Stripe or yellow rust (YR) caused by Puccinia striiformis tritici (Pst) is an important foliar disease affecting wheat production globally. Resistant varieties are the most economically and environmentally effective way to manage this disease. The common winter wheat (Triticum aestivum L.) cultivar Luomai 163 exhibited resistance to Pst races CYR32 and CYR33 at the seedling stage and showed a high level adult plant resistance in the field. To understand the genetic basis of YR resistance in this cultivar, 142 F5 recombinant inbred lines (RILs) derived from cross Apav#1 × LM163 and both parents were genotyped with the 16K SNP array and bulked segregant analysis sequencing (BSA-Seq). The analysis detected a major gene, YrLM163, at the seedling stage associated with the 1BL.1RS translocation. Additionally, three genes for resistance at the adult plant stage were detected on chromosome arms 1BL (Lr46/Yr29/Pm39/Sr58), 6BS and 6BL in Luomai 163, whereas Apav#1 contributed resistance at a QTL on 2BL. These QTL explained YR disease severity variations ranging from 6.9 to 54.8%. KASP markers KASP-2BL, KASP-6BS and KASP-6BL for three novel loci QYr.hzau-2BL, QYr.hzau-6BS and QYr.hzau-6BL were developed and validated. QYr.hzau-1BL, QYr.hzau-2BL and QYr.hzau-6BS showed varying degrees of resistance to YR when present individually or in combination based on genotype and phenotype analysis of a panel of 570 wheat accessions. Six RILs combining resistance alleles of all QTL, showing higher resistance to YR in the field than Luomai 163 with disease severities of 10.7-16.0%, are important germplasm resources for breeding programs to develop YR resistant wheat varieties with good agronomic traits.

BACKGROUND: Prenatal fish intake is a key source of omega-3 (ω-3) polyunsaturated fatty acids needed for brain development, yet intake is generally low, and studies addressing associations with autism spectrum disorder (ASD) and related traits are lacking.
OBJECTIVE: This study aimed to examine associations of prenatal fish intake and ω-3 supplement use with both autism diagnosis and broader autism-related traits.
METHODS: Participants were drawn from 32 cohorts in the Environmental influences on Child Health Outcomes Cohort Consortium. Children were born between 1999 and 2019 and part of ongoing follow-up with data available for analysis by August 2022. Exposures included self-reported maternal fish intake and ω-3/fish oil supplement use during pregnancy. Outcome measures included parent report of clinician-diagnosed ASD and parent-reported autism-related traits measured by the Social Responsiveness Scale (SRS)-second edition (n = 3939 and v3609 for fish intake analyses, respectively; n = 4537 and n = 3925 for supplement intake analyses, respectively).
RESULTS: In adjusted regression models, relative to no fish intake, fish intake during pregnancy was associated with reduced odds of autism diagnosis (odds ratio: 0.84; 95% confidence interval [CI]: 0.77, 0.92), and a modest reduction in raw total SRS scores (β: -1.69; 95% CI: -3.3, -0.08). Estimates were similar across categories of fish consumption from \"any\" or \"less than once per week\" to \"more than twice per week.\" For ω-3 supplement use, relative to no use, no significant associations with autism diagnosis were identified, whereas a modest relation with SRS score was suggested (β: 1.98; 95% CI: 0.33, 3.64).
CONCLUSIONS: These results extend previous work by suggesting that prenatal fish intake, but not ω-3 supplement use, may be associated with lower likelihood of both autism diagnosis and related traits. Given the low-fish intake in the United States general population and the rising autism prevalence, these findings suggest the need for better public health messaging regarding guidelines on fish intake for pregnant individuals.

Sexual selection plays a crucial role in modern evolutionary theory, offering valuable insight into evolutionary patterns and species diversity. Recently, a comprehensive definition of sexual selection has been proposed, defining it as any selection that arises from fitness differences associated with nonrandom success in the competition for access to gametes for fertilization. Previous research on discrete traits demonstrated that non-random mating can be effectively quantified using Jeffreys (or symmetrized Kullback-Leibler) divergence, capturing information acquired through mating influenced by mutual mating propensities instead of random occurrences. This novel theoretical framework allows for detecting and assessing the strength of sexual selection and assortative mating. In this study, we aim to achieve two primary objectives. Firstly, we demonstrate the seamless alignment of the previous theoretical development, rooted in information theory and mutual mating propensity, with the aforementioned definition of sexual selection. Secondly, we extend the theory to encompass quantitative traits. Our findings reveal that sexual selection and assortative mating can be quantified effectively for quantitative traits by measuring the information gain relative to the random mating pattern. The connection of the information indices of sexual selection with the classical measures of sexual selection is established. Additionally, if mating traits are normally distributed, the measure capturing the underlying information of assortative mating is a function of the square of the correlation coefficient, taking values within the non-negative real number set [0, +∞). It is worth noting that the same divergence measure captures information acquired through mating for both discrete and quantitative traits. This is interesting as it provides a common context and can help simplify the study of sexual selection patterns.

CONCLUSIONS: 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.
METHODS: 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).

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.