Gene frequency changes following selection may reveal the existence of gene effects on the trait selected. Loci for the selected quantitative trait (SQTL) may thus be detected. Additionally, one can estimate the average effect (α) of a marker allele associated with an SQTL from the allele frequency change (Δq) due to selection of given intensity (i). In a sample of unrelated individuals, it is optimal to select the upper and lower 27% for generating Δq in order to estimate α. For a given number of individuals genotyped, this estimator is 0·25i2 times more efficient than the classical estimator of α, based on the regression of the trait on the genotype at the marker locus. The method is extended to selection criteria using information from relatives, showing that combined selection considerably increases the efficiency of estimation for traits of low heritability. The method has been applied to the detection of SQTL in a selection experiment in which the trait selected was pig litter size averaged over the first four parities, with i=3. Results for four genes are provided, one of which yielded a highly significant effect. The conditions required for valid application of the method are discussed, including selection experiments over several generations. Additional advantages of the method can be anticipated from determining gene frequencies on pooled samples of blood or DNA.

We used Drosophila melanogaster to test for compensatory control of cell area and cell number in the regulation of total wing area. In two random bred wild-type base stocks collected from different geographic locations we found a negative association between the area and the number of cells in the wing blade. Three replicate lines were selected for increased or decreased wing area, with cell area maintained at the same level as in the three controls. After eight generations of selection, despite a large and highly significant difference in wing area between the large, control and small selection lines, cell area did not differ significantly between them. Rather, the difference in wing area between selection regimes was attributable to differences in cell number. Over the course of selection, the initially significant negative correlation between cell area and cell number in the wing increased, providing evidence for compensatory regulation of cell area and cell number. As a result of the increasingly negative association between the two traits, the variance in wing area declined as selection proceeded. It will be important to discover the mechanisms underlying the compensatory regulation of cell area and cell number.

A method for finding optimum breeding schemes which maximize genetic gain under index selection with constraints on the rate of inbreeding is derived. The selection index includes information on the candidate and its sibs. Optimization is for the numbers of males and females to be selected and for the index weights when fixed numbers of offspring per generation, heritabilities and time horizons are considered. The expected rate of gain after a number of generations of selection is combined with the expected asymptotic rate of inbreeding (ΔF) in a single objective function which is maximized for finding the optimum solutions. Under restricted inbreeding, optimum designs are very similar for maximizing gains at different time horizons. The optimum number of selected males (for giving maximum gains) increases with the size of the scheme and with the severity in restricting ΔF and decreases with the heritability. Low heritability, less severe restrictions on ΔF and large schemes lead to increases in the relative weights given to performance of relatives in the index. The presence of common environmental effects leads to increases in optimum mating ratio when the heritability is low, to increases in the number of selected males and to more intense selection within families. Gains from index selection are compared with gains from mass selection. Under restricted inbreeding the advantage of optimized index selection over mass selection is only notable when the heritability is low and the scheme is large (in which case indices put more emphasis on family information than mass selection) and when the heritability is high and the scheme is small (in which case indices put less emphasis on family information).

This paper reports the first direct molecular evidence that X chromosome loss during determination of male aphids (XO) is random. Clonal and sexual females, and males, of the species Sitobion near fragariae were screened using three polymorphic microsatellite loci. Two loci, Sm10 and Sm17, showed the same heterozygous genotypes in all three aphid morphs. The third, Sm11, was heterozygous for the same two alleles in clonal and sexual females, but of the 25 males screened 11 showed the ‘160’ allele and 14 showed the ‘156’ allele. This result indicates X-linkage of locus Sm11, with random loss of the X chromosome during the formation of male embryos. The possible implications of this result are discussed with respect to aphid sex determination, recombination and chromosome evolution.

We compare the powers of three methods for the QTL analysis of non-normally distributed traits. We describe the nonparametric and the logistic regression approaches recently proposed in the literature and study the properties of the standard regression interval mapping method when the trait is not normally distributed. It is shown that the standard approach is robust against non-normality and behaves quite well for both continuous and discrete characters. The loss of power compared with the nonparametric or the logistic approach is generally minor. Moreover, the least squares estimation procedure of the regression interval mapping is not affected by departure from normality. The use of other approaches could be restricted to extreme cases where the trait distribution is very skewed.

A new hereditary duplication located at the hetechromatin region of the 14th chromosome is described by CBG, CDD and RE banding in a family of Charolais cattle.