ( https://wbbc.westlake.edu.cn/ ) which could yield substantial improvement of imputation performance in Chinese population, especially for low-frequency and rare variants. By analyzing the singleton density of the WGS data, we find selection signatures in SNX29, DNAH1 and WDR1 genes, and the derived alleles of the alcohol metabolism genes (ADH1A and ADH1B) emerge around 7,000 years ago and tend to be more common from 4,000 years ago in East Asia. Genetic evidence supports the corresponding geographical boundaries of the Qinling-Huaihe Line and Nanling Mountains, which separate the Han Chinese into subgroups, and we reveal that North Han was more homogeneous than South Han.

Observational and genetic evidence highlight the association of human sleep behaviors with the incidence of fracture.

We combined conventional evidence from longitudinal data in UK Biobank and genetic evidence from Mendelian randomization (MR) approach to infer the causality between sleep behaviors and fracture risk. We found that participants with insomnia showed 6.4% higher risk of fracture (hazard ratio [HR] = 1.064, 95% CI = 1.038-1.090, P = 7.84 × 10-7), falls and bone mineral density (BMD) mediated 24.6% and 10.6% of the intermediary effect; the MR analyses provided the consistent evidence. A U-shape relationship was observed between sleep duration and fracture risk (P < 0.001) with the lowest risk at sleeping 7-8 h per day. The excessive daytime sleepiness and "evening" chronotype were associated with fracture risk in observational study, but the association between chronotype and fracture did not show in MR analyses. We further generated a sleep risk score (SRS) with potential risk factors (i.e., insomnia, sleep duration, chronotype, and daytime sleepiness). We found that the risk of fracture increased with an increasing SRS (HR = 1.087, 95% CI = 1.065-1.111, P = 1.27 × 10-14). Moreover, 17.4% of the fracture cases would be removed if all participants exhibited a healthy sleep pattern. In conclusion, insomnia had a causal effect on fracture, falls had a larger intermediary effect than BMD in this association. Individuals with fracture risk could benefit from the intervention on unhealthy sleep pattern.

Integrative analysis of genomic and epigenomic data reveal underlying superenhancer-mediated microRNA regulatory network for human bone mineral density.

Bone mineral density (BMD) is a highly heritable complex trait and is a key indicator for diagnosis and treatment for osteoporosis. In the last decade, numerous susceptibility loci for BMD and fracture have been identified by genome-wide association studies (GWAS); however, fine mapping of these loci is challengeable. Here, we proposed a new long-range fine-mapping approach that combined superenhancers (SEs) and microRNAs (miRNAs) data, which were two important factors in control of cell identity and specific differentiation, with the GWAS summary datasets in cell-type-restricted way. Genome-wide SE-based analysis found that the BMD-related variants were significantly enriched in the osteoblast SE regions, indicative of potential long-range effects of such SNPs. With the SNP-mapped SEs (mSEs), 13 accessible long-range mSE-interacted miRNAs (mSE-miRNAs) were identified by integrating osteoblast Hi-C and ATAC-seq data, including three known bone-related miRNAs (miR-132-3p, miR-212-3p and miR-125b-5p). The putative targets of the two newly identified mSE-miRNAs (miR-548aj-3p and miR-190a-3p) were found largely enriched in osteogenic-related pathway and processes, suggesting that these mSE-miRNAs could be functional in the regulation of osteoblast differentiation. Furthermore, we identified 54 genes with the long-range 'mSE-miRNA' approach, and 24 of them were previously reported to be related to skeletal development. Besides, enrichment analysis found that these genes were specifically enriched in the post-transcriptional regulation and bone formation processes. This study provided a new insight into the approach of fine-mapping of GWAS loci. A tool was provided for the genome-wide SE-based analysis and the detection of long-range osteoblast-restricted mSE-miRNAs (https://github.com/Zheng-Lab-Westlake/Osteo-Fine-Mapp-SNP2SE2miRNA).

Cohort profile: the Westlake BioBank for Chinese (WBBC) pilot project.

PURPOSE: The Westlake BioBank for Chinese (WBBC) pilot cohort is a population-based prospective study with its major purpose to better understand the effect of genetic and environmental factors on growth and development from adolescents to adults. PARTICIPANTS: A total of 14 726 participants (4751 males and 9975 females) aged 14-25 years were recruited and the baseline survey was carried out from 2017 to 2019. The pilot cohort contains rich range of information regarding of demographics and anthropometric measurements, lifestyle and sleep patterns, clinical and health outcomes. Visit the WBBC website for more information (https://wbbc.westlake.edu.cn/index.html). FINDINGS TO DATE: The mean age of the study samples were 18.6 years for males and 18.5 years for females, respectively. The mean height and weight were 172.9 cm and 65.81 kg for males, and 160.1 cm and 52.85 kg for females. Results indicated that the prevalence of underweight in female was much higher than male, but the prevalence of overweight and obesity in female was lower than male. The mean serum 25(OH)D level in the 14 726 young participants was 22.4±5.3 ng/mL, and male had a higher level of serum 25(OH)D than female, overall, 33.5% of the participants had vitamin D deficiency and even more participants suffered from vitamin D insufficiency (58.2%). The proportion of deficiency in females was much higher than that in males (41.8 vs 16.4%). The issue of underweight and vitamin D deficiency in young people should be paid attention, especially in females. These results reflected the fact that thinness and paler skin are preferred in modern aesthetics of Chinese culture. FUTURE PLANS: WBBC pilot is designed as a prospective cohort study and provides a unique and rich data set analysing health trajectories from adolescents to young adults. WBBC will continue to collect samples with old age.

Systemic evaluation of the relationship between psoriasis, psoriatic arthritis and osteoporosis: observational and Mendelian randomisation study.

OBJECTIVES AND METHODS: With 432 513 samples from UK Biobank dataset, multivariable linear/logistic regression were used to estimate the relationship between psoriasis/psoriatic arthritis (PsA) and estimated bone mineral density (eBMD)/osteoporosis, controlling for potential confounders. Here, confounders were set in three ways: model0 (including age, height, weight, smoking and drinking), model1 (model0 +regular physical activity) and model2 (model1 +medication treatments). The eBMD was derived from heel ultrasound measurement. And 4904 patients with psoriasis and 847 patients with PsA were included in final analysis. Mendelian randomisation (MR) approach was used to evaluate the causal effect between them. RESULTS: Lower eBMD were observed in patients with PsA than in controls in both model0 (ß-coefficient=-0.014, p=0.0006) and model1 (ß-coefficient=-0.013, p=0.002); however, the association disappeared when conditioning on treatment with methotrexate or ciclosporin (model2) (ß-coefficient=-0.005, p=0.28), mediation analysis showed that 63% of the intermediary effect on eBMD was mediated by medication treatment (p<2E-16). Patients with psoriasis without arthritis showed no difference of eBMD compared with controls. Similarly, the significance of higher risk of osteopenia in patients with PsA (OR=1.27, p=0.002 in model0) could be eliminated by conditioning on medication treatment (p=0.244 in model2). Psoriasis without arthritis was not related to osteopenia and osteoporosis. The weighted Genetic Risk Score analysis found that genetically determined psoriasis/PsA were not associated with eBMD (p=0.24 and p=0.88). Finally, MR analysis showed that psoriasis/PsA had no causal effect on eBMD, osteoporosis and fracture. CONCLUSIONS: The effect of PsA on osteoporosis was secondary (eg, medication) but not causal. Under this hypothesis, psoriasis without arthritis was not a risk factor for osteoporosis.

Multiple association analysis of loci and candidate genes that regulate body size at three growth stages in Simmental beef cattle.

BACKGROUND: Body size traits as one of the main breeding selection criteria was widely used to monitor cattle growth and to evaluate the selection response. In this study, body size was defined as body height (BH), body length (BL), hip height (HH), heart size (HS), abdominal size (AS), and cannon bone size (CS). We performed genome-wide association studies (GWAS) of these traits over the course of three growth stages (6, 12 and 18 months after birth) using three statistical models, single-trait GWAS, multi-trait GWAS and LONG-GWAS. The Illumina Bovine HD 770 K BeadChip was used to identify genomic single nucleotide polymorphisms (SNPs) in 1217 individuals. RESULTS: In total, 19, 29, and 10 significant SNPs were identified by the three models, respectively. Among these, 21 genes were promising candidate genes, including SOX2, SNRPD1, RASGEF1B, EFNA5, PTBP1, SNX9, SV2C, PKDCC, SYNDIG1, AKR1E2, and PRIM2 identified by single-trait analysis; SLC37A1, LAP3, PCDH7, MANEA, and LHCGR identified by multi-trait analysis; and P2RY1, MPZL1, LINGO2, CMIP, and WSCD1 identified by LONG-GWAS. CONCLUSIONS: Multiple association analysis was performed for six growth traits at each growth stage. These findings offer valuable insights for the further investigation of potential genetic mechanism of growth traits in Simmental beef cattle.

Genome-wide association studies using binned genotypes.

Linear mixed models (LMM) that tests trait association one marker at a time have been the most popular methods for genome-wide association studies. However, this approach has potential pitfalls: over conservativeness after Bonferroni correction, ignorance of linkage disequilibrium (LD) between neighboring markers, and power reduction due to overfitting SNP effects. So, multiple locus models that can simultaneously estimate and test all markers in the genome are more appropriate. Based on the multiple locus models, we proposed a bin model that combines markers into bins based on their LD relationships. A bin is treated as a new synthetic marker and we detect the associations between bins and traits. Since the number of bins can be substantially smaller than the number of markers, a penalized multiple regression method can be adopted by fitting all bins to a single model. We developed an innovative method to bin the neighboring markers and used the least absolute shrinkage and selection operator (LASSO) method. We compared BIN-Lasso with SNP-Lasso and Q + K-LMM in a simulation experiment, and showed that the new method is more powerful with less Type I error than the other two methods. We also applied the bin model to a Chinese Simmental beef cattle population for bone weight association study. The new method identified more significant associations than the classical LMM. The bin model is a new dimension reduction technique that takes advantage of biological information (i.e., LD). The new method will be a significant breakthrough in associative genomics in the big data era.

Evaluation of GBLUP, BayesB and elastic net for genomic prediction in Chinese Simmental beef cattle.

Chinese Simmental beef cattle are the most economically important cattle breed in China. Estimated breeding values for growth, carcass, and meat quality traits are commonly used as selection criteria in animal breeding. The objective of this study was to evaluate the accuracy of alternative statistical methods for the estimation of genomic breeding values. Analyses of the accuracy of genomic best linear unbiased prediction (GBLUP), BayesB, and elastic net (EN) were performed with an Illumina BovineHD BeadChip on 1,217 animals by applying 5-fold cross-validation. Overall, the accuracies ranged from 0.17 to 0.296 for ten traits, and the heritability estimates ranged from 0.36 to 0.63. The EN (alpha = 0.001) model provided the most accurate prediction, which was also slightly higher (0.2-2%) than that of GBLUP for most traits, such as average daily weight gain (ADG) and carcass weight (CW). BayesB was less accurate for each trait than were EN (alpha = 0.001) and GBLUP. These findings indicate the importance of using an appropriate variable selection method for the genomic selection of traits and suggest the influence of the genetic architecture of the traits we analyzed.

Comprehensive assessment of the association between genes on JAK-STAT pathway (IFIH1, TYK2, IL-10) and systemic lupus erythematosus: a meta-analysis.

Previous studies have reported that genes relating to JAK-STAT pathway (IFIH1, TYK2 and IL-10) conferred the susceptibility to SLE. In this study, we performed a meta-analysis (including 43 studies) to evaluate the association between IFIH1 (9288 patients and 24,040 controls), TYK2 (4928 patients and 11,536 controls), IL-10 (3623 patients and 4907 controls) polymorphisms and systemic lupus erythematosus (SLE) in a comprehensive way. We found that IFIH1 rs1990760_T allele was associated with risk of SLE in overall population under three models (allelic: P = 2.56 × 10-11, OR 1.135, 95% CI 1.094-1.179, dominant: P = 1.8 × 10-8, OR 1.203, 95% CI 1.128-1.284, recessive: P = 2.6 × 10-7, OR 1.163, 95% CI 1.098-1.231). A strong association had been observed between TYK2 polymorphism rs2304256_C allele and SLE in Europeans (P = 5.82 × 10-5, OR 1.434, 95% CI 1.203-1.710). When coming to overall population, TYK2 rs2304256_C showed a significant association with SLE under recessive model (P = 8.05 × 10-3, OR 1.314, 95% CI 1.074-1.608). However, the other two SNPs (rs12720270, rs280519) of TYK2 were not significant. The results also indicated an association between IL-10 rs1800896_G allele and SLE in Asians under recessive model (P = 4.65 × 10-3, OR 2.623, 95% CI 1.346-5.115), while, IL-10 rs1800896_G had a trend of association with SLE in European population in dominant model (P = 1.21 × 10-2, OR 1.375, 95% CI 1.072-1.764). In addition, we found IL-10 rs1800896 GG homozygote might be associated with increased susceptibility to SLE (GG vs AA, P = 4.65 × 10-3, OR 1.539, 95% CI 1.142-2.072). We concluded that IFIH1 rs1990760_T and TYK2 rs2304256_C alleles were significantly associated with SLE, and IL-10 rs1800896 GG homozygote might have an enhancement effect on SLE risk.

Genome-wide association study identifies loci and candidate genes for internal organ weights in Simmental beef cattle.

Cattle internal organs as accessible raw materials have a long history of being widely used in beef processing, feed and pharmaceutical industry. These traits not only are of economic interest to breeders, but they are intrinsically linked to many valuable traits, such as growth, health, and productivity. Using the Illumina Bovine HD 770K SNP array, we performed a genome-wide association study for heart weight, liver weight, spleen weight, lung weight, and kidney weight in 1,217 Simmental cattle. In our research, 38 significant single nucleotide polymorphisms (SNPs) ( P < 1.49 × 10-6) were identified for five internal organ weight traits. These SNPs are within or near 13 genes, and some of them have been reported previously, including NDUFAF4, LCORL, BT.94996, SLIT2, FAM184B, LAP3, BBS12, MECOM, CD300LF, HSD17B3, TLR4, MXI1, and MB21D2. In addition, we detected four haplotype blocks on BTA6 containing 18 significant SNPs associated with spleen weight. Our results offer worthy insights into understanding the genetic mechanisms of internal organs' development, with potential application in breeding programs of Simmental beef cattle.

Searching for new loci and candidate genes for economically important traits through gene-based association analysis of Simmental cattle.

Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental cattle. Additionally, 21836 cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

Genome-Wide Association Study Reveals the PLAG1 Gene for Knuckle, Biceps and Shank Weight in Simmental Beef Cattle.

Carcass traits of beef cattle have been genetically improved to increase yield of high quality meat. Genome-wide association study (GWAS) is a powerful method to identify genetic variants associated with carcass traits. For the 770K genotyped SNPs from 1141 Chinese Simmental cattle, we used the compressed mixed linear model (CMLM) to perform a genome-wide association study for knuckle, biceps and shank of beef carcass traits. Seventeen significantly associated SNPs were found, which are located on BTA6, BTA14 and BTA15. Interestingly, one pleiotropic quantitative trait nucleotide (QTN), named BovineHD1400007259 (p < 10-8) within the well-known gene region PLAG1-CHCHD7 on BTA14, was found to govern variation of the knuckle, biceps and shank traits. The QTN accounted for 8.6% of phenotypic variance for biceps. In addition, 16 more SNPs distributed on BTA14 were detected as being associated with the carcass traits.

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.

Pathway-Based Genome-Wide Association Studies for Two Meat Production Traits in Simmental Cattle.

Most single nucleotide polymorphisms (SNPs) detected by genome-wide association studies (GWAS), explain only a small fraction of phenotypic variation. Pathway-based GWAS were proposed to improve the proportion of genes for some human complex traits that could be explained by enriching a mass of SNPs within genetic groups. However, few attempts have been made to describe the quantitative traits in domestic animals. In this study, we used a dataset with approximately 7,700,000 SNPs from 807 Simmental cattle and analyzed live weight and longissimus muscle area using a modified pathway-based GWAS method to orthogonalise the highly linked SNPs within each gene using principal component analysis (PCA). As a result, of the 262 biological pathways of cattle collected from the KEGG database, the gamma aminobutyric acid (GABA)ergic synapse pathway and the non-alcoholic fatty liver disease (NAFLD) pathway were significantly associated with the two traits analyzed. The GABAergic synapse pathway was biologically applicable to the traits analyzed because of its roles in feed intake and weight gain. The proposed method had high statistical power and a low false discovery rate, compared to those of the smallest P-value and SNP set enrichment analysis methods.