Perspectives of autistic adolescent girls and women on the determinants of their mental health and social and emotional well-being: A systematic review and thematic synthesis of lived experience

Autistic girls and women experience more mental health difficulties and poorer well-being than their non-autistic peers. Little emphasis has been placed on the perspectives of the girls and women within the literature. This review aims to provide an overview of the factors that impact autistic females’ emotional and social well-being and mental health, as described in self-report qualitative studies. The protocol for the present review was pre-registered on PROSPERO (CRD42020184983), and this article follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PsycInfo, Academic Search Complete and MEDLINE were systematically searched using a pre-defined search string. This yielded 877 unique records, which were systematically screened by two reviewers, resulting in 52 eligible studies. Structured data extraction and quality appraisal were completed. The present review presents the perspectives of 973 autistic females aged 13–70+. Thematic synthesis identified three themes and nine subthemes. Our findings suggest that autistic girls’ and women’s experiences can be conceptualised within a social model, where biological and psychological factors (‘The Autistic Neurotype’) are experienced through the lens of social factors (‘The Neurotypical World’ and ‘Stigma’), together shaping well-being and mental health outcomes. Lay abstract Difficulties with mental health and low levels of well-being are more common among autistic girls and women than non-autistic people, but we do not fully understand why. Research does not focus enough on what autistic girls and women could tell us about this. This review aims to summarise the studies where autistic girls and women explain things that affect their mental health and well-being to help us understand how to prevent these difficulties from developing. Three research databases were searched to find possibly relevant studies. There were 877 studies found, which two researchers screened according to particular criteria. They found 52 studies that could be included in this review. One researcher evaluated the quality of these studies and extracted the key information from them. This review summarises the views of 973 autistic girls and women aged between 13 and 70+. The findings from the 52 studies were analysed, and we found many factors that affect the mental health and well-being of autistic girls and women. These factors fall into two categories: (1) difficulties living in a world not designed for autistic people and (2) the impact of stigma due to being autistic

Regeneration of grassy fynbos near Grahamstown (eastern Cape) after fire

The regeneration of grassy fynbos was studied for a period of 13months after the original vegetation had been totally destroyed by fire. Because the fire was preceded by a very dry spell and succeeded by months of high rainfall, regeneration proved to be rapid and the growth of some of the species was clearly related to rainfall. Species recolonization was very rapid and in a particular order. The classification of the species into regeneration type and phenological groups relates well to other studies on fire regeneration in the fynbos. After 13months over 100 species had returned to the area, the plants showing a progressive increase in height, basal and aerial cover

Genomic selection using random regressions on known and latent environmental covariates

KEY MESSAGE: The integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments. ABSTRACT: This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is [Formula: see text] higher than conventional random regression models for current environments and [Formula: see text] higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00122-022-04186-w

Genomic selection strategies for clonally propagated crops

For genomic selection (GS) in clonal breeding programs to be effective, parents should be selected based on genomic predicted cross-performance unless dominance is negligible. Genomic prediction of cross-performance enables efficient exploitation of the additive and dominance value simultaneously. Here, we compared different GS strategies for clonally propagated crops with diploid (-like) meiotic behavior, using strawberry as an example. We used stochastic simulation to evaluate six combinations of three breeding programs and two parent selection methods. The three breeding programs included (1) a breeding program that introduced GS in the first clonal stage, and (2) two variations of a two-part breeding program with one and three crossing cycles per year, respectively. The two parent selection methods were (1) parent selection based on genomic estimated breeding values (GEBVs) and (2) parent selection based on genomic predicted cross-performance (GPCP). Selection of parents based on GPCP produced faster genetic gain than selection of parents based on GEBVs because it reduced inbreeding when the dominance degree increased. The two-part breeding programs with one and three crossing cycles per year using GPCP always produced the most genetic gain unless dominance was negligible. We conclude that (1) in clonal breeding programs with GS, parents should be selected based on GPCP, and (2) a two-part breeding program with parent selection based on GPCP to rapidly drive population improvement has great potential to improve breeding clonally propagated crops

Maximizing the potential of multi-parental crop populations.

Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations