Mapping of quantitative trait loci (QTLs) underlying variation in quantitative traits continues to be a powerful tool in genetic study of plants and other organisms. Whole genome average interval mapping (WGAIM), a mixed model QTL mapping approach using all intervals or markers simultaneously, has been demonstrated to outperform composite interval mapping, a common approach for QTL analysis. However, the advent of high-throughput high-dimensional marker platforms provides a challenge. To overcome this, a dimension reduction technique is proposed for WGAIM for efficient analysis of a large number of markers. This approach results in reduced computing time as it is dependent on the number of genetic lines (or individuals) rather than the number of intervals (or markers). The approach allows for the full set of potential QTL effects to be recovered. A proposed random effects version of WGAIM aims to reduce bias in the estimated size of QTL effects. Lastly, the two-stage outlier procedure used in WGAIM is replaced by a single stage approach to reduce possible bias in the selection of putative QTL in both WGAIM and the random effects version. Simulation is used to demonstrate the efficiency of the dimension reduction approach as well as demonstrate that while the approaches are very similar, the random WGAIM performs better than the original and modified fixed WGAIM by reducing bias and in terms of mean square error of prediction of estimated QTL effects. Finally, an analysis of a doubled haploid population is used to illustrate the three approaches.

The possibility of breeding for uniform individuals by selecting animals expressing a small response to environment has been studied extensively in animal breeding. Bayesian methods for fitting models with genetic components in the residual variance have been developed for this purpose, but have limitations due to the computational demands. We use the hierarchical (h)-likelihood from the theory of double hierarchical generalized linear models (DHGLM) to derive an estimation algorithm that is computationally feasible for large datasets. Random effects for both the mean and residual variance parts of the model are estimated together with their variance/covariance components. An important feature of the algorithm is that it can fit a correlation between the random effects for mean and variance. An h-likelihood estimator is implemented in the R software and an iterative reweighted least square (IRWLS) approximation of the h-likelihood is implemented using ASReml. The difference in variance component estimates between the two implementations is investigated, as well as the potential bias of the methods, using simulations. IRWLS gives the same results as h-likelihood in simple cases with no severe indication of bias. For more complex cases, only IRWLS could be used, and bias did appear. The IRWLS is applied on the pig litter size data previously analysed by Sorensen & Waagepetersen (2003) using Bayesian methodology. The estimates we obtained by using IRWLS are similar to theirs, with the estimated correlation between the random genetic effects being −0·52 for IRWLS and −0·62 in Sorensen & Waagepetersen (2003).

Current methods for detecting Hardy–Weinberg disequilibrium (HWD) only deal with one locus at a time. We developed a method that can jointly detect HWD for multiple loci. The method was developed under the generalized linear model (GLM) using the probit link function. When applied to a single locus, the new method is more powerful than the exact test. When applied to two or more loci, the method can reduce false positives caused by linkage disequilibrium (LD). We applied the method to 24 single nucleotide polymorphism (SNP) markers of a single human gene and eliminated several false positive HWDs due to LD. We developed an R package ‘hwdglm’ for joint HWD detection, which can be downloaded from our personal website (www.statgen.ucr.edu).

Growing evidence suggests that metabolic syndrome (MetS) has both genetic and environmental bases. We estimated the heritability of the MetS and its components in the families from the Tehran Lipid and Glucose Study (TLGS). We investigated 904 nuclear families in TLGS with two biological parents and at least one offspring (1565 parents and 2448 children), aged 3–90 years, for whom MetS information was available and had at least two members of family with MetS. Variance component methods were used to estimate age and sex adjusted heritability of metabolic syndrome score (MSS) and MetS components using SOLAR software. The heritability of waist circumference (WC), HDL-cholesterol (HDL-C), triglycerides (TGs), fasting blood sugar (FBS), systolic blood pressure (SBP) and diastolic blood pressure (DBP) as continuous traits after adjusting for age and gender were 27, 46, 36, 29, 25, 26 and 15%, respectively, and MSS had a heritability of 15%. When MetS components were analysed as discrete traits, the estimates of age and gender adjusted heritability for MetS, abdominal obesity, low HDL-C, high TG, high FBS and high blood pressure (BP) were 22, 40, 34, 38 and 23%, respectively (P < 0·05). Three factors were extracted from the six continuous traits of the MetS including factor I (BP), factor II (lipids) and factor III (obesity and FBS). Heritability estimation for these three factors were 7, 13 (P < 0·05) and 2%, respectively. The highest heritability was for HDL-C and TG. The results strongly encourage efforts to identify the underlying susceptibility genes.

The X-linked telomeric P elements (TPs) TP5 and TP6 regulate the activity of the entire P element family because they are inserted in a major locus for the production of Piwi-interacting RNAs (piRNAs). The potential for this cytotype regulation is significantly strengthened when either TP5 or TP6 is combined with a non-telomeric X-linked or autosomal transgene that contains a P element. By themselves, none of the transgenic P elements have any regulatory ability. Synergism between the telomeric and transgenic P elements is much greater when the TP is derived from a female. Once an enhanced regulatory state is established in a female, it is transmitted to her offspring independently of either the telomeric or transgenic P elements – that is, it works through a strictly maternal effect. Synergistic regulation collapses when either the telomeric or the transgenic P element is removed from the maternal genotype, and it is significantly impaired when the TPs come from stocks heterozygous for mutations in the genes aubergine, piwi or Su(var)205. The synergism between telomeric and transgenic P elements is consistent with a model in which P piRNAs are amplified by alternating, or ping-pong, targeting of primary piRNAs to sense and antisense P transcripts, with the sense transcripts being derived from the transgenic P element and the antisense transcripts being derived from the TP.