Reliability test of the visual assessment of cranial traits for sex determination

Interobserver variation in visual evaluation was analyzed for 10 cranial traits in a homogeneous archaeological series. Two observers independently scored cranial traits commonly used for determination of sex. Though determination of sex did not differ significantly for the two observers, individual traits had different levels of interobserver reliability. In addition, indices of relative "maleness" and "femaleness" derived by the two observers differed at statistically significant levels. Because such indices are used in cross-population comparisons of relative gracility and robusticity of diverse samples, these comparisons should be interpreted with caution when more than one investigator has performed an assessment. Most of our instances of interobserver discordance derived from character traits described in subjective terms without accompanying diagrams. Clarity of definition, rather than number of character traits, was found to be critical for effective determination of sex by the visual assessment method. Use of fewer, more precisely defined character traits can improve interobserver reliability.     

Motivational value and salience of images of infants

Researchers have typically reported relatively greater preferences for infants among females than among males, though this varies somewhat across samples and age groups. The mechanism by which this sex difference occurs is not well understood and many studies rely on participants' self-reported preferences rather than measuring motivated behavior or patterns of visual attention directly. The present research consists of two independent studies investigating attention to infants. The aim of these studies was to extend research on the characteristics associated with interest in infants by measuring motivation to view infant faces (Study 1) and visual attention to infants in a complex visual scene (Study 2). In Study 1, participants controlled the length of viewing time for different images. Women demonstrated motivation to extend viewing of infants. Men showed the opposite pattern, working to decrease the length of time they viewed infants. In Study 2, participants were shown complex scenes that contained infants. Patterns of visual attention were measured using eye-tracking technology. Infants did not receive a particularly high proportion of fixations from either sex. However, there were relationships between gender-linked traits, such as digit ratio and self-reported interest in infants, and the percentage of fixations on infants. Additionally, participants who reported being in a romantic relationship demonstrated greater interest in infants. This suggests the long-reported sex difference in interest in infants may relate more to current life circumstances and gender linked traits than an overwhelming tendency among all members of a sex.     

Accuracies of genomic prediction for twenty economically important traits in Chinese Simmental beef cattle

Genomic prediction has been widely utilized to estimate genomic breeding values (GEBVs) in farm animals. In this study, we conducted genomic prediction for 20 economically important traits including growth, carcass and meat quality traits in Chinese Simmental beef cattle. Five approaches (GBLUP, BayesA, BayesB, BayesCπ and BayesR) were used to estimate the genomic breeding values. The predictive accuracies ranged from 0.159 (lean meat percentage estimated by BayesCπ) to 0.518 (striploin weight estimated by BayesR). Moreover, we found that the average predictive accuracies across 20 traits were 0.361, 0.361, 0.367, 0.367 and 0.378, and the averaged regression coefficients were 0.89, 0.86, 0.89, 0.94 and 0.95 for GBLUP, BayesA, BayesB, BayesCπ and BayesR respectively. The genomic prediction accuracies were mostly moderate and high for growth and carcass traits, whereas meat quality traits showed relatively low accuracies. We concluded that Bayesian regression approaches, especially for BayesR and BayesCπ, were slightly superior to GBLUP for most traits. Increasing with the sizes of reference population, these two approaches are feasible for future application of genomic selection in Chinese beef cattle.     

Technical note: Prediction of sex based on five skull traits using decision analysis (CHAID)

Osteologists commonly assess the sex of skeletal remains found in forensic and archaeological contexts based on ordinal scores of subjectively assessed sexually dimorphic traits. Using known‐sex samples, logistic regression (LR) discriminant functions have been recently developed, which allow sex probabilities to be determined. A limitation of LR is that it emphasizes main effects and not interactions. Chi‐square automatic interaction detection (CHAID) is an alternative classification strategy that emphasizes the information in variable interactions and uses decision trees to maximize the probability of correct sex determinations. We used CHAID to analyze the predictive value of the 31 possible combinations of five sexually dimorphic skull traits that Walker used previously to develop logistic regression sex determination equations. The samples consisted of 304 individuals of known sex of English, African American, and European American origin. Based on practical considerations, selection criteria for the best sex predictive trait combinations (SPTCs) were set at accuracies for both sexes of 75% or greater and sex biases lower than 5%. Although several of the trees meeting these criteria were produced for the English and European American samples, none met them for the African American sample. In the series of out‐of‐sample tests we performed, the trees from the English and combined sample of all groups predicted best. Am J Phys Anthropol, 2009. © 2009 Wiley‐Liss, Inc.     

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins

SNP chips are commonly used for genotyping animals in genomic selection but strategies for selecting low-density (LD) SNPs for imputation-mediated genomic selection have not been addressed adequately. The main purpose of the present study was to compare the performance of eight LD (6K) SNP panels, each selected by a different strategy exploiting a combination of three major factors: evenly-spaced SNPs, increased minor allele frequencies, and SNP-trait associations either for single traits independently or for all the three traits jointly. The imputation accuracies from 6K to 80K SNP genotypes were between 96.2 and 98.2%. Genomic prediction accuracies obtained using imputed 80K genotypes were between 0.817 and 0.821 for daughter pregnancy rate, between 0.838 and 0.844 for fat yield, and between 0.850 and 0.863 for milk yield. The two SNP panels optimized on the three major factors had the highest genomic prediction accuracy (0.821–0.863), and these accuracies were very close to those obtained using observed 80K genotypes (0.825–0.868). Further exploration of the underlying relationships showed that genomic prediction accuracies did not respond linearly to imputation accuracies, but were significantly affected by genotype (imputation) errors of SNPs in association with the traits to be predicted. SNPs optimal for map coverage and MAF were favorable for obtaining accurate imputation of genotypes whereas trait-associated SNPs improved genomic prediction accuracies. Thus, optimal LD SNP panels were the ones that combined both strengths. The present results have practical implications on the design of LD SNP chips for imputation-enabled genomic prediction.     

Reciprocal style polymorphisms are not easily categorised: the case of heterostyly in Lithodora and Glandora (Boraginaceae)

Interest in reciprocal floral polymorphisms, such as heterostyly, has increased in recent decades because they can be used as suitable model systems to study mechanisms of outbreeding and disassortative mating in plants. Heterostylous plants are characterised by the presence of discrete morphs that differ in sex organ position and in some other ancillary traits. As regards sex organ deployment, different types of polymorphisms have been described, depending on number and type of discrete classes present in populations and degree of reciprocity between them. However, a clear-cut characterisation of stylar polymorphisms does not appear to be the best approach when there is great variability among populations because of continuous variation of some of traits examined. A recent study in Lithodora sensu lato (recently split into two separate genera, Lithodora and Glandora) showed a wide variation in sex organ position across species in the genus, which warrants precise population analysis of stylar polymorphism and its reciprocity. We provide a detailed morphometric analysis of flower sexual traits and include those considered to be ancillary characters. We report a wide variation in these traits in populations of Lithodora s.l. and highlight the subjectivity of the former characterisation of style polymorphism based on visual inspection. Ancillary traits appear repeatedly in Lithodora and Glandora, particularly in the latter. The appearance of these traits seems to be related to greater reciprocity between sexual whorls in Glandora, with the exception of G.?prostrata. These results agree with evolutionary steps proposed in the build-up of heterostyly according to some evolutionary models. We also examined variation in polymorphisms in light of current models for evolution of heterostyly, and, more specifically, we sought to verify the prediction that flower traits as a whole (i.e., flower integration) respond to selective pressure to assure the exact location of pollen on the pollinator body. Most reciprocal populations and species, where between-morph pollen transfer is expected to be higher, would show greater integration. Our results confirm this hypothesis.     

Genomic prediction of reproduction traits for Merino sheep

Economically important reproduction traits in sheep, such as number of lambs weaned and litter size, are expressed only in females and later in life after most selection decisions are made, which makes them ideal candidates for genomic selection. Accurate genomic predictions would lead to greater genetic gain for these traits by enabling accurate selection of young rams with high genetic merit. The aim of this study was to design and evaluate the accuracy of a genomic prediction method for female reproduction in sheep using daughter trait deviations (DTD) for sires and ewe phenotypes (when individual ewes were genotyped) for three reproduction traits: number of lambs born (NLB), litter size (LSIZE) and number of lambs weaned. Genomic best linear unbiased prediction (GBLUP), BayesR and pedigree BLUP analyses of the three reproduction traits measured on 5340 sheep (4503 ewes and 837 sires) with real and imputed genotypes for 510 174 SNPs were performed. The prediction of breeding values using both sire and ewe trait records was validated in Merino sheep. Prediction accuracy was evaluated by across sire family and random cross‐validations. Accuracies of genomic estimated breeding values (GEBVs) were assessed as the mean Pearson correlation adjusted by the accuracy of the input phenotypes. The addition of sire DTD into the prediction analysis resulted in higher accuracies compared with using only ewe records in genomic predictions or pedigree BLUP. Using GBLUP, the average accuracy based on the combined records (ewes and sire DTD) was 0.43 across traits, but the accuracies varied by trait and type of cross‐validations. The accuracies of GEBVs from random cross‐validations (range 0.17–0.61) were higher than were those from sire family cross‐validations (range 0.00–0.51). The GEBV accuracies of 0.41–0.54 for NLB and LSIZE based on the combined records were amongst the highest in the study. Although BayesR was not significantly different from GBLUP in prediction accuracy, it identified several candidate genes which are known to be associated with NLB and LSIZE. The approach provides a way to make use of all data available in genomic prediction for traits that have limited recording.     

QTL mapping and breeding value estimation through pedigree-based analysis of fruit size and weight in four diverse peach breeding programs

The narrow genetic base of peach (Prunus persica L. Batsch) challenges efforts to accurately dissect the genetic architecture of complex traits. Standardized phenotypic assessment of pedigree-linked breeding germplasm and new molecular strategies and analytical approaches developed and conducted during the RosBREED project for enabling marker-assisted breeding (MAB) in Rosaceae crops has overcome several aspects of this challenge. The genetic underpinnings of fruit size (fruit equatorial diameter (FD)) and weight (fresh weight (FW)), two most important components of yield, were investigated using the pedigree-based analysis (PBA) approach under a Bayesian framework which has emerged as an alternative strategy to study the genetics of quantitative traits within diverse breeding germplasm across breeding programs. In this study, a complex pedigree with the common founder “Orange Cling” was identified and FD and FW data from 2011 and 2012 analyzed. A genetic model including genetic additive and dominance effects was considered, and its robustness was evaluated by using various prior and initial values in the Markov chain Monte Carlo procedure. Five QTLs were identified which accounted for up to 29 and 17 % of the phenotypic variation for FD and FW, respectively. Additionally, genomic breeding values were obtained for both traits, with accuracies >85 %. This approach serves as a model study for performing PBA across diverse pedigrees. By incorporating multiple breeding programs, the method and results presented support and highlight the ability of this strategy to identify genomic resources as targets for DNA marker development and subsequent MAB within each program.     

Sex discrimination from the acetabulum in a twentieth-century skeletal sample from France using digital photogrammetry

Digital photogrammetric methods were used to collect diameter, area, and perimeter data of the acetabulum for a twentieth-century skeletal sample from France (Georges Olivier Collection, Musée de l’Homme, Paris) consisting of 46 males and 36 females. The measurements were then subjected to both discriminant function and logistic regression analyses in order to develop osteometric standards for sex assessment. Univariate discriminant functions and logistic regression equations yielded overall correct classification accuracy rates for both the left and the right acetabula ranging from 84.1% to 89.6%. The multivariate models developed in this study did not provide increased accuracy over those using only a single variable. Classification sex bias ratios ranged between 1.1% and 7.3% for the majority of models. The results of this study, therefore, demonstrate that metric analysis of acetabular size provides a highly accurate, and easily replicable, method of discriminating sex in this documented skeletal collection. The results further suggest that the addition of area and perimeter data derived from digital images may provide a more effective method of sex assessment than that offered by traditional linear measurements alone.     

Heritability of anthropometric phenotypes in caste populations of Visakhapatnam,India

In this study, we used anthropometric data from six Andhra caste populations to examine heritability patterns of 23 anthropometric phenotypes (linear, craniofacial, and soft tissue measures) with special reference to caste differences. We obtained anthropometric data from 342 nuclear families from Brahmin, Reddy, Telaga, Nagara, Ag. Kshatriya, and Mala castes of Visakhapatnam, India. These caste groups represent the existing hierarchical stratification of Indian populations. We used a variance components approach to determine the heritability (h2) of these 23 anthropometric phenotypes (height, weight, BMI, etc.). The sample consisted of 1918 individuals ranging in age from 6 to 72 years (mean = 21.5, S.D. = 13.8). The heritabilities (h2 +/- S.E.) for all anthropometric traits for the entire sample were significant (p < 0.0001) and varied from 0.25 +/- 0.05 (BMI) to 0.61 +/- 0.05 (bizygomatic breadth) after accounting for sex, age, and caste effects. Since data on socioeconomic and nutritional covariates were available for a subset of families, we repeated the genetic analyses using this subset, which has yielded higher heritabilities ranging from 0.21 +/- 0.16 (head breadth) to 0.72 +/- 0.18 (nasal breadth). In general, craniofacial measurements exhibited higher h2 compared to linear measures. Breadth measurements and circumferences yielded more or less similar heritabilities. Age and sex effects were significant (p < 0.0001 ) for most of the traits, while the effects of caste, socioeconomic status, and nutritional status were inconsistent across the traits. In conclusion, anthropometric phenotypes examined in this study are under appreciable additive genetic influences.     

Genomic Predictability of Interconnected Biparental Maize Populations

Intense structuring of plant breeding populations challenges the design of the training set (TS) in genomic selection (GS). An important open question is how the TS should be constructed from multiple related or unrelated small biparental families to predict progeny from individual crosses. Here, we used a set of five interconnected maize (Zea mays L.) populations of doubled-haploid (DH) lines derived from four parents to systematically investigate how the composition of the TS affects the prediction accuracy for lines from individual crosses. A total of 635 DH lines genotyped with 16,741 polymorphic SNPs were evaluated for five traits including Gibberella ear rot severity and three kernel yield component traits. The populations showed a genomic similarity pattern, which reflects the crossing scheme with a clear separation of full sibs, half sibs, and unrelated groups. Prediction accuracies within full-sib families of DH lines followed closely theoretical expectations, accounting for the influence of sample size and heritability of the trait. Prediction accuracies declined by 42% if full-sib DH lines were replaced by half-sib DH lines, but statistically significantly better results could be achieved if half-sib DH lines were available from both instead of only one parent of the validation population. Once both parents of the validation population were represented in the TS, including more crosses with a constant TS size did not increase accuracies. Unrelated crosses showing opposite linkage phases with the validation population resulted in negative or reduced prediction accuracies, if used alone or in combination with related families, respectively. We suggest identifying and excluding such crosses from the TS. Moreover, the observed variability among populations and traits suggests that these uncertainties must be taken into account in models optimizing the allocation of resources in GS.     

太行山猕猴掌骨性别判别分析

为了了解成年太行山猕猴5根掌骨的性差大小。本文对太行山猕猴掌骨标本40例(雄:10, 雌:30)进行观察并选择掌骨的9项形态变量进行测量。数据用SPSS19.0软件进行多变量判别分析。结果表明: 5根掌骨长度等变量在性别之间有明显差异。用全模型法5根掌骨的性别正确判别率范围为94.1%~100.0%, 用逐步判别法掌骨性别正确判别率范围为93.8%~97.5%。有关掌骨的9个形态变量, 长度变量首先被挑选出来, 说明在性别判别中起重要作用。分别用左右侧掌骨的判别函数来判别性别时侧别差异不明显。结论: 用每根掌骨的形态变量建立判别函数可以有效地区分性别, 对非人灵长类掌骨标本的性别鉴定有一定的理论意义和应用价值。     

Genomic selection accuracies within and between environments and small breeding groups in white spruce

Background

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of N_e ≈ 20. Marker subsets were also tested.

Results

Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71–0.79) and moderately high for growth (r = 0.52–0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies.

Conclusions

Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.     

Accuracies of genomically estimated breeding values from pure-breed and across-breed predictions in Australian beef cattle

Background

The major obstacles for the implementation of genomic selection in Australian beef cattle are the variety of breeds and in general, small numbers of genotyped and phenotyped individuals per breed. The Australian Beef Cooperative Research Center (Beef CRC) investigated these issues by deriving genomic prediction equations (PE) from a training set of animals that covers a range of breeds and crosses including Angus, Murray Grey, Shorthorn, Hereford, Brahman, Belmont Red, Santa Gertrudis and Tropical Composite. This paper presents accuracies of genomically estimated breeding values (GEBV) that were calculated from these PE in the commercial pure-breed beef cattle seed stock sector.

Methods

PE derived by the Beef CRC from multi-breed and pure-breed training populations were applied to genotyped Angus, Limousin and Brahman sires and young animals, but with no pure-breed Limousin in the training population. The accuracy of the resulting GEBV was assessed by their genetic correlation to their phenotypic target trait in a bi-variate REML approach that models GEBV as trait observations.

Results

Accuracies of most GEBV for Angus and Brahman were between 0.1 and 0.4, with accuracies for abattoir carcass traits generally greater than for live animal body composition traits and reproduction traits. Estimated accuracies greater than 0.5 were only observed for Brahman abattoir carcass traits and for Angus carcass rib fat. Averaged across traits within breeds, accuracies of GEBV were highest when PE from the pooled across-breed training population were used. However, for the Angus and Brahman breeds the difference in accuracy from using pure-breed PE was small. For the Limousin breed no reasonable results could be achieved for any trait.

Conclusion

Although accuracies were generally low compared to published accuracies estimated within breeds, they are in line with those derived in other multi-breed populations. Thus PE developed by the Beef CRC can contribute to the implementation of genomic selection in Australian beef cattle breeding.     

Quantitative Genetics of Sex Ratio Traits in the Parasitic Wasp, Nasonia Vitripennis

We detected significant parent-offspring regressions for the first sex ratio (the sex ratio produced by a female in a fresh host) and the second sex ratio (the sex ratio produced by a female in a previously parasitized host) in the parasitic wasp, Nasonia vitripennis. For both traits, estimates of the narrow-sense heritability range from &0.05 to &0.15 (depending on how the data are analyzed). The study population was derived from isofemale strains created from wasps captured in a single bird nest. The same population exhibited no significant parent-offspring regression for the brood sizes associated with the first and second sex ratios. There may be a significant negative parent-offspring regression for diapause proportion in the first sex ratio broods. The estimates of the genetic correlations between first and second sex ratios are positive although almost all are not significantly different from 0.0. To our knowledge, this study is the first ``fine-scale' analysis of genetic variation for sex ratio traits in any species of insect. Such studies are an essential part of the assessment of the validity of claims that sex ratio traits are locally optimal.     

Morphological relationship between the cranial and supraorbital regions in Homo sapiens

Although some hypotheses that attempt to explain the variation in supraorbital region morphology in modern humans have been proposed, this issue is still not well understood. In this study, the craniofacial size and spatial models were tested using a sample of modern human crania from geographically diverse populations, and the co‐occurrence of the degrees of glabella (GL) and superciliary arch (ST) expression were analyzed. The two supraorbital structures were examined by visual assessment, and eight quantitative variables were included in the three‐way ANOVA, canonical variates analysis and partial rank correlation. The influences of sex and the region of origin of the cranial samples on the relationships between the examined variables and the degrees of supraorbital structures expression were also considered. The results only partially supported the craniofacial size and spatial models and suggested that GL and ST experienced separate influences during development. In the sample of all crania, the neurocranial size more strongly influenced the morphological variation of the ST than of the GL, and sex influenced both of these structures the most. The results suggest that sex may be the main factor (having an influence independent of the other traits) on the morphological variation of the GL and ST. Am J Phys Anthropol 156:110–124, 2015 © 2014 Wiley Periodicals, Inc.     

Mandibular ramus flexure: A new morphologic indicator of sexual dimorphism in the human skeleton

In the skeleton, male and female characteristics lie along a continuum of morphologic configurations and metric values. Size alone is not the best indicator of sex. In contrast, morphologic differences that arise from genetically sex-linked growth and development allow better separation of the sexes. This study presents a new morphologic indicator of sexual dimorphism in the human mandible. A sample of 300 mandibles from adults of known sex primarily from the Dart collection was analyzed. Of these, 100 were found to have obvious bony pathologies and/or excessive tooth loss (“pathologic” sample). Thus, the normative sample consisted of 200 individuals (116 males, 84 females). Examination of morphologic features led to the discovery of a distinct angulation of the posterior border of the mandibular ramus at the level of the occlusal surface of the molars in adult males. Flexure appears to be a male developmental trait because it is only manifest consistently after adolescence. In most females, the posterior border of the ramus retained the straight juvenile shape. If flexure was noted, it was found to occur either at a higher point near the neck of the condyle or lower in association with gonial prominence or eversion. In the normative sample, overall prediction accuracy from ramus shape was 99%. When the “pathologic” sample was analyzed separately, 91.0% were correctly diagnosed. Because the African samples were overwhelmingly black, this trait was also tested on American samples (N = 247) of whites (N = 85), Amerinds (N = 66), and blacks (N = 96) that included a mix of healthy individuals and those with extensive tooth loss and evidence of pathology. The results were nearly identical to those of the “pathologic” African sample, with accuracies ranging from about 91% in whites and blacks to over 92% in Amerinds. Total accuracy for all African and American samples combined (N = 547) is 94.2%. In conclusion, at 99%, sexing from the shape of the ramus of a healthy mandible is on a par with accuracy attainable from a complete pelvis. Moreover, there is no record that any other single morphologic or metric indicator of sex (that has been quantified from the adult skeleton) surpasses the overall accuracy attained from the more representative mixed sample produced by combining all groups assessed in this study. The usefulness of this trait is enhanced by the survivability of the mandible and the fact that preliminary investigations show that the trait is clearly evident in fossil hominids. © 1996 Wiley-Liss, Inc.