Allelic expression of the rice yield-related gene, leucine-rich receptor-like kinase 6 (LRK6), in the hybrid of 93-11 (Oryza sativa L. subsp. Indica var. 93-11) and Nipponbare (O. sativa L. subsp. Japonica var. Nipponbare) is determined by allelic promoter cis-elements. Using deletion analysis of the LRK6 promoter, we identified two distinct regions that might contribute to LRK6 expression. Sequence alignment revealed differences in these LRK6 promoter regions in 93-11 and Nipponbare. One of the segments, named differential sequence of LRK6 promoter 2 (DSLP2), contains potential transcription factor binding sites. Using a yeast one-hybrid assay, we isolated an ethylene-responsive factor (ERF) protein that binds to DSLP2. Sequence analysis and a GCC-box assay showed that the ERF gene, O. sativa ERF 3 (OsERF3), which belongs to ERF subfamily class II, has a conserved ERF domain and an ERF-associated amphiphilic repression repressor motif. We used an in vivo mutation assay to identify a new motif (5′-TAA(A)GT-3′) located in DSLP2, which interacts with OsERF3. These results suggest that OsERF3, an AP2 (APETALA 2 Gene)/ERF transcription factor, binds the LRK6 promoter at this new motif, which might cause differential expression of LRK6 in the 93-11/Nipponbare hybrid.

Dealing with genotype uncertainty is an ongoing issue in genetic analyses of complex traits. Here we consider genotype uncertainty in quantitative trait loci (QTL) analyses for large crosses in variance component models, where the genetic information is included in identity-by-descent (IBD) matrices. An IBD matrix is one realization from a distribution of potential IBD matrices given available marker information. In QTL analyses, its expectation is normally used resulting in potentially reduced accuracy and loss of power. Previously, IBD distributions have been included in models for small human full-sib families. We develop an Expectation–Maximization (EM) algorithm for estimating a full model based on Monte Carlo imputation for applications in large animal pedigrees. Our simulations show that the bias of variance component estimates using traditional expected IBD matrix can be adjusted by accounting for the distribution and that the calculations are computationally feasible for large pedigrees.

The fast multipoint maximum likelihood mapping algorithm for crosses between inbred lines, introduced by Jansen et al. (2001),is extended for mapping in a full-sib family of an outbreeding species. The method accommodates different segregation types of markers and differences in recombination between parents. The two separate parental multipoint maximum likelihood maps are joined into an integrated map by averaging lengths over anchored segments and by interpolating or extrapolating for markers segregating in one parent only. The method is illustrated with simulated data. The method will enable a more accurate estimation of maps in outbreeding species than current methods.

The fluctuation experiment is the preferred method for estimating microbial mutation rates. A difficult task facing the data analyst is to infer the mean number of mutations from the number of mutant cells that only indirectly reflects the number of mutations. Partial plating, commonly practised in the laboratory, renders this task even more challenging by allowing only a portion of the mutant cells to be counted. In this paper, we propose a Bayesian approach to correcting for partial plating in the analysis of fluctuation experiments.

Prediction of genetic merit or disease risk using genetic marker information is becoming a common practice for selection of livestock and plant species. For the successful application of genome-wide marker-assisted selection (GWMAS), genomic predictions should be accurate and unbiased. The effect of selection on bias and accuracy of genomic predictions was studied in two simulated animal populations under weak or strong selection and with several heritabilities. Prediction of genetic values was by best-linear unbiased prediction (BLUP) using data either from relatives summarized in pseudodata for genotyped individuals (multiple-step method) or using all available data jointly (single-step method). The single-step method combined genomic- and pedigree-based relationship matrices. Predictions by the multiple-step method were biased. Predictions by a single-step method were less biased and more accurate but under strong selection were less accurate. When genomic relationships were shifted by a constant, the single-step method was unbiased and the most accurate. The value of that constant, which adjusts for non-random selection of genotyped individuals, can be derived analytically.

Understanding the relative influence of genetic drift and selection is fundamental in evolutionary biology. The theory of neutrality predicts that the genetic differentiation of a quantitative trait (QST) equals the genetic differentiation at neutral molecular markers (FST) if the quantitative trait has not been under selection. Thus, the relative magnitude of observed QST and expected QST under neutral expectations suggests the importance of selection and genetic drift for any observed phenotypic divergence. Because QST is based on additive genetic variance, estimating QST based on phenotypic measurements is problematic due to unknown environmental effects. To account for this, we used a model where the environmental component was allowed to vary when estimating QST. The model was used on data from 14 house sparrow (Passer domesticus) populations in Norway. In accordance with the significant phenotypic inter-population differences our analyses suggested that directional selection may have favoured different optimal phenotypes for some morphological traits across populations. In particular, different body mass and male ornamental phenotypes seemed to have been favoured. The conclusions are, however, dependent on assumptions regarding the proportion of the observed inter-population variation that is due to additive genetic differences, showing the importance of collecting such information in natural populations. By estimating QST, allowing the additive genetic proportion of phenotypic inter-population variation to vary, and by making use of recent statistical methods to compare observed QST with neutral expectations, we can use data that are relatively easy to collect to identify adaptive variation in natural populations.