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The scientific landscape surrounding amyotrophic lateral sclerosis (ALS) continues to shift as the number of genes associated with the disease risk and pathogenesis, and the cellular processes involved, continues to grow. Despite decades of intense research and over 50 potentially causative or disease-modifying genes identified, etiology remains unexplained and treatment options remain limited for the majority of ALS patients. Various factors have contributed to the slow progress in understanding and developing therapeutics for this disease. Here, we review the genetic basis of ALS, highlighting factors that have contributed to the elusiveness of genetic heritability. The most commonly mutated ALS-linked genes are reviewed with an emphasis on disease-causing mechanisms. The cellular processes involved in ALS pathogenesis are discussed, with evidence implicating their involvement in ALS summarized. Past and present therapeutic strategies and the benefits and limitations of the model systems available to ALS researchers are discussed with future directions for research that may lead to effective treatment strategies outlined.

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the -value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values ( > 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable ( > 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

Although genome-wide association studies (GWAS) have provided valuable insights into the decoding of the relationships between sequence variation and complex phenotypes, they have explained little heritability. Regional heritability mapping (RHM) provides heritability estimates for genomic segments containing both common and rare allelic effects that individually contribute too little variance to be detected by GWAS. We carried out GWAS and RHM for seven growth, wood and disease resistance traits in a breeding population of 768 Eucalyptus hybrid trees using EuCHIP60K. Total genomic heritabilities accounted for large proportions (64–89%) of pedigree-based trait heritabilities, providing additional evidence that complex traits in eucalypts are controlled by many sequence variants across the frequency spectrum, each with small contributions to the phenotypic variance. RHM detected 26 quantitative trait loci (QTLs) encompassing 2191 single nucleotide polymorphisms (SNPs), whereas GWAS detected 13 single SNP–trait associations. RHM and GWAS QTLs individually explained 5–15% and 4–6% of the genomic heritability, respectively. RHM was superior to GWAS in capturing larger proportions of genomic heritability. Equated to previously mapped QTLs, our results highlighted genomic regions for further examination towards gene discovery. RHM-QTLs bearing a combination of common and rare variants could be useful enhancements to incorporate prior knowledge of the underlying genetic architecture in genomic prediction models.

Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, we analyse high coverage whole-genome sequencing data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants is skewed towards the rare spectrum, and damaging variants are more often rare. We estimate that less than 4.3% of the narrow-sense heritability is expected to be explained by rare variants in our cohort. Using a gene-based approach, we identify Cis -associations for 237 of the proteins, which is slightly more compared to a GWAS ( N  = 213), and we identify 34 associated loci in Trans . Several associations are driven by rare variants, which have larger effects, on average. We therefore conclude that rare variants could be of importance for precision medicine applications, but have a more limited contribution to the missing heritability of complex diseases. Despite the success of genome-wide association studies, much of the genetic contribution to complex traits remains unexplained. Here, the authors identify effects by rare variants on plasma proteins, and estimate the contribution of rare variants to the heritability.

Genomic selection is being used increasingly in plant breeding to accelerate genetic gain per unit time. One of the most important applications of genomic selection in maize breeding is to predict and select the best un-phenotyped lines in bi-parental populations based on genomic estimated breeding values. In the present study, 22 bi-parental tropical maize populations genotyped with low density SNPs were used to evaluate the genomic prediction accuracy ( ) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD), and assess the effect of trait heritability ( ), TPS and MD on estimation. Our results showed that: (1) moderate values were obtained for different trait-environment combinations, when 50% of the total genotypes was used as training population and ~200 SNPs were used for prediction; (2) increased with an increase in , TPS and MD, both correlation and variance analyses showed that is the most important factor and MD is the least important factor on estimation for most of the trait-environment combinations; (3) predictions between pairwise half-sib populations showed that the values for all the six trait-environment combinations were centered around zero, 49% predictions had values above zero; (4) the trend observed in differed with the trend observed in / , and is the square root of heritability of the predicted trait, it indicated that both and / values should be presented in GS study to show the accuracy of genomic selection and the relative accuracy of genomic selection compared with phenotypic selection, respectively. This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs.

We introduce Proteome-Wide Association Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein’s function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.

[This corrects the article DOI: 10.1371/journal.pone.0192948.].

The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivity and atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

Due to its reputation for receiving little rainfall, Jordan's Al-Mafraq region (where the sheep is reared) is susceptible to droughts and water shortages and poor range. Estimating genetic and environmental factors for growth traits in purebred Awassi sheep in Al Mafraq (semi-arid region) was the goal of the study. The study's data came from 3481 lambs born at the National Centre for Agricultural Research's (NARC) Al-Khanasri livestock and rangeland research station in Jordan over a 15-year period (2009–2023). Birth weights (BWT), weaning weights (WWT), and average daily gain from birth to weaning (ADG) were the traits under study. While genetic parameters were estimated assuming the mixed model using the Restricted Maximum Likelihood (REML) method process using the ASReml program, the environmental effects were estimated using the SAS program's General Linear Model (GLM) technique. BWT, WWT, and ADG were significantly impacted by the year of birth, dam weight at lambing, parity, lamb sex, and method of birth; however, the examined growth traits were not significantly impacted by the dam's age (P > 0.05). BWT, WWT, and ADG had heritability estimates of 0.38±0.05, 0.21±0.05, and 0.16±0.05, respectively, and corresponding repeatabilities of 0.41±0.05, 0.39±0.06, and 0.32±0.05. Environmental correlations ranged from negative (-0.20) between BWT and ADG to high (0.98) between WWT and ADG, while phenotypic correlations ranged from negative (-0.011) between BWT and ADG to high (0.98) between WWT and ADG. The estimated genetic correlations among the traits under study were high and strongly positive, ranging from 0.54 between BWT and ADG to 0.99 between WWT and ADG. To sum up, the findings ought to be used to a genetic selection program that aims to enhance Awassi sheep's growth performance. أجريت هذه الدراسة في محطة الخناصري لبحوث الثروة الحيوانية والمراعي التابعة للمركز الوطني للبحوث الزراعية، الأردن للمدة 2023-2009 حيث تم استخدام 3481سجلا لأوزان الحملان عند الولادة ووزنها عند الفطام ولمعدل الزيادة الوزنية اليومية من الولادة الى الفطام وذلك بهدف دراسة تقدير العوامل الوراثية والبيئية لصفات النمو في أغنام العواسي في المفرق (منطقة شبة قاحلة). تم تقدير المعالم الوراثية باستخدام طريقة الاحتمالية القصوى المقيدة (REML) باستخدام برنامج ASReml، كما تم تقدير العوامل البيئية باستخدام النموذج الخطي العام (GLM) باستخدام برنامج SAS. تبين من النتائج بأن لكل من سنة الميلاد ووزن الأم عند الولادة والجنس المولود ونوع الولادة وعدد البطنات تأثيرا معنويا في الصفات النمو المدروسة، ولم يكن لعمر الأم تأثيرا معنويا في أي من صفات النمو. بلغت تقديرات المكافئ الوراثي 0.38 ،0.21  و0.16 لكل من الوزن عند الولادة، وزن عند الفطام ومعدل الزيادة اليومية من الولادة الى الفطام، كما بلغ المعامل التكراري 0.41 ،0.39 ،0.32 و 0.32 للصفات أعلاه وبنفس الترتيب السابق. بلغت تقديرات الارتباطات الوراثية بين الصفات المدروسة موجبة والعالية، حيث تراوحت  0.54بين الوزن عند الولادة ومعدل الزيادة الوزنية اليومية و0.99 بين وزن عند الفطام ومعدل الزيادة الوزنية اليومية ، في حين بلعت تقديرات الارتباطات المظهرية سالبة 0.011- بين وزن عند الولادة ومعدل الزيادة الوزنية اليومية، وعالية 0.98 بين وزن عند الفطام ومعدل الزيادة الوزنية اليومية. أما الارتباطات البيئية فكانت سالبة -0.20 بين وزن عند الولادة ومعدل الزيادة الوزنية اليومية، وعالية 0.98 بين وزن عند الفطام ومعدل الزيادة الوزنية اليومية. باختصار، ينبغي استخدام هذه النتائج في برنامج انتقاء وراثي يهدف إلى تحسين أداء نمو أغنام العواسي.