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BackgroundResearch has begun to explore the effects of parental genetic nurturing on offspring outcomes using polygenic scores (PGSs). However, there are concerns regarding potential biases due to confounding when mediating parental phenotypes are included.MethodsDepression, educational attainment and height PGSs were generated for 2680 biological parent-offspring trios using genome-wide association study (GWAS) meta-analysis summary statistics in a large population study: Generation Scotland. Regression and pathway models were estimated incorporating PGSs for both parents and offspring to explore direct (offspring PGS) and genetic nurturing (parental PGS) effects on psychological distress, educational attainment and height. Genetic nurturing via parental phenotypes were incorporated into the models. To explore sources of bias we conducted simulation analyses of 10,000 trios using combinations of PGS predictive accuracy and accounted variance.ResultsModels incorporating both offspring and parental PGSs suggested positive parental genetic nurturing effects on offspring educational attainment, but not psychological distress or height. In contrast, models additionally incorporating parental phenotypic information suggested positive parent phenotype mediated genetic nurturing effects were at play for all phenotypes explored as well as negative residual genetic nurturing effects for height. 10,000 parent-offspring trio effects (without genetic nurturing effects) were simulated. Simulations demonstrated that models incorporating parent and offspring PGSs resulted in genetic nurturing effects that were unbiased. However, adding parental phenotypes as mediating variables results in biased positive estimates of parent phenotype mediated genetic nurturing effects and negative estimates of residual genetic nurturing effects. Biased effects increased in magnitude as PGS accuracy and accounted variance decreased. These biases were only eliminated when PGSs were simulated to capture the entirety of trait genetic variance.ConclusionResults suggest that in the absence of PGSs that capture all genetic variance, parental phenotypes act as colliders in the same way as heritable environments. Relatively simple models combining parental and offspring PGSs can be used to detect genetic nurturing effects in complex traits. However, our findings suggest alternative methods should be utilised when aiming to identify mediating phenotypes and potentially modifiable parental nurturing effects.

SummaryBackgroundSelf-reported trauma exposure has consistently been found to be a risk factor for Major Depressive Disorder (MDD) and several studies have reported interactions with genetic liability. To date, most studies have examined interaction effects with trauma exposure using genome-wide variants (single nucleotide polymorphisms SNPs) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. We sought to re-examine genome-by-trauma interaction effects using genetic measures utilising all available genotyped data and thus, maximising accounted variance.MethodsMeasures of self-reported depression, neuroticism and trauma exposure for 148 129 participants with whole genome SNP data are available from the UK Biobank study. Here, we used a mixed-model statistical approach utilising genetic, trauma exposure and genome-by-trauma exposure interaction similarity matrices to explore sources of variation in depression and neuroticism.FindingsOur approach estimated the heritability of MDD to be approximately 0·160 [SE 0·016]. Subtypes of self-reported trauma exposure (catastrophic, adult, childhood and full trauma) accounted for a significant proportion of the variance of each trait, ranging from 0·056 [SE 0·013] to 0·176 [SE 0·025]. The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction ranged from 0·074 [SE 0·006] to 0·201 [SE 0·009]. Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in males than in females.InterpretationThis is the first study to utilise an approach combining all genome-wide SNP data when exploring genome-by-trauma interaction effects in MDD and present evidence that interaction effects are influential in depression manifestation. This effect accounts for greater trait variance within males which points to potential differences in depression aetiology between the sexes. The methodology utilised in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.Research In ContextEvidence before this studyWe searched PubMed up to January 30th 2022, with the following terms: (“gene environment interaction” OR “gene environment” OR “genome wide by environment” OR “GWEIS” OR “polygenic environment” OR (“gene” AND “environment”)) AND (“polygenic risk score” OR “polygenic score” OR “genomic relationship matrix” OR “GRM”) AND (“trauma” OR “environmental adversity” OR “stressful life events”) AND (“depression” OR “major depressive disorder” OR “MDD” OR “depressive symptoms”). Date or language restrictions were not applied. We further reviewed the reference lists of identified articles. This search was supplemented by reviewing related articles identified by Google Scholar. We identified 12 relevant articles. Studies to date have not explored genome-by-environment interaction effects in depression using genomic similarity matrices, however, these effects have been explored using individual single nucleotide polymorphisms (SNPs) from genome-wide studies and polygenic scores (PGSs). Some findings suggest genome-by-environment interaction effects increase risk of depression. However, replication attempts have produced either inconsistent or null findings. Taken together, it is evident that findings have failed to provide consistent evidence of substantial interaction effects. These findings may be a result of limited statistical power in analyses due to genome-wide variants and PGSs failing to capture the polygenic nature of depression with sufficient precision.Added value of this studyThis study is the first to explore genome-by-trauma interaction effects on MDD through the estimation of variance components using relationship matrices. Genomic relationship matrices (GRMs) utilise all available genotyped variants, thus, capturing a greater proportion of the trait variance and potentially providing greater power to detect genetic effects in comparison to PGSs. Additional relationship matrices capturing trauma exposure, and genome-by-trauma exposure similarity are computed and included into mixed linear models. We found evidence for substantial genome-by-trauma (including subtypes of trauma) exposure interaction effects on depression manifestation. Estimated genome-by-trauma interaction effects were larger in males than in females.Implications of all the available evidenceOur findings are the first to show substantial genome-by-trauma effects on depression using whole genome methods. These findings highlight that the role of trauma exposure on depression manifestation may be non-additive and different between sexes. Exploring these effects in depth may yield important insight into various mechanisms, which may explain prevalence differences observed between males and females. Future work can build upon the framework we propose to explore genome-by-trauma interaction effects and the underlying molecular sites and mechanisms which are involved in depression manifestation.

Characterisation of genetic diversity in a large number of European pig populations has been undertaken with EC support. The populations sampled included local (rare) breeds, national varieties of the major international breeds, commercial lines and the Chinese Meishan breed. A second phase of the project will sample a further 50 Chinese breeds. Neutral genetic markers (AFLP and microsatellites), with individual or bulk typing, were used and compared.DNA from 59 European pig populations was extracted on samples of about 50 individuals per population. Individuals were typed for 50 microsatellites and for 148 AFLP bands. A subset of 25 populations was typed for 20 microsatellites on pools of DNA. Allele frequencies were estimated by direct allele counting for the co-dominant markers. Frequencies of AFLP negative alleles (absent bands) were obtained by taking the square root of absent band frequencies. Within-breed variability was summarised using standard statistics: expected and observed heterozygosity, mean observed and effective numbers of alleles, and F statistics. Between-breed diversity analysis was based on a bootstrapped Neighbor-Joining (NJ) tree derived from Reynolds distances (DR). The standard distance of Nei (DS) was also calculated.

Methods that detect QTL within commercial populations circumvent the need for expensive experimental populations and facilitate direct application of results through marker assisted selection. Variance component analysis (VCA) uses phenotypic, pedigree and marker information within a mixed linear model to simultaneously detect QTL and estimate breeding values. The inclusion of non-additive effects has potential for greater accuracy of selection and understanding of underlying mechanisms. The linear model can be extended to include higher order effects such as dominance, however, there is little information on empirical power. Here VCA was applied to real and simulated commercial broiler data to detect additive and dominant QTL effects.

The heritable components of many traits in which livestock producers strive for improvement comprise a number of genetic loci. Many workers have now reported the identification of such quantitative trait loci (QTL) using model crosses between divergent breeds or lines in combination with whole genome scans using mapped DNA markers. Such methods rely on two or three generation pedigrees in order to be able to trace the inheritance of markers and the QTL. However, it is not yet clear if such QTL have application in commercial pig populations. The AFLP™ technology is a very efficient method for identifying markers segregating in any population. We have previously reported the successful use of the AFLP™technology and bulk segregant analysis (BSA) for the detection of markers associated with pig coat colour, a monogenic trait (Plastow et al 1998). We set out to determine whether these methods could be used for the detection of loci associated with quantitative traits directly in commercial populations.

DNA-based markers and genetic maps of major livestock species have been developed in the last few years. These, together with experimental populations and appropriate statistical methods, provide the tools to dissect causes of economically important genetic variation in livestock. A cross between genetically diverse lines is a powerful experimental design for such a study. In an F2or backcross population from such a cross, markers are likely to be highly informative and the genes that control the differences between the lines will be segregating. We have been developing such a resource population based on a cross between the British Large White and Chinese Meishan pigs. These breeds differ for many traits, with the Meishan being inferior for growth rate and fatness, but superior for reproduction traits. Here we report the mapping of major genetic effects on growth rate and fatness in our cross.

With the rapid development of genome maps of livestock, many genetic markers are becoming available. These markers usually have no affect on animals in their own right, their value is that they allow the inheritance of sections of chromosome to be followed in suitable pedigrees. How then may genetic markers be used in livestock breeding? It is useful to distinguish the use of markers in selection programmes within breeds and crossbreeding programmes. Furthermore, for both types of breeding programmes there are different kinds of information that can be provided by markers. Genetic markers may be linked to major genes, or in some cases we may have a direct test for a mutation which causes a major difference in animal performance or phenotype. Alternatively, we may have markers which are linked to (unknown) quantitative trait loci (QTLs), i.e. loci which influence traits such as growth, milk yield, carcass quality and litter size.

Naturally occurring genetic variation is the basis for differences in performance and appearance between and within different breeds and lines of livestock. In a few instances (e.g. coat colour, polling) the genes (or loci) which control the variation between animals and breeds have a large enough effect to be individually recognisable. For many traits, however, the combined effects of many different genes act together to control quantitative differences between breeds and individuals within breeds (hence such genes are often referred to as quantitative trait loci or QTLs). Thus the dramatic successes of modern breeding result from generations of selection which has produced accumulated changes at a number of different loci. The genome contains up to 100,000 different genes and identifying those which contribute to variation in traits of interest is a difficult task. One first step is to identify regions of the genome containing loci of potential interest through their linkage to genetic markers.

Indirect selection of traits of no intrinsic economic value, which are genetically correlated with economically valuable traits, may have an important contribution to make to improvement in overall economic efficiency. Indirect selection is potentially particularly valuable for traits which are age-limited or sex-limited in their expression, such as litter-size and milk yield. For example, in some cases use of an indirect trait in males could double the rate of response to selection for litter size. Indirect selection could make practicable improvements in traits which are difficult or expensive to measure, such as carcass composition or stress and disease resistance. Possible indirect traits require thorough research to define the optimum way in which to use them, and to enable prediction of the effects of their use on all components of overall merit. A good general strategy to fulfil these requirements is to produce selection lines for the major components of economic merit, in which potential indirect traits can be identified and evaluated.

The major components of economic performance in pigs - growth rate, feed conversion ratio and carcass merit - have been the targets of selection for a number of years in the closed herds of breeding companies. Over the past 20 years substantial progress has been made for these traits. Declining fatness in closed herds may lead to a decline in the genetic variation for growth and carcass traits, and thus reduced opportunities for selection. This could arise either as genetic variation is exhausted in closed lines, or as physiological selection limits are approached. In this study genetic variation in closed lines with a history of selection for growth and carcass traits was investigated.The data were collected from two closed lines between 1979 and 1985 by the Cotswold Pig Development Company Ltd. The two lines were housed on a single farm in the same environment and had a history of selection based upon an index of growth rate and fat depths. There were 167 sires with 4722 male and 5386 female progeny in line A, a synthetic breed based largely upon the Landrace breed, and 164 sires with 4160 male and 3887 female progeny in line B, derived from the Large White breed.

International audience; Vertical profiles of NO2 and NO have been obtained from solar occultation measurements by the Atmospheric Chemistry Experiment (ACE), using an infrared Fourier Transform Spectrometer (ACE-FTS) and (for NO2) an ultraviolet-visible-near-infrared spectrometer, MAESTRO (Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation). In this paper, the quality of the ACE-FTS version 2.2 NO2 and NO and the MAESTRO version 1.2 NO2 data are assessed using other solar occultation measurements (HALOE, SAGE II, SAGE III, POAM III, SCIAMACHY), stellar occultation measurements (GOMOS), limb measurements (MIPAS, OSIRIS), nadir measurements (SCIAMACHY), balloon-borne measurements (SPIRALE, SAOZ) and ground-based measurements (UV-VIS, FTIR). Time differences between the comparison measurements were reduced using either a tight coincidence criterion, or where possible, chemical box models. ACE-FTS NO2 and NO and the MAESTRO NO2 are generally consistent with the correlative data. The ACE-FTS and MAESTRO NO2 volume mixing ratio (VMR) profiles agree with the profiles from other satellite data sets to within about 20% between 25 and 40 km, with the exception of MIPAS ESA (for ACE-FTS) and SAGE II (for ACE-FTS (sunrise) and MAESTRO) and suggest a negative bias between 23 and 40 km of about 10%. MAESTRO reports larger VMR values than the ACE-FTS. In comparisons with HALOE, ACE-FTS NO VMRs typically (on average) agree to ±8% from 22 to 64 km and to +10% from 93 to 105 km, with maxima of 21% and 36%, respectively. Partial column comparisons for NO2 show that there is quite good agreement between the ACE instruments and the FTIRs, with a mean difference of +7.3% for ACE-FTS and +12.8% for MAESTRO.