Evidence suggests core autism trait consistency in older children, but development of these traits is variable in early childhood. The Social Responsiveness Scale (SRS) measures autism-related traits and broader autism phenotype, with two age-dependent forms in childhood (preschool, 2.5-4.5 years; school age, 4-18 years). Score consistency has been observed within forms, though reliability across forms has not been evaluated. Using data from the Environmental Influences on Child Health Outcomes (ECHO) program (n = 853), preschool, and school-age SRS scores were collected via maternal report when children were an average of 3.0 and 5.8 years, respectively. We compared reproducibility of SRS total scores (T-scores) and agreement above a clinically meaningful cutoff (T-scores ≥ 60) and examined predictors of discordance in cutoff scores across forms. Participant scores across forms were similar (mean difference: 3.3 points; standard deviation: 7), though preschool scores were on average lower than school-age scores. Most children (88%) were classified below the cutoff on both forms, and overall concordance was high (92%). However, discordance was higher in cohorts following younger siblings of autistic children (16%). Proportions of children with an autism diagnoses were also higher among those with discordant scores (27%) than among those with concordant scores (4%). Our findings indicate SRS scores are broadly reproducible across preschool and school-age forms, particularly for capturing broader, nonclinical traits, but also suggest that greater variability of autism-related traits in preschool-age children may reduce reliability with later school-age scores for those in the clinical range.

UNASSIGNED: Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure, and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer.
UNASSIGNED: tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).
UNASSIGNED: daiki.tagami@hertford.ox.ac.uk.

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.

To enhance sesame yield, it is important to utilize diverse parents in breeding programs and implement an effective selection procedure, which exploits the association of quantitative traits. Therefore, the objective of this experiment was to explore the correlation among quantitative traits and assess genetic variability and diversity using both qualitative and quantitative traits. Correlation coefficients indicated a noteworthy (P < 0.001) positive phenotypic (rp) and genotypic (rg) correlation between seed yield and various traits: plant height (rp = 0.60; rg = 0.58), length of the capsule-bearing zone (rp = 0.77; rg = 0.80), number of capsules on the main stem (rp = 0.80; rg = 0.85), primary branches (rp = 0.66; rg = 0.66), and capsules per plant (rp = 0.90; rg = 0.91). In contrast, a negative correlation (P < 0.001) was observed between yield and bacterial blight disease severity, both phenotypically (rp = -0.60) and genotypically (rg = -0.76). The analysis of path coefficients indicated that the most substantial positive direct effect on yield (0.77) was attributed to capsules per plant, whereas other traits associated with yield exhibited a significant indirect influence on yield through capsules per plant. Qualitative traits exhibited diversity, except for plant growth type, plant growth habit, and stem branching. Shannon-Weaner (H) and Simpson (1-D) diversity indices were higher for interior corolla color (H = 1.63; 1-D = 0.66), seed color (H = 1.50; 1-D = 0.46), and capsule beak type (H = 1.08; 1-D = 0.50). The analysis of variance indicated a notable variation among the examined genotypes regarding quantitative traits, excluding internode length. The plant materials were divided into five clusters through cluster analysis, where clusters I to V consisted of 21, 29, 4, 4, and 6 genotypes, respectively. The current study has shown that the yield of sesame can be enhanced through indirect selection for traits associated with yield, particularly the highest number of capsules per plant. Furthermore, examinations of genetic diversity confirmed the presence of variability within the assessed genotypes, providing valuable insights for upcoming sesame breeding programs.

Induced mutation for the creation of desirable traits through chronic gamma irradiation provides an opportunity for the selection and development of new chili varieties. This study was conducted to assess the effects of different doses of chronic gamma irradiation on morpho-physiological traits in chili. Ten plants from each variety were exposed to different doses of chronic gamma irradiation for 277.02 h at three weeks after germination under gamma greenhouse facilities, with accumulative dose; 185.61Gy, 83.11Gy, 47.096Gy, 30.474Gy, 19.4Gy, 13.9Gy, 11.1Gy, 8.31Gy, 5.54Gy) and 2.77Gy respectively. Highly significant differences were observed among doses (Rings) of chronic gamma irradiation expressed in mean values for all investigated traits. Relatively moderate doses of chronic gamma irradiation represented by doses 47.096 Gy (Ring 4) and 19.40 Gy (Ring 6) resulted in significant stimulation for most of the studied characters. The highest heritability was recorded in days to flowering at 99.88 while the lowest was observed in fruit dry weight at 34.66 %. High genetic advance were recorded for most of the quantitative traits studied. In addition, a highly significant positive correlation was observed between total fruit per plant, total number of fruit per plant, plant height, fruit fresh weight, number of secondary branches, chlorophyll a, fruit dry weight, total chlorophyll content, stem diameter, fruit length and fruit girth. With increasing chronic gamma dose, mutagenic efficiency and efficacy generally increased. Induced variety of desirable features will considerably increase the chilli\'s amelioration through mutation breeding, leading to the development of improved varieties. The results of this research offer valuable information for the use of chronic gamma radiation in the mutations breeding of Capsicum annuum L., which will be advantageous for future breeding programs.

The present study was aimed at evaluating the genetic variation and population structure in a collection of 22 rice genotypes. Twenty-two rice genotypes were assessed using quantitative traits and SSR molecular markers for genetic variability and genetic diversity. As for genetic diversity, the genotypes were clarified based on twelve quantitative traits. Clustering produced two large groups: the IR70423-169-2-2 variety was in a branch alone due to its long duration, while, the second group included all rest of genotypes and was split up into two sub-groups. The first sub-group included IR67418-131-2-3-3-3, IR67420-206-3-1-3-3, Giza181, Giza182, Sakha104, and P1044-86-5-3-3-2M. However, pedigree played in divided clustering with Giza181 and Giza182, which were belonging to the Indica type and produced from the same parents. SSR markers produced 87 alleles, with a mean of 4.3 alleles per locus, which were detected in 22 rice genotypes. A higher number of alleles were found with primers RM262, RM244, RM3843, RM212, and RM3330. With an overall mean of 0.837, the polymorphic information content values were high for all SSR markers, ranging from a low of 0.397 for M254 to a high of 0.837 for RM244. The dendogram was divided into six groups according to the types of genotypes, with the pedigree playing a major role for the genetic distance. In order to help breeders choose parents and create suitable hybrids to achieve genetic improvement in crops, particularly rice, SSR is a useful technique for analysing genotype diversity and aiding in the genetic fingerprinting of each variety.

Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

Modern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for disentangling evolutionary correlations from those arising from hierarchical phylogenetic relationships. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for shared ancestry. However, recent evidence has shown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution-the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt lineage-specific evolutionary shifts, which current regression models are largely ill equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations than the standard least-squares estimator, while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against unreplicated evolution and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

Numerous staple crops exhibit polyploidy and are difficult to genetically modify. However, recent advances in genome sequencing and editing have enabled polyploid genome engineering. The hexaploid black nightshade species Solanum nigrum has immense potential as a beneficial food supplement. We assembled its genome at the scaffold level. After functional annotations, we identified homoeologous gene sets, with similar sequence and expression profiles, based on comparative analyses of orthologous genes with close diploid relatives Solanum americanum and S. lycopersicum. Using CRISPR-Cas9-mediated mutagenesis, we generated various mutation combinations in homoeologous genes. Multiple mutants showed quantitative phenotypic changes based on the genotype, resulting in a broad-spectrum effect on the quantitative traits of hexaploid S. nigrum. Furthermore, we successfully improved the fruit productivity of Boranong, an orphan cultivar of S. nigrum suggesting that engineering homoeologous genes could be useful for agricultural improvement of polyploid crops.