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Objective: The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and genome-wide association study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking. Methods: This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits. Results: A total of 16 significant single nucleotide polymorphism (SNP) markers were identified in Group A, comprising 7 for carcass weight (CWT), 3 for back fat thickness (BFT), and 6 for marbling score (MS). In Group B, 7 significant SNP markers were identified, including 3 for CWT, 1 for eye muscle area, 1 for BFT, and 2 for MS. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while protein-protein interaction analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A. Conclusion: GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes. [ABSTRACT FROM AUTHOR]

Objective: The improvement of carcass traits is essential for the Hanwoo industry because of the Hanwoo grade determination system, and genome-wide association study (GWAS) analysis is an instrumental tool for identifying the genetic factors that impact these traits. While GWAS analysis utilizing family data offers advantages in minimizing genetic bias, research on family-based GWAS in Hanwoo is currently lacking., Methods: This study classified Group A using both parental and offspring genetic information, and Group B based solely on offspring genetic information, to compare GWAS analysis results of Hanwoo carcass traits., Results: A total of 16 significant single nucleotide polymorphism (SNP) markers were identified in Group A, comprising 7 for carcass weight (CWT), 3 for back fat thickness (BFT), and 6 for marbling score (MS). In Group B, 7 significant SNP markers were identified, including 3 for CWT, 1 for eye muscle area, 1 for BFT, and 2 for MS. Functional annotation analysis revealed only one common function related to carcass traits between the groups, while protein-protein interaction analysis indicated more gene interactions in Group A. The reliability of estimated values for common SNP markers identified between the groups was higher in Group A., Conclusion: GWAS analysis utilizing parental genetic information holds greater potential for application, owing to its higher reliability of estimated values and the ability to explore numerous candidate genes.

The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P < 5E-08) as the four P values resulted from the tested methods for any SNP were all below or all above 5E-08. Nevertheless, larger discrepancies were detected between logistic regression and LMMs-based models at suggestive level of significance (i.e., of 5E-08 ≤ P < 5E-06). The SNP effects estimated by the logistic regression models were not statistically different from those estimated by GLMMs that implemented Wald's test. However, several SNP effects were significantly different from their counterparts in LMMs analyses. We suggest that fitting GLMMs with Wald's test on a pre-selected subset of SNPs obtained from logistic regression models can ensure the balance between the speed of analyses and the accuracy of parameters. [ABSTRACT FROM AUTHOR]

Abstract A number of genome-wide association studies (GWASs) have identified several genetic determinants of plasma lipids in European populations, in which analytical approaches have often been based on the linear regression models and the association test between a SNP and each lipid component individually in cross-sectional designs. Since lipid variations are correlated, the consideration of pleiotropy is necessary and using methods that can perform simultaneous association test of multiple longitudinal traits provides more information about the recognition of the pleiotropic variants. To identify new pleiotropic variants and to determine whether loci identified in previous GWASs can also exert the same effect on lipid concentrations in Iranian population, longitudinal measurements of lipid variations were used in a sample of Iranian population (16,353 individuals within 3100 families) that followed up every 3 years and using a two-step model, the associations of 20,036 available SNPs on chromosome 16 were assessed. Twenty variants within the AC009035.1, SLC12A3, CETP, NLRC5, ESRP2 and, C16orf95 genes showed strong evidence for association with HDL-C, cholesterol, and triglycerides with p -values ranging from 1.7 × 10−102 to 6.6 × 10−5. Since many genetic variants associated with lipids still remain to be determined, the results of the present study may provide valuable information on identifying the associations of new genetic loci with lipid variations in other populations. Highlights • We used a two-step method to identify pleiotropic genetic variants associated with blood lipid levels. • GLMM and GQLSM were used for simultaneous genetic association test of multiple longitudinal traits with a familial design. • We explore the associations of 20 new pleiotropic SNPs with HDL-C, TC, and TG levels in a sample of Iranian families. • Using individual GQLSM for each lipid component, some new genetic variants related to HDL-C, TC, and TG levels were found. [ABSTRACT FROM AUTHOR]

Twenty percent of people aged 20 to 79 have type 2 diabetes (T2D) in the United Arab Emirates (UAE). Genome-wide association studies (GWAS) to identify genes for T2D have not been reported for Arab countries. We performed a discovery GWAS in an extended UAE family (N = 178; 66 diabetic; 112 healthy) genotyped on the Illumina Human 660 Quad Beadchip, with independent replication of top hits in 116 cases and 199 controls. Power to achieve genome-wide significance (commonly P = 5 × 10-8) was therefore limited. Nevertheless, transmission disequilibrium testing in FBAT identified top hits at Chromosome 4p12-p13 (KCTD8: rs4407541, P = 9.70 × 10-6; GABRB1: rs10517178/rs1372491, P = 4.19 × 10-6) and 14q13 (PRKD1: rs10144903, 3.92 × 10-6), supported by analysis using a linear mixed model approximation in GenABEL (4p12-p13 GABRG1/GABRA2: rs7662743, Padj-agesex = 2.06 × 10-5; KCTD8: rs4407541, Padj-agesex = 1.42 × 10-4; GABRB1: rs10517178/rs1372491, Padj-agesex = 0.027; 14q13 PRKD1: rs10144903, Padj-agesex = 6.95 × 10-5). SNPs across GABRG1/GABRA2 did not replicate, whereas more proximal SNPs rs7679715 (Padj-agesex = 0.030) and rs2055942 (Padj-agesex = 0.022) at COX7B2/GABRA4 did, in addition to a trend distally at KCTD8 (rs4695718: Padj-agesex = 0.096). Modelling of discovery and replication data support independent signals at GABRA4 (rs2055942: Padj-agesex-combined = 3 × 10-4) and at KCTD8 (rs4695718: Padj-agesex-combined = 2 × 10-4). Replication was observed for PRKD1 rs1953722 (proxy for rs10144903; Padj-agesex = 0.031; Padj-agesex-combined = 2 × 10-4). These genes may provide important functional leads in understanding disease pathogenesis in this population. [ABSTRACT FROM AUTHOR]

Genetic association studies of child health outcomes often employ family-based study designs. One of the most popular family-based designs is the case-parent trio design that considers the smallest possible nuclear family consisting of two parents and their affected child. This trio design is particularly advantageous for studying relatively rare disorders because it is less prone to type 1 error inflation due to population stratification compared to population-based study designs (e.g., case-control studies). However, obtaining genetic data from both parents is difficult, from a practical perspective, and many large studies predominantly measure genetic variants in mother-child dyads. While some statistical methods for analyzing parent-child dyad data (most commonly involving mother-child pairs) exist, it is not clear if they provide the same advantage as trio methods in protecting against population stratification, or if a specific dyad design (e.g., case-mother dyads vs. case-mother/control-mother dyads) is more advantageous. In this article, we review existing statistical methods for analyzing genome-wide marker data on dyads and perform extensive simulation experiments to benchmark their type I errors and statistical power under different scenarios. We extend our evaluation to existing methods for analyzing a combination of case-parent trios and dyads together. We apply these methods on genotyped and imputed data from multiethnic mother-child pairs only, case-parent trios only or combinations of both dyads and trios from the Gene, Environment Association Studies consortium (GENEVA), where each family was ascertained through a child affected by nonsyndromic cleft lip with or without cleft palate. Results from the GENEVA study corroborate the findings from our simulation experiments. Finally, we provide recommendations for using statistical genetic association methods for dyads., (© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.)