Model fit after pairwise maximum likelihood

Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log--likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two--way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations

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The factor structure of executive function in childhood and adolescence

Executive functioning (EF) plays a major role in many domains of human behaviour, including self-regulation, academic achievement, and even sports expertise. While a significant proportion of cross-sectional research has focused on the developmental pathways of EF, the existing literature is fractionated due to a wide range of methodologies applied to narrow age ranges, impeding comparison across a broad range of age groups. The current study used a cross-sectional design to investigate the factor structure of EF within late childhood and adolescence. A total of 2166 Flemish children and adolescents completed seven tasks of the Cambridge Brain Sciences test battery. Based on the existing literature, a Confirmatory Factor Analysis was performed, which indicated that a unitary factor model provides the best fit for the youngest age group (7–12 years). For the adolescents (12–18 years), the factor structure consists of four different components, including working memory, shifting, inhibition and planning. With regard to differences between early (12–15 years) and late (15–18 years) adolescents, working memory, inhibition and planning show higher scores for the late adolescents, while there was no difference on shifting. The current study is one of the first to administer the same seven EF tests in a considerably large sample of children and adolescents, and as such contributes to the understanding of the developmental trends in EF. Future studies, especially with longitudinal designs, are encouraged to further increase the knowledge concerning the factor structure of EF, and the development of the different EF components

Genome-wide association studies for feedlot and growth traits in cattle

A genome wide-association study for production traits in cattle was carried out using genotype data from the 10K Affymetrix (Santa Clara, CA) and the 50K Illumina (San Diego, CA) SNP chips. The results for residual feed intake (RFI), BW, and hip height in 3 beef breed types (Bos indicus, Bos taurus, and B. indicus × B. taurus), and for stature in dairy cattle, are presented. The aims were to discover SNP associated with all traits studied, but especially RFI, and further to test the consistency of SNP effects across different cattle populations and breed types. The data were analyzed within data sets and within breed types by using a mixed model and fitting 1 SNP at a time. In each case, the number of significant SNP was more than expected by chance alone. A total of 75 SNP from the reference population with 50K chip data were significant (P < 0.001) for RFI, with a false discovery rate of 68%. These 75 SNP were mapped on 24 different BTA. Of the 75 SNP, the 9 most significant SNP were detected on BTA 3, 5, 7, and 8, with P ≤ 6.0 × 10 . In a population of Angus cattle divergently selected for high and low RFI and 10K chip data, 111 SNP were significantly (P < 0.001) associated with RFI, with a false discovery rate of 7%. Approximately 103 of these SNP were therefore likely to represent true positives. Because of the small number of SNP common to both the 10K and 50K SNP chips, only 27 SNP were significantly (P < 0.05) associated with RFI in the 2 populations. However, other chromosome regions were found that contained SNP significantly associated with RFI in both data sets, although no SNP within the region showed a consistent effect on RFI. The SNP effects were consistent between data sets only when estimated within the same breed type

Haplotype Analysis Improved Evidence for Candidate Genes for Intramuscular Fat Percentage from a Genome Wide Association Study of Cattle

In genome wide association studies (GWAS), haplotype analyses of SNP data are neglected in favour of single point analysis of associations. In a recent GWAS, we found that none of the known candidate genes for intramuscular fat (IMF) had been identified. In this study, data from the GWAS for these candidate genes were re-analysed as haplotypes. First, we confirmed that the methodology would find evidence for association between haplotypes in candidate genes of the calpain-calpastatin complex and musculus longissimus lumborum peak force (LLPF), because these genes had been confirmed through single point analysis in the GWAS. Then, for intramuscular fat percent (IMF), we found significant partial haplotype substitution effects for the genes ADIPOQ and CXCR4, as well as suggestive associations to the genes CEBPA, FASN, and CAPN1. Haplotypes for these genes explained 80% more of the phenotypic variance compared to the best single SNP. For some genes the analyses suggested that there was more than one causative mutation in some genes, or confirmed that some causative mutations are limited to particular subgroups of a species. Fitting the SNPs and their interactions simultaneously explained a similar amount of the phenotypic variance compared to haplotype analyses. Haplotype analysis is a neglected part of the suite of tools used to analyse GWAS data, would be a useful method to extract more information from these data sets, and may contribute to reducing the missing heritability problem