Linear Selection Index Research Articles

Plateau-linear reaction norm model analysis of number born alive in purebred Landrace pigs using meteorological data in Japan.

We performed a plateau-linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off-farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau-linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau-linear RNM was lower than that for a simple repeatability model (RM). With the plateau-linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be -0.52 from the plateau-linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau-linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient.

The statistical theory of linear selection indices from phenotypic to genomic selection.

A linear selection index (LSI) can be a linear combination of phenotypic values, marker scores, and genomic estimated breeding values (GEBVs); phenotypic values and marker scores; or phenotypic values and GEBVs jointly. The main objective of the LSI is to predict the net genetic merit (H), which is a linear combination of unobservable individual traits’ breeding values, weighted by the trait economic values; thus, the target of LSI is not a parameter but rather the unobserved random H values. The LSI can be single‐stage or multi‐stage, where the latter are methods for selecting one or more individual traits available at different times or stages of development in both plants and animals. Likewise, LSIs can be either constrained or unconstrained. A constrained LSI imposes predetermined genetic gain on expected genetic gain per trait and includes the unconstrained LSI as particular cases. The main LSI parameters are the selection response, the expected genetic gain per trait, and its correlation with H. When the population mean is zero, the selection response and expected genetic gain per trait are, respectively, the conditional mean of H and the genotypic values, given the LSI values. The application of LSI theory is rapidly diversifying; however, because LSIs are based on the best linear predictor and on the canonical correlation theory, the LSI theory can be explained in a simple form. We provided a review of the statistical theory of the LSI from phenotypic to genomic selection showing their relationships, advantages, and limitations, which should allow breeders to use the LSI theory confidently in breeding programs.

Selective consumption of macroalgal species by herbivorous fishes suggests reduced functional complementarity on a fringing reef in Moorea, French Polynesia

Worldwide, many coral reefs are at risk of shifting to degraded algal-dominated states, due to compromised ecological conditions. Functional diversity of herbivorous fishes maintains coral reef health and promotes reef resilience to disturbances. Given previous evidence, it appears the functional roles of herbivorous fishes differ across geographical locations, indicating a need for further assessment of macroalgal consumption by herbivorous fishes. We assessed functional diversity by examining foraging behavior of herbivorous fish species on macroalgae on a fringing reef in Moorea, French Polynesia. We video-recorded choice experiments containing seven common macroalgae and used Strauss' linear resource selection index to determine macroalgal selectivities. We used cluster analysis to identify any distinct groups within herbivorous fish species, given the macroalgal species they targeted, and fitted generalized linear mixed-effects models to identify factors that best predicted the number of bites taken on macroalgae. Seven species from 3 fish families/tribes took a total of 956 bites. Fish species differed in their selectivity with some species (Naso lituratus, N. unicornis, Calatomus carolinus) strongly preferring one or two macroalgal species, while other fish species (Acanthurus nigrofuscus, Ctenochaetus striatus, Chlorurus sordidus, Balistapus undulatus) were less selective. This resulted in fish species forming two clusters. Only 3 of 7 macroalgae were preferred by any fish species, with two fish species both preferring the same two macroalgae. The limited differences in fish species' preferences for different macroalgae suggests limited functional complementarity. Two models (macroalgal species identity+fish functional group, macroalgal species identity+fish species) best predicted the number of bites taken on macroalgae compared to models incorporating only a single explanatory factor or fish family. In the context of this Moorean fringing reef, there is greater functional redundancy than complementarity of herbivorous fishes consuming macroalgae, and the fishes grouped together according to their relative selectivity. We observed fish species that are not classified as browsers consuming macroalgae, suggesting diets of herbivorous fishes may be broader than previously thought. Finally, we observed macroalgal selectivities and consumption that differed from previous studies for the same fish species. Our results contribute to the understanding of functional diversity of herbivorous fishes across coral reefs, and also highlight the need for additional research to further elucidate the role of context and functional diversity of herbivorous fishes consuming macroalgae on coral reefs.

MGIDI: toward an effective multivariate selection in biological experiments

AbstractMotivationMultivariate data are common in biological experiments and using the information on multiple traits is crucial to make better decisions for treatment recommendations or genotype selection. However, identifying genotypes/treatments that combine high performance across many traits has been a challenger task. Classical linear multi-trait selection indexes are available, but the presence of multicollinearity and the arbitrary choosing of weighting coefficients may erode the genetic gains.ResultsWe propose a novel approach for genotype selection and treatment recommendation based on multiple traits that overcome the fragility of classical linear indexes. Here, we use the distance between the genotypes/treatment with an ideotype defined a priori as a multi-trait genotype–ideotype distance index (MGIDI) to provide a selection process that is unique, easy-to-interpret, free from weighting coefficients and multicollinearity issues. The performance of the MGIDI index is assessed through a Monte Carlo simulation study where the percentage of success in selecting traits with desired gains is compared with classical and modern indexes under different scenarios. Two real plant datasets are used to illustrate the application of the index from breeders and agronomists’ points of view. Our experimental results indicate that MGIDI can effectively select superior treatments/genotypes based on multi-trait data, outperforming state-of-the-art methods, and helping practitioners to make better strategic decisions toward an effective multivariate selection in biological experiments.Availability and implementationThe source code is available in the R package metan (https://github.com/TiagoOlivoto/metan) under the function mgidi().Supplementary informationSupplementary data are available at Bioinformatics online.

Expectation and variance of the estimator of the maximized selection response of linear selection indices with normal distribution

Key messageThe expectation and variance of the estimator of the maximized index selection response allow the breeders to construct confidence intervals and to complete the analysis of a selection process.The maximized selection response and the correlation of the linear selection index (LSI) with the net genetic merit are the main criterion to compare the efficiency of any LSI. The estimator of the maximized selection response is the square root of the variance of the estimated LSI values multiplied by the selection intensity. The expectation and variance of this estimator allow the breeder to construct confidence intervals and determine the appropriate sample size to complete the analysis of a selection process. Assuming that the estimated LSI values have normal distribution, we obtained those two parameters as follows. First, with the Fourier transform, we found the distribution of the variance of the estimated LSI values, which was a Gamma distribution; therefore, the expectation and variance of this distribution were the expectation and variance of the variance of the estimated LSI values. Second, with these results, we obtained the expectation and the variance of the estimator of the selection response using the Delta method. We validated the theoretical results in the phenotypic selection context using real and simulated dataset. With the simulated dataset, we compared the LSI efficiency when the genotypic covariance matrix is known versus when this matrix is estimated; the differences were not significant. We concluded that our results are valid for any LSI with normal distribution and that the method described in this work is useful for finding the expectation and variance of the estimator of any LSI response in the phenotypic or genomic selection context.

Combined Multistage Linear Genomic Selection Indices To Predict the Net Genetic Merit in Plant Breeding.

A combined multistage linear genomic selection index (CMLGSI) is a linear combination of phenotypic and genomic estimated breeding values useful for predicting the individual net genetic merit, which in turn is a linear combination of the true unobservable breeding values of the traits weighted by their respective economic values. The CMLGSI is a cost-saving strategy for improving multiple traits because the breeder does not need to measure all traits at each stage. The optimum (OCMLGSI) and decorrelated (DCMLGSI) indices are the main CMLGSIs. Whereas the OCMLGSI takes into consideration the index correlation values among stages, the DCMLGSI imposes the restriction that the index correlation values among stages be zero. Using real and simulated datasets, we compared the efficiency of both indices in a two-stage context. The criteria we applied to compare the efficiency of both indices were that the total selection response of each index must be lower than or equal to the single-stage combined linear genomic selection index (CLGSI) response and that the correlation of each index with the net genetic merit should be maximum. Using four different total proportions for the real dataset, the estimated total OCMLGSI and DCMLGSI responses explained 97.5% and 90%, respectively, of the estimated single-stage CLGSI selection response. In addition, at stage two, the estimated correlations of the OCMLGSI and the DCMLGSI with the net genetic merit were 0.84 and 0.63, respectively. We found similar results for the simulated datasets. Thus, we recommend using the OCMLGSI when performing multistage selection.

Efficiency of a Constrained Linear Genomic Selection Index To Predict the Net Genetic Merit in Plants.

The constrained linear genomic selection index (CLGSI) is a linear combination of genomic estimated breeding values useful for predicting the net genetic merit, which in turn is a linear combination of true unobservable breeding values of the traits weighted by their respective economic values. The CLGSI is the most general genomic index and allows imposing constraints on the expected genetic gain per trait to make some traits change their mean values based on a predetermined level, while the rest of them remain without restrictions. In addition, it includes the unconstrained linear genomic index as a particular case. Using two real datasets and simulated data for seven selection cycles, we compared the theoretical results of the CLGSI with the theoretical results of the constrained linear phenotypic selection index (CLPSI). The criteria used to compare CLGSI vs. CLPSI efficiency were the estimated expected genetic gain per trait values, the selection response, and the interval between selection cycles. The results indicated that because the interval between selection cycles is shorter for the CLGSI than for the CLPSI, CLGSI is more efficient than CLPSI per unit of time, but its efficiency could be lower per selection cycle. Thus, CLGSI is a good option for performing genomic selection when there are genotyped candidates for selection.

Efficiency of linear selection index in predicting rice hybrid performance

Selection index (SI) theory has been applied to predict the net genetic merit, select parents for the next cycle, and maximize the selection response in plant breeding. However, up to now, SI has not been applied to predict unobserved hybrid performance. In hybrid breeding, it is impossible to test every cross, and accurate prediction can help breeders greatly reduce the experimental cost. Traditional genomic selection (GS) targets single-trait prediction, and useful information about other related traits is ignored. With the data set of 575 rice hybrids subjected to the North Carolina mating design II, this information was utilized to develop a linear SI-based GS method to predict rice hybrid for a more accurate and comprehensive selection. Cross-validation results showed that genetic information of a low-heritability target trait such as grain yield could be greatly aggregated from auxiliary traits using SI. When SI was used for directly predicting the target trait, the SI-direct prediction underperformed the traditional genomic prediction in most cases. However, when the SI-direct prediction was combined with the traditional genomic prediction using a suitable weight ω inferred from the training data set, significantly higher accuracy could be obtained. This method was called as SI-assisted prediction. It provided a promising prediction means for breeding application, which used the phenotypes of auxiliary traits only in the training data set. Additionally, it was found that SI-assisted accuracy increased as the genetic correlation between auxiliary traits and the target trait increased, and high heritability of auxiliary traits could also improve the prediction performance.

An evaluation of alternative selection indexes for a non-linear profit trait approaching its economic optimum.

This study used simulation to evaluate the performance of alternative selection index configurations in the context of a breeding programme where a trait with a non-linear economic value is approaching an economic optimum. The simulation used a simple population structure that approximately mimics selection in dual purpose sheep flocks in New Zealand (NZ). In the NZ dual purpose sheep population, number of lambs born is a genetic trait that is approaching an economic optimum, while genetically correlated growth traits have linear economic values and are not approaching any optimum. The predominant view among theoretical livestock geneticists is that the optimal approach to select for nonlinear profit traits is to use a linear selection index and to update it regularly. However, there are some nonlinear index approaches that have not been evaluated. This study assessed the efficiency of the following four alternative selection index approaches in terms of genetic progress relative to each other: (i) a linear index, (ii) a linear index updated regularly, (iii) a nonlinear (quadratic) index, and (iv) a NLF index (nonlinear index below the optimum and then flat). The NLF approach does not reward or penalize animals for additional genetic merit beyond the trait optimum. It was found to be at least comparable in efficiency to the approach of regularly updating the linear index with short (15year) and long (30year) time frames. The relative efficiency of this approach was slightly reduced when the current average value of the nonlinear trait was close to the optimum. Finally, practical issues of industry application of indexes are considered and some potential practical benefits of efficient deployment of a NLF index in highly heterogeneous industries (breeds, flocks and production environments) such as in the NZ dual purpose sheep population are discussed.

Multivariate approach for young bull selection from a performance test using multiple traits of economic importance

This study used multivariate statistics to identify clusters of animals with similar expected progeny difference (EPD) and also identify leading traits that discriminate between bulls. Various linear selection indices based on specific selection criteria were proposed. Records were collected from 880 young Nelore bulls submitted to performance testing in central Brazil between 2001 and 2012. Pre-weaning average daily gain and weights at 210 days with direct and maternal effects were used in the analysis, in addition to post-weaning weight, scrotal circumference at 365 and 450 days, carcass finish and rib eye area. EPDs were classified into three groups, and the EPD means of two of these groups stood out and were considered important based on principal component analysis that associated higher values of direct EPD of weights, average daily weight gain and scrotal circumference. The EPDs for weight at 210, 365 and 450 days, pre- and post-weaning daily gain and scrotal circumference at 365 and 450 days were major causes of variation. Each linear selection index proposed (SI₁, SI₂, SI₃, SI₄ and SI₅) defined a specific approach meaning that a different selection index should be used depending on breeding goals and selection criteria.

Algal foraging by a rheophilic chironomid (Eukiefferiella claripennis Lundbeck) extensively encountered in high nutrient enriched streams

Water enrichment has been described to have major impact on the freshwater food-webs structure. We investigated the structure of the epilithic algal assemblage and the gut contents of a rheophilic orthoclad in situ in a nutrient enriched stream. 4-th instar larvae of Eukiefferiella claripennis, a widespread orthoclad in the Palearctic ecozone and abundant in nutrient-enriched streams, were chosen to be studied due to their great densities in such environment. The taxonomic composition of the epilithic algal assemblage, the gut contents and the ingested algal-cell biovolumes were used to establish the foraging behavior of this chironomid, and to explain its success under such environmental conditions. A Colnertia analysis, and a Two Way INdicator SPecies ANalysis were uses to compare the co-structure of algae assemblages in the epilithon and the guts. Then Strauss's linear food selection index was used to assess the electivity of each algae taxon. Cell biovolumes and distribution were also compared and tested for significant differences between the epilithic algal assemblage and the gut contents. Biovolumes of cells in the epilithon were tested for significant differences from the mean biovolume value of the ingested cells. Eukiefferiella claripennis larvae seem to feed preferentially on Chlorophyceae and Cyanophyceae; Diatomophyceae were randomly grazed without preference while Rhodophyceae were avoided. Cell biovolumes were significantly different between the epilithon and the gut contents, and the larvae preferentially consumed a narrow range of cell biovolumes compared to the wide range of cell biovolumes encountered in the environment. Stigeoclonium and Oscillatoria were the key taxa of the algal assemblage foraged by the larvae. Two complementary approaches seem to drive the foraging behavior of this orthoclad, 1) taxa-type preference and 2) cell-bivolume preference. In nutrient enriched streams, an algal assemblage consisting of cells of Chlorophyceae and Cyanophyceae averaging 1702 μm 3 offers the best diet to this chironomid.

Breeding and genetics of waterfowl

Genetic parameters of traits of interest in ducks and geese are reported. Recent achievements of research, specially to improve the Brown Tsaiya laying duck; the intergeneric crossbreeding in duck; the meat ducks Pekin and Muscovy; the goose reproduction ability; the meat and fatty liver production in geese and ducks were analysed. A linear genetic selection index based on MT-BLUP animal model of breeding values with objectives to maintain body weight, egg weight at current levels, to increase egg shell strength and egg number, used for four generations in Brown Tsaiya laying ducks was evaluated. A seven generations selection experiment was successful in increasing the duration of fertility of Brown Tsaiya female ducks by artificial insemination with pooled Muscovy semen. The genetic parameters of growth and laying traits in a selected Muscovy line in Taiwan were estimated. Favourable genetic trends for body weight at given age, without decreasing egg production, were found in this selected Muscovy line. The selection of grandparent stocks (White Tsaiya and Pekin) for white plumage mule ducks in Taiwan was efficient. The genetic parameters of laying and gosling production traits of Landaise geese and of a White plumage goose strain, bred in two systems, respectively outdoors with natural photoperiodism and in indoors with short daylight conditions were reported. The more advantageous system in selecting for laying traits in geese is discussed. Fatty liver weight was found to be highly heritable in geese and male Muscovy ducks. The genetic parameters of overfed mule duck traits were estimated in the Pekin dam population. It is concluded that future research should focus on biotechnology, quantitative trait loci detection and genome analysis.

Genetic Trends for Laying Traits in the Brown Tsaiya (Anas platyrhynchos) Selected with Restricted Genetic Selection Index

A small body size of Brown Tsaiya laying duck is desirable to reduce maintenance requirements, so the body weight at 40 weeks of age (BW40) has to be maintained at its current level. Egg weight has to be maintained at around 65 g to meet market requirements. Eggshell strength at 40 weeks of age (ES40) must to be increased in order to maintain a low incidence of broken eggs. Thus, number of eggs laid up to 52 weeks of age (EN52) has to be increased without negative correlated response on ES40. A new linear genetic selection index was used: Ig=a0×GEW40 (g)+a1×GBW40 (g)+a2×GES40 (kg/cm 2 )+a3×GEN52 (eggs) where GEW40, GBW40, GES40 and GEN52 were the multitrait best linear unbiased prediction (MT-BLUP) animal model predictors of the breeding values respectively of egg weight and body weight at 40 weeks of age (EW40, BW40), ES40 and EN52. The coefficients a0, a1, a2 and a3 were calculated with constraints of 0.0 g, 0.0 g and 0.013 kg/cm 2 for expected genetic gains in EW40, BW40 and ES40 respectively and maximum gain in EN52. Since 1997, the drakes and the ducks were selected according to their own indexes, with this new genetic selection index. From G0 to G4, the average per generation predicted genetic responses in female duck were +0.05 g for EW40, +0.92 g for BW40, +0.035 kg/cm 2 for ES40 and +2.13 eggs for EN52. Which represented respectively 0.07%, 0.06%, 0.67% and 1.0% of the means of the EW40, BW40, ES40 and EN52. For ES40 and EN52, it represented also respectively 16.1% and 21.6% of the additive genetic standard deviation of these traits. These results indicated that selection of laying Brown Tsaiya by a restricted genetic selection index and with MT-BLUP animal model could be an efficient tool for improving the efficiency of egg production, increasing egg shell strength and egg number while holding egg weight and body weight constants. (Asian-Aust. J. Anim. Sci. 2003. Vol 16, No. 12 : 1705- 1710)

Prey selectivity in planktivorous juvenile fishes associated with floating objects in the western Mediterranean

Aspects of the feeding ecology of planktivorous juvenile fishes were assessed by comparing stomach content samples from purse seine catches with food environment samples from vertical hauls of a WP-2 mesozooplankton net. Sampling was performed fortnightly over 2 years, around moored experimental floating objects and at open water control stations in oceanic Mediterranean waters. Species composition, abundance and seasonal occurrence of both potential prey (zooplankton) and predators (fish) were studied. Dietary composition was examined for six fish species, and prey selectivity was assessed by means of several selectivity indices: Ivlev's selectivity index, linear food selection index and forage ratio. A comparison of the results showed significant variations among indices, related to both the degree of selection and the relative abundance of the food type in the environment. Most fish caught were pelagic juveniles, and had fed on plankton; however, there was variability in diet composition and prey selectivity among species allowing their separation into generalists and specialists. The pilot fish Naucrates ductor (Linnaeus) was a specialist, preying upon neustonic hyperiid amphipods, whereas the three congeneric species of Trachurus: T. picturatus (Bowdich), T. trachurus (Linnaeus) and T. mediterraneus (Steinachner) fed on copepods and other planktonic crustaceans. The species studied appeared to partition food resources to minimize the overlap in use of a very specific habitat.

The diet of two mud-shrimps,Calocaris macandreaeandUpogebia stellata(Crustacea: Decapoda: Thalassinidea)

Abstract The monthly variations in the gut contents of Calocaris macandreae, principally a deposit feeder, and Upogebia stellata, principally a suspension feeder, were examined over the period of a year. The diet of both species consisted of a mixture of organic and fine inorganic fragments. The identifiable components of their respective diets included diatoms, dinoflagellates, algal and terrestrial plant fragments, and material of animal origin. The contribution of these different dietary components varied between the two species, and also over the year within each species. Using Strauss' Linear Food Selection Index (L) it was found that U. stellata positively selected diatoms, dinoflagellates and plant and algal fragments whilst avoiding animal material. In contrast, C. marandreae showed little evidence of food selectivity, except in the case of algal material which was positively selected. In general, variations observed in the diet were related to changes in food abundance and availability within the...

Comparison of selection based on phenotype, selection index and best linear unbiased prediction using data from a closed broiler line

1. Selection based on three methods of estimating breeding values, Best Linear Unbiased Prediction (BLUP), selection index (SI), and phenotype (SP) were compared for three traits, juvenile body weight (JW), percentage breast meat yield (BM) and hen‐day rate of egg production (EP) using records provided by a commercial broiler breeding company. 2. Product moment correlations were calculated between breeding values estimated by each method and averaged across sexes. A mean correlation of 0.69 was obtained between selection on SP and BLUP for JW. Mean correlations of 0.88 and 0.68 and 0.87 were obtained between SI and BLUP for the traits JW, EP and BM, respectively. 3. A mean estimated genetic response of 77.7% was obtained with SP for JW relative to BLUP in the absence of restrictions on the selection of close relatives. Estimated genetic responses of 90.7%, 66.9% and 88.4% were obtained by SI relative to BLUP for JW, EP and BM, respectively. 4. Applying restrictions on the selection of close relatives resulted in slight decreases in estimated responses but not in the respective ranking of the selection methods. 5. The results indicate that BLUP could provide commercial breeders with increased selection responses compared to index selection, in particular for traits of low heritability and where relatively few animals possess performance records.

Optimum linear selection indexes for multiple generation objectives with non-linear profit functions

AbstractA method to obtain linear selection indexes that maximize objectives that involve average profit in one or more generations within a planning horizon based on non-linear profit functions, was derived through application of optimal control theory. The method involves simultaneous optimization of indexes for each generation in the planning horizon. Optimum linear indexes were found to be conform indexes derived from selection index theory, with economic values equal to a weighted sum of partial derivatives of the profit function at the trait means which result in each generation of the planning horizon. Numerical procedures to derive optimum indexes are presented. Methods and properties of alternative strategies for selection witli non-linear profit functions are illustrated for selection on egg weight and rate of lay in poultry. In the example, the additional benefit of selection indexes that maximize cumulative net present value of profit over a planning horizon of10 years was small relative to use of traditional selection procedures. Optimal indexes were also derived with a derivative-free non-linear programming optimizer, with identical results. The latter method also allows incorporation of additional constraints.Possible extensions of the optimal control methodology to address other problems related to optimization of selection over multiple generations are discussed.

Optimum linear indices for non-linear profit functions

AbstractAn iterative method is presented, based on the method of Moav and Hill (1966) to derive the optimum linear selection index for any number of traits with linear or non-linear profit functions. For non-linear profit functions the index weights will be functions of the trait means prior to selection and the selection intensity. Using the equations developed, the optimum selection index for three dairy cattle milk production traits was computed. Convergence was obtained after three to four iterations, and was robust to the starting values used for iteration. The ratio of expected genetic gains were only marginally different for selection intensities of 1 and 4 standard deviation units. Differences were greater for the index coefficients. All alternative indices tested gave lower gains in profit than the optimum index. For linear profit functions this index reduces to the standard linear index, and for two uncorrelated traits this index reduces to the index of Moav and Hill (1966).

A unified procedure of computing restricted best linear unbiased prediction and restricted selection index1,2

A unified procedure of computing restricted best linear unbiased prediction and restricted selection index¹,2

Alternative methods of selection for litter size in mice: I. Characterization of base population and development of methods.

Studies on a base population of mice were used to establish an index of components of litter size and a physiological model for measuring uterine capacity to be used subsequently in a selection experiment evaluating alternative methods for practicing selection to increase litter size. Heritability estimates of litter size, ovulation rate and ova success (fraction of ova resulting in fully formed pups) were .18, .33 and .15, respectively. No significant genetic or phenotypic correlation was found between overall ovulation rate and ova success. Phenotypic means and genetic variances were higher for characteristics measured on the right than on the left side of the reproductive tract. Linear and quadratic selection indexes, derived for a quadratic definition of breeding value, were compared. The linear index was predicted to be .99 as efficient as the quadratic one. Due to simplicity, the linear index (I = 1.21 x ovulation rate + 9.05 x ova success), scaled to have variance the same as litter size, was chosen for use. Ovulation rate in unilaterally ovariectomized females was .95 of that in females with both ovaries. No hypertrophy of the ipsilateral uterine horn in unilaterally ovariectomized females was found before implantation of embryos. Thus, unilateral ovariectomy appears to provide a physiological state to measure uterine capacity (as litter size) in the mouse.