Overview of Statistical Methods for Genome-Wide Association Studies (GWAS)

The impact of PTSD symptoms on suicide ideation in time of terror and war: A nationwide prospective study on the moderating role of loneliness

The terrorist attack of October 7, 2023, and its accompanying war have increased the risk for posttraumatic stress symptoms (PTSS) and suicide ideation (SI). In this national prospective cohort study, we examined the extent to which a sense of loneliness moderates the association between PTSS and SI in the wake of the October 7th attack and the Israel-Hamas war. A representative sample of 710 Israeli adults (362 female, 51.1 %) aged 18-85 (M = 41.01, SD = 13.72) participated in a longitudinal study assessing depression, current SI, and loneliness at two time points: T1, one month before the attack (August 2023) and T2 (November 2023), one month after the attack. We found two significant interactions in which a sense of loneliness at T2 moderated the link between both PTSS at T1 and T2 and current SI at T2. Specifically, the level of PTSS contributed to current SI at T2 more strongly among individuals reporting higher loneliness levels than those reporting low loneliness levels. Clinicians treating individuals coping with high PTSS levels should attend to their patients' sense of loneliness, as it comprises a significant risk factor for current SI and may be considered an important target in treatment.

Understanding natural genetic variation for nutritional quality in grain and identification of superior haplotypes in deepwater rice genotypes of Assam, India

Rice grown under deepwater ecosystem is considered to be natural farming and hence they are considered to be input efficient. Thus, to identify gene responsible for nutritional content under natural conditions, a genome-wide association study (GWAS)was performed. GWAS identified single nucleotide polymorphisms (SNPs) significantly associated with various nutritional quality traits such as Zn (mg/kg), Fe (mg/kg), Protein (%), Oil (%), Amylose (%), Starch (%), Phytic acid (%), Phenol (%) and TDF (%) in 184 deepwater rice accessions evaluated over 2 consecutive years. A total of 278 SNPs distributed across 12 chromosomes were found to be significantly associated with Zn, Oil and Phenol content. Among them, eight high confidence SNPs were significant and identified on chr1 (AX-95933712), chr7 (AX-95957036), and chr8 (AX-95965181) for Zn content. Similarly, on chr2 (AX-95945186), chr8 (AX-95964718), and chr11 (AX-95961099) have been found to be associated with Oil content and for, on chr3 (AX-95922121) and chr4 (AX-95963889) for Phenol content. Genomic regions of ± 220 kb flanking the three consistent lowest p value containing SNPs for each trait were considered for finding superior haplotypes. These SNPs showed significant phenotypic variations with different identified haplotype blocks. The allelic variations with phenotypes were considered to be superior haplotypes i.e., Block 1: Hap 1 (ACCC) for high Zn content, Block 2: Hap 1 (CT) for high Oil content, and Block 2: Hap 1(CGGG) for low Phenol content. The discovered superior haplotype with high nutritional content could be important for understanding the mechanisms involving nutrient use efficiency. Thus, the present study demonstrated that developing rice varieties with appropriate nutritional quality traits will be possible through the incorporation of such superior haplotypes in breeding programs.

Development and application of a 1K functional liquid chip for lactation performance in Bactrian camels

Introduction

The advancement of high-throughput, high-quality, flexible, and cost-effective genotyping platforms is crucial for the progress of dairy breeding in Bactrian camels. This study focuses on developing and evaluating a 1K functional liquid single nucleotide polymorphism (SNP) array specifically designed for milk performance in Bactrian camels.

Methods

We utilized RNA sequencing data from 125 lactating camels to identify and select 1,002 loci associated with milk production traits for inclusion in the SNP array. The array's performance was then assessed using 24 randomly selected camels. Additionally, the array was employed to genotype 398 individuals, which allowed for population validation to assess the polymorphism of SNP sites.

Results

The SNP array demonstrated high overall SNP call rates (> 99%) and a remarkable 100% consistency in genotyping. Population validation results indicate that camels from six breeding areas in Northwest China share a similar genetic background regarding lactation functionality.

Discussion

This study highlights the potential of the SNP array to accelerate the breeding process of lactating Bactrian camels and provides a robust technical foundation for improving lactation performance.

Advancing personalized medicine in neurodegenerative diseases: The role of epigenetics and pharmacoepigenomics in pharmacotherapy

About 80 % of brain disorders have a genetic basis. The pathogenesis of most neurodegenerative diseases is associated with a myriad of genetic defects, epigenetic alterations (DNA methylation, histone/chromatin remodeling, miRNA dysregulation), and environmental factors. The emergence of new sequencing technologies and tools to study the epigenome has led to identifying predictive biomarkers for earlier diagnosis, opening up the possibility of prophylactical interventions. As a result, advances in pharmacogenetics and pharmacoepigenomics now allow for personalized treatments based on the profile of each patient and the specific genetic and epigenetic mechanisms involved. This Review highlights the complexity of neurodegenerative diseases and the variability in patient responses to pharmacotherapy, emphasizing the influence of genetic polymorphisms on the pharmacokinetics and pharmacodynamics of drugs used to treat those conditions. We specifically discuss the potential modulatory effect of several genetic polymorphisms associated with an increased risk of developing different neurodegenerative diseases. We explore genetic and genomic technologies and the potential of analyzing individual-specific drug metabolism to predict and influence drug response and associated clinical outcomes. We also provide insights into the mechanism of action of the drugs under investigation and their potential impact on disease-modifying pathways. Finally, the Review underscores the great potential of this field to enhance the effectiveness and safety of drug treatments through personalized medicine.

The moderating role of belongingness in the contribution of depression to suicide ideation following the October 7, 2023, terrorist attack in Israel: A nationwide prospective study

INTRODUCTION

With >1300 civilians murdered, the terrorist attack of October 7 is one of the deadliest terrorist attacks in modern history. Previous research documented a sharp increase in depression in the aftermath of the attacks and the military conflict that followed. In this national prospective cohort study, we examined to what extent perceived belongingness (PB) moderates the association between depression and suicide ideation (SI) in the wake of the October 7th terrorist attack.

METHODS

A representative sample of 710 Israeli adults (of them, 362 females, 51.1 %), Jews (557, 79.9 %), and Arabs (153, 20.1 %), aged 18-85 (M = 41.01, SD = 13.72) completed questionnaires assessing depression, current SI, and perceived belongingness at two timepoints: T1 (in August 2023) and T2 (in November 2023).

RESULTS

Perceived belongingness at T1 predicted SI at T2 beyond demographic and trauma-related characteristics. Importantly, we found a significant interaction in which a PB at T1 moderated the link between depression and current SI at T2. Specifically, the level of depression at T2 contributed to current SI-T2 more strongly for individuals with low PB levels than for individuals with high PB levels.

DISCUSSION

Our study highlights the impact of PB on SI following the October 7th terrorist attack. Clinicians treating individuals coping with depression should attend to their patients' sense of belongingness, as low PB comprises a significant risk factor for current SI. Moreover, community and national initiatives that could increase levels of PB among the citizens may help to diminish suicide risk in the aftermath of the attack.

Causal relationship of interferon-γ and interleukin-18 upstream of intervertebral disc degeneration pathogenesis: a two-sample Mendelian randomization study

Introduction

Intervertebral disc degeneration (IVDD) is a complex disease caused by genetic and environmental factors, but its pathogenesis is still unclear. Although studies of inflammatory cytokines have been used in recent years to unravel the biological mechanisms of a variety of diseases, such analyses have not yet been applied to IVDD. Therefore, we used a Mendelian Randomization approach to explore the potential mechanisms underlying the pathogenesis of IVDD.

Methods

We obtained GWAS data from publicly available databases for inflammatory cytokines and IVDD, respectively, and explored the causal relationship between individual inflammatory cytokines and IVDD using instrumental variable (IV) analysis. We primarily used IVW methods to assess causality, while sensitivity, heterogeneity and multidirectionality analyses were performed for positive results (p < 0.05). All analyses were performed using R software.

Results

In our study, we performed a two-sample MR analysis of 41 inflammatory cytokines to identify metabolites causally associated with IVDD. Ultimately, 2 serum metabolites associated with IVDD were identified (pval<0.05), IFN-γ and IL-18. sensitivity, heterogeneity, and Pleiotropy test analyses were performed for all results.

Conclusion

Our study identified a causal relationship between IFN-γ and IL-18 and IVDD. It is valuable for the monitoring and prevention of IVDD and the exploration of targeted drugs. However, more evidence is needed to validate our study.

Immune cell signatures and causal association with irritable bowel syndrome: A mendelian randomization study

BACKGROUND

The mucosal barrier's immune-brain interactions, pivotal for neural development and function, are increasingly recognized for their potential causal and therapeutic relevance to irritable bowel syndrome (IBS). Prior studies linking immune inflammation with IBS have been inconsistent. To further elucidate this relationship, we conducted a Mendelian randomization (MR) analysis of 731 immune cell markers to dissect the influence of various immune phenotypes on IBS. Our goal was to deepen our understanding of the disrupted brain-gut axis in IBS and to identify novel therapeutic targets.

AIM

To leverage publicly available data to perform MR analysis on 731 immune cell markers and explore their impact on IBS. We aimed to uncover immunophenotypic associations with IBS that could inform future drug development and therapeutic strategies.

METHODS

We performed a comprehensive two-sample MR analysis to evaluate the causal relationship between immune cell markers and IBS. By utilizing genetic data from public databases, we examined the causal associations between 731 immune cell markers, encompassing median fluorescence intensity, relative cell abundance, absolute cell count, and morphological parameters, with IBS susceptibility. Sensitivity analyses were conducted to validate our findings and address potential heterogeneity and pleiotropy.

RESULTS

Bidirectional false discovery rate correction indicated no significant influence of IBS on immunophenotypes. However, our analysis revealed a causal impact of IBS on 30 out of 731 immune phenotypes (P < 0.05). Nine immune phenotypes demonstrated a protective effect against IBS [inverse variance weighting (IVW) < 0.05, odd ratio (OR) < 1], while 21 others were associated with an increased risk of IBS onset (IVW ≥ 0.05, OR ≥ 1).

CONCLUSION

Our findings underscore a substantial genetic correlation between immune cell phenotypes and IBS, providing valuable insights into the pathophysiology of the condition. These results pave the way for the development of more precise biomarkers and targeted therapies for IBS. Furthermore, this research enriches our comprehension of immune cell roles in IBS pathogenesis, offering a foundation for more effective, personalized treatment approaches. These advancements hold promise for improving IBS patient quality of life and reducing the disease burden on individuals and their families.

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

Genomic regions, candidate genes, and pleiotropic variants associated with physiological and anatomical indicators of heat stress response in lactating sows

BACKGROUND

Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses.

RESULTS

A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively.

CONCLUSIONS

Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.

Human genetic determinants of COVID-19 in Brazil: challenges and future plans

COVID-19 pandemic represented a worldwide major challenge in different areas, and efforts undertaken by the scientific community led to the understanding of some of the genetic determinants that influence the different COVID-19 outcomes. In this paper, we review the studies about the role of human genetics in COVID-19 severity and how Brazilian studies also contributed to those findings. Rare variants in genes related to Inborn Errors of Immunity (IEI) in the type I interferons pathway, and its phenocopies, have been described as being causative of severe outcomes. IEI and its phenocopies are present in Brazil, not only in COVID-19 patients, but also in autoimmune conditions and severe reactions to yellow fever vaccine. In addition, studies focusing on common variants and GWAS studies encompassing worldwide patients have found several loci associated with COVID-19 severity. A GWAS study including only Brazilian COVID-19 patients identified a new locus 1q32.1 associated with COVID-19 severity. Thus, more comprehensive studies considering the Brazilian genomic diversity should be performed, since they can help to reveal not only what are the genetic determinants that contribute to the different outcomes for COVID-19 in the Brazilian population, but in the understanding of human genetics in different health conditions.

Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis

Background

Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory.

Methods

We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants).

Findings

The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years.

Interpretation

Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making.

Funding

The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.

Mapping and Functional Analysis of QTL for Kernel Number per Row in Tropical and Temperate-Tropical Introgression Lines of Maize (Zea mays L.)

Kernel number per row (KNR) is an essential component of maize (Zea mays L.) grain yield (GY), and understanding its genetic mechanism is crucial to improve GY. In this study, two F₇ recombinant inbred line (RIL) populations were created using a temperate-tropical introgression line TML418 and a tropical inbred line CML312 as female parents and a backbone maize inbred line Ye107 as the common male parent. Bi-parental quantitative trait locus (QTL) mapping and genome-wide association analysis (GWAS) were then performed on 399 lines of the two maize RIL populations for KNR in two different environments using 4118 validated single nucleotide polymorphism (SNP) markers. This study aimed to: (1) detect molecular markers and/or the genomic regions associated with KNR; (2) identify the candidate genes controlling KNR; and (3) analyze whether the candidate genes are useful in improving GY. The authors reported a total of 7 QTLs tightly linked to KNR through bi-parental QTL mapping and identified 21 SNPs significantly associated with KNR through GWAS. Among these, a highly confident locus qKNR7-1 was detected at two locations, Dehong and Baoshan, with both mapping approaches. At this locus, three novel candidate genes (Zm00001d022202, Zm00001d022168, Zm00001d022169) were identified to be associated with KNR. These candidate genes were primarily involved in the processes related to compound metabolism, biosynthesis, protein modification, degradation, and denaturation, all of which were related to the inflorescence development affecting KNR. These three candidate genes were not reported previously and are considered new candidate genes for KNR. The progeny of the hybrid Ye107 × TML418 exhibited strong heterosis for KNR, which the authors believe might be related to qKNR7-1. This study provides a theoretical foundation for future research on the genetic mechanism underlying KNR in maize and the use of heterotic patterns to develop high-yielding hybrids.

Molecular markers in tea plant (Camellia sinensis): Applications to evolution, genetic identification, and molecular breeding

Tea plants have a long cultivation history in the world, and the beverage (tea) made from its leaves is well known in the world. Due to the characteristics of self-incompatibility, long-term natural and artificial hybridization, tea plants have a very complex genetic background, which make the classification of tea plants unclear. Molecular marker, one type of genetic markers, has the advantages of stable inheritance, large amount of information, and high reliability. The development of molecular marker has facilitated the understanding of complex tea germplasm resources. So far, molecular markers had played important roles in the study of the origin and evolution, the preservation and identification of tea germplasms, and the excellent cultivars breeding of tea plants. However, the information is scattered, making it difficult to understand the advance of molecular markers in tea plants. In this paper, we summarized the development process and types of molecular markers in tea plants. In addition, the application advance of these molecular markers in tea plants was reviewed. Perspectives of molecular markers in tea plants were also systematically provided and discussed. The elaboration of molecular markers in this paper should help us to renew understanding of its application in tea plants.

Using multi-tissue transcriptome-wide association study to identify candidate susceptibility genes for respiratory infectious diseases

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms.

Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella.

Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p &lt; 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p &lt; 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p &lt; 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p &lt; 0.05.

Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

A genomic assessment of the correlation between milk production traits and claw and udder health traits in Holstein dairy cattle

Claw diseases and mastitis represent the most important disease traits in dairy cattle with increasing incidences and a frequently mentioned connection to milk yield. Yet, many studies aimed to detect the genetic background of both trait complexes via fine-mapping of quantitative trait loci. However, little is known about genomic regions that simultaneously affect milk production and disease traits. For this purpose, several tools to detect local genetic correlations have been developed. In this study, we attempted a detailed analysis of milk production and disease traits as well as their interrelationship using a sample of 34,497 50K genotyped German Holstein cows with milk production and claw and udder disease traits records. We performed a pedigree-based quantitative genetic analysis to estimate heritabilities and genetic correlations. Additionally, we generated GWAS summary statistics, paying special attention to genomic inflation, and used these data to identify shared genomic regions, which affect various trait combinations. The heritability on the liability scale of the disease traits was low, between 0.02 for laminitis and 0.19 for interdigital hyperplasia. The heritabilities for milk production traits were higher (between 0.27 for milk energy yield and 0.48 for fat-protein ratio). Global genetic correlations indicate the shared genetic effect between milk production and disease traits on a whole genome level. Most of these estimates were not significantly different from zero, only mastitis showed a positive one to milk (0.18) and milk energy yield (0.13), as well as a negative one to fat-protein ratio (-0.07). The genomic analysis revealed significant SNPs for milk production traits that were enriched on Bos taurus autosome 5, 6, and 14. For digital dermatitis, we found significant hits, predominantly on Bos taurus autosome 5, 10, 22, and 23, whereas we did not find significantly trait-associated SNPs for the other disease traits. Our results confirm the known genetic background of disease and milk production traits. We further detected 13 regions that harbor strong concordant effects on a trait combination of milk production and disease traits. This detailed investigation of genetic correlations reveals additional knowledge about the localization of regions with shared genetic effects on these trait complexes, which in turn enables a better understanding of the underlying biological pathways and putatively the utilization for a more precise design of breeding schemes.

Metabolic perspective of astrocyte dysfunction in Alzheimer's disease and type 2 diabetes brains

The term type III diabetes (T3DM) has been proposed for Alzheimer's disease (AD) due to the shared molecular and cellular features between type 2 diabetes (T2DM) and insulin resistance-associated memory deficits and cognitive decline in elderly individuals. Astrocytes elicit neuroprotective or deleterious effects in AD progression and severity. Patients with T2DM are at a high risk of cognitive impairment, and targeting astrocytes might be promising in alleviating neurodegeneration in the diabetic brain. Recent studies focusing on cell-specific activities in the brain have revealed the important role of astrocytes in brain metabolism (e.g., glucose metabolism, lipid metabolism), neurovascular coupling, synapses, and synaptic plasticity. In this review, we discuss how astrocytes and their dysfunction result in multiple pathological and clinical features of AD and T2DM from a metabolic perspective and the potential comorbid mechanism in these two diseases from the perspective of astrocytes.

Detection of Candidate Genes Associated with Fecundity through Genome-Wide Selection Signatures of Katahdin Ewes

One of the strategies to genetically improve reproductive traits, despite their low inheritability, has been the identification of candidate genes. Therefore, the objective of this study was to detect candidate genes associated with fecundity through the fixation index (F_ST) and runs of homozygosity (ROH) of selection signatures in Katahdin ewes. Productive and reproductive records from three years were used and the genotypes (OvineSNP50K) of 48 Katahdin ewes. Two groups of ewes were identified to carry out the genetic comparison: with high fecundity (1.3 ± 0.03) and with low fecundity (1.1 ± 0.06). This study shows for the first time evidence of the influence of the CNOT11, GLUD1, GRID1, MAPK8, and CCL28 genes in the fecundity of Katahdin ewes; in addition, new candidate genes were detected for fecundity that were not reported previously in ewes but that were detected for other species: ANK2 (sow), ARHGAP22 (cow and buffalo cow), GHITM (cow), HERC6 (cow), DPF2 (cow), and TRNAC-GCA (buffalo cow, bull). These new candidate genes in ewes seem to have a high expression in reproduction. Therefore, future studies are needed focused on describing the physiological basis of changes in the reproductive behavior influenced by these genes.

Genome-wide association study of yield-related traits in common wheat (Triticum aestivum L.) under normal and drought treatment conditions

The primary goal of modern wheat breeding is to develop new high-yielding and widely adaptable varieties. We analyzed four yield-related agronomic traits in 502 wheat accessions under normal conditions (NC) and drought treatment (DT) conditions over three years. The genome-wide association analysis identified 51 yield-related and nine drought-resistance-related QTL, including 13 for the thousand-grain weight (TGW), 30 for grain length (GL), three for grain width (GW), five for spike length (SL) and nine for stress tolerance index (STI) QTL in wheat. These QTL, containing 72 single nucleotide polymorphisms (SNPs), explained 2.23 - 7.35% of the phenotypic variation across multiple environments. Eight stable SNPs on chromosomes 2A, 2D, 3B, 4A, 5B, 5D, and 7D were associated with phenotypic stability under NC and DT conditions. Two of these stable SNPs had association with TGW and STI. Several novel QTL for TGW, GL and SL were identified on different chromosomes. Three linked SNPs were transformed into kompetitive allele-specific PCR (KASP) markers. These results will facilitate the discovery of promising SNPs for yield-related traits and/or drought stress tolerance and will accelerate the development of new wheat varieties with desirable alleles.

Genome-wide association study of traits in sacred lotus uncovers MITE-associated variants underlying stamen petaloid and petal number variations

Understanding the genetic variants responsible for floral trait diversity is important for the molecular breeding of ornamental flowers. Widely used in water gardening for thousands of years, the sacred lotus exhibits a wide range of diversity in floral organs. Nevertheless, the genetic variations underlying various morphological characteristics in lotus remain largely unclear. Here, we performed a genome-wide association study of sacred lotus for 12 well-recorded ornamental traits. Given a moderate linkage disequilibrium level of 32.9 kb, we successfully identified 149 candidate genes responsible for seven flower traits and plant size variations, including many pleiotropic genes affecting multiple floral-organ-related traits, such as NnKUP2. Notably, we found a 2.75-kb presence-and-absence genomic fragment significantly associated with stamen petaloid and petal number variations, which was further confirmed by re-examining another independent population dataset with petal number records. Intriguingly, this fragment carries MITE transposons bound by siRNAs and is related to the expression differentiation of a nearby candidate gene between few-petalled and double-petalled lotuses. Overall, these genetic variations and candidate genes responsible for diverse lotus traits revealed by our GWAS highlight the role of transposon variations, particularly MITEs, in shaping floral trait diversity.

Transcriptome-wide association study identifies PSMB9 as a susceptibility gene for coal workers' pneumoconiosis

Coal workers' pneumoconiosis (CWP) is a type of typical occupational lung disease caused by prolonged inhalation of coal mine dust. The individuals' different genetic background may underlie their different susceptibility to develop pneumoconiosis, even under the same exposure level. This study aimed to identify susceptibility genes associated with CWP. Based on our previous genome-wide association study (GWAS, 202 CWP cases vs. 198 controls) and gene expression data obtained by analyzing human lungs and whole blood from the Genotype-Tissue Expression (GTEx) Portal, a transcriptome-wide association study (TWAS) was applied to identify CWP risk-related genes. Luciferase report gene assay, qRT-PCR, Western blot, immunofluorescence assay, and TUNEL assay were conducted to explore the potential role of the candidate gene in CWP. Proteasome 20S subunit beta 9 (PSMB9) was identified as a strong risk-related gene of CWP in both lungs and whole blood (Lungs: P_TWAS  = 4.22 × 10^-4 ; Whole blood: P_TWAS  = 2.11 × 10^-4 ). Single nucleotide polymorphisms (SNPs) rs2071480 and rs1351383, which locate in the promoter region and the first intron of the PSMB9 gene, were in high linkage disequilibrium (LD, r²  = 0.98) with the best GWAS SNP rs4713600 (G>T, OR = 0.55, 95% CI: 0.42-0.74, P = 6.86 × 10^-5 ). Both rs2071480 and rs1351383 significantly enhanced the transcriptional activity of PSMB9. Functional experiments revealed that silica exposure remarkably reduced the PSMB9 expression and caused cell apoptosis, while overexpression of PSMB9 markedly abolished silica-induced cell apoptosis. We here identified PSMB9 as a novel susceptibility gene for CWP and provided important insights into the further exploration of the CWP pathogenesis.

A genome-wide screen for resilient responses in growing pigs

Background

There is a growing interest to decipher the genetic background of resilience and its possible improvement through selective breeding. The objective of the present study was to provide new insights into the genetic make-up of resilience in growing pigs by identifying genomic regions and candidate genes associated with resilience indicators. Commercial Duroc pigs were challenged with an attenuated Aujeszky vaccine at 12 weeks of age. Two resilience indicators were used: deviation from the expected body weight at 16 weeks of age given the growth curve of non-vaccinated pigs (∆BW) and the increase in acute-phase protein haptoglobin at four days post-vaccination (∆HP). Genome-wide association analyses were carried out on 445 pigs, using genotypes at 41,165 single nucleotide polymorphisms (SNPs) and single-marker and Bayesian multiple-marker regression approaches.

Results

Genomic regions on pig chromosomes 2, 8, 9, 11 (∆BW) and 8, 9, 13 (∆HP) were found to be associated with the resilience indicators and explained high proportions of their genetic variance. The genomic regions that were associated explained 27 and 5% of the genetic variance of ∆BW and ∆HP, respectively. These genomic regions harbour promising candidate genes that are involved in pathways related to immune response, response to stress, or signal transduction (CD6, PTGDR2, IKZF1, RNASEL and MYD88), and growth (GRB10 and LCORL).

Conclusions

Our study identified novel genomic regions that are associated with two resilience indicators (∆BW and ∆HP) in pigs. These associated genomic regions harbour potential candidate genes involved in immune response and growth pathways, which emphasise the strong relationship between resilience and immune response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12711-022-00739-1.

Genetics and Epigenetics of Spontaneous Intracerebral Hemorrhage

Intracerebral hemorrhage (ICH) is a complex and heterogeneous disease, and there is no effective treatment. Spontaneous ICH represents the final manifestation of different types of cerebral small vessel disease, usually categorized as: lobar (mostly related to cerebral amyloid angiopathy) and nonlobar (hypertension-related vasculopathy) ICH. Accurate phenotyping aims to reflect these biological differences in the underlying mechanisms and has been demonstrated to be crucial to the success of genetic studies in this field. This review summarizes how current knowledge on genetics and epigenetics of this devastating stroke subtype are contributing to improve the understanding of ICH pathophysiology and their potential role in developing therapeutic strategies.

A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping

Motivation: Identifying new genetic associations in non-Mendelian complex diseases is an increasingly difficult challenge. These diseases sometimes appear to have a significant component of heritability requiring explanation, and this missing heritability may be due to the existence of subtypes involving different genetic factors. Taking genetic information into account in clinical trials might potentially have a role in guiding the process of subtyping a complex disease. Most methods dealing with multiple sources of information rely on data transformation, and in disease subtyping, the two main strategies used are 1) the clustering of clinical data followed by posterior genetic analysis and 2) the concomitant clustering of clinical and genetic variables. Both of these strategies have limitations that we propose to address. Contribution: This work proposes an original method for disease subtyping on the basis of both longitudinal clinical variables and high-dimensional genetic markers via a sparse mixture-of-regressions model. The added value of our approach lies in its interpretability in relation to two aspects. First, our model links both clinical and genetic data with regard to their initial nature (i.e., without transformation) and does not require post-processing where the original information is accessed a second time to interpret the subtypes. Second, it can address large-scale problems because of a variable selection step that is used to discard genetic variables that may not be relevant for subtyping. Results: The proposed method was validated on simulations. A dataset from a cohort of Parkinson's disease patients was also analyzed. Several subtypes of the disease and genetic variants that potentially have a role in this typology were identified. Software availability: The R code for the proposed method, named DiSuGen, and a tutorial are available for download (see the references).

[Genetic aspects involved in asthma]

Asthma is an etiologically heterogeneous disease resulting from a complex interaction between genetic. The genetic aspects involved in asthma, which were analyzed from the perspective of the traditional model of multifactorial inheritance, were susceptibility, host factors, and environmental exposures. In the present paper, studies on their family aggregation, concordance in twins, and heritability were analized; as well as the current knowledge about candidate genes, genome wide association studies, and epigenomics contributions and other omic studies that have increased our knowledge about their pathophysiology and environmental interactions.

Identification of Genetic Factors Controlling the Formation of Multiple Flowers Per Node in Pepper (Capsicum spp.)

Flower production provides the foundation for crop yield and increased profits. Capsicum annuum is a pepper species with a sympodial shoot structure with solitary flowers. By contrast, C. chinense produces multiple flowers per node. C. annuum accounts for 80% of pepper production worldwide. The identification of C. chinense genes that control multiple flowers and their transfer into C. annuum may open the way to increasing fruit yield. In this study, we dissected the genetic factors were dissected controlling the multiple-flower-per-node trait in Capsicum. 85 recombinant inbred lines (RILs) between the contrasting C. annuum 'TF68' and C. chinense 'Habanero' accessions were phenotyped and genotyped. Quantitative Trait Loci (QTL) analysis identified four novel QTLs on chromosomes 1, 2, 7, and 11 that accounted for 65% of the total phenotypic variation. Genome-wide association study was also performed on a panel of 276 genotyped and phenotyped C. annuum accessions, which revealed 28 regions significantly associated with the multiple-flower trait, of which three overlapped the identified QTLs. Five candidate genes involved in the development of the shoot and flower meristems were identified and these genes could cause multiple flowers per node in pepper. These results contribute to our understanding of multiple flower formation in Capsicum and will be useful to develop high-yielding cultivars.

Exploring Machine Learning Algorithms to Unveil Genomic Regions Associated With Resistance to Southern Root-Knot Nematode in Soybeans

Southern root-knot nematode [SRKN, Meloidogyne incognita (Kofold & White) Chitwood] is a plant-parasitic nematode challenging to control due to its short life cycle, a wide range of hosts, and limited management options, of which genetic resistance is the main option to efficiently control the damage caused by SRKN. To date, a major quantitative trait locus (QTL) mapped on chromosome (Chr.) 10 plays an essential role in resistance to SRKN in soybean varieties. The confidence of discovered trait-loci associations by traditional methods is often limited by the assumptions of individual single nucleotide polymorphisms (SNPs) always acting independently as well as the phenotype following a Gaussian distribution. Therefore, the objective of this study was to conduct machine learning (ML)-based genome-wide association studies (GWAS) utilizing Random Forest (RF) and Support Vector Machine (SVM) algorithms to unveil novel regions of the soybean genome associated with resistance to SRKN. A total of 717 breeding lines derived from 330 unique bi-parental populations were genotyped with the Illumina Infinium BARCSoySNP6K BeadChip and phenotyped for SRKN resistance in a greenhouse. A GWAS pipeline involving a supervised feature dimension reduction based on Variable Importance in Projection (VIP) and SNP detection based on classification accuracy was proposed. Minor effect SNPs were detected by the proposed ML-GWAS methodology but not identified using Bayesian-information and linkage-disequilibrium Iteratively Nested Keyway (BLINK), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Enriched Compressed Mixed Linear Model (ECMLM) models. Besides the genomic region on Chr. 10 that can explain most of SRKN resistance variance, additional minor effects SNPs were also identified on Chrs. 10 and 11. The findings in this study demonstrated that overfitting in GWAS may lead to lower prediction accuracy, and the detection of significant SNPs based on classification accuracy limited false-positive associations. The expansion of the basis of the genetic resistance to SRKN can potentially reduce the selection pressure over the major QTL on Chr. 10 and achieve higher levels of resistance.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

Genetic architecture of variation in Arabidopsis thaliana rosettes

Rosette morphology across Arabidopsis accessions exhibits considerable variation. Here we report a high-throughput phenotyping approach based on automatic image analysis to quantify rosette shape and dissect the underlying genetic architecture. Shape measurements of the rosettes in a core set of Recombinant Inbred Lines from an advanced mapping population (Multiparent Advanced Generation Inter-Cross or MAGIC) derived from inter-crossing 19 natural accessions. Image acquisition and analysis was scaled to extract geometric descriptors from time stamped images of growing rosettes. Shape analyses revealed heritable morphological variation at early juvenile stages and QTL mapping resulted in over 116 chromosomal regions associated with trait variation within the population. Many QTL linked to variation in shape were located near genes related to hormonal signalling and signal transduction pathways while others are involved in shade avoidance and transition to flowering. Our results suggest rosette shape arises from modular integration of sub-organ morphologies and can be considered a functional trait subjected to selective pressures of subsequent morphological traits. On an applied aspect, QTLs found will be candidates for further research on plant architecture.

Linking genetic, morphological, and behavioural divergence between inland island and mainland deer mice

The island syndrome hypothesis (ISH) stipulates that, as a result of local selection pressures and restricted gene flow, individuals from island populations should differ from individuals within mainland populations. Specifically, island populations are predicted to contain individuals that are larger, less aggressive, more sociable, and that invest more in their offspring. To date, tests of the ISH have mainly compared oceanic islands to continental sites, and rarely smaller spatial scales such as inland watersheds. Here, using a novel set of genome-wide SNP markers in wild deer mice (Peromyscus maniculatus) we conducted a genomic assessment of predictions underlying the ISH in an inland riverine island system: analysing island-mainland population structure, and quantifying heritability of phenotypes thought to underlie the ISH. We found clear genomic differentiation between the island and mainland populations and moderate to high marker-based heritability estimates for overall variation in traits previously found to differ in line with the ISH between mainland and island locations. F_ST outlier analyses highlighted 12 loci associated with differentiation between mainland and island populations. Together these results suggest that the island populations examined are on independent evolutionary trajectories, the traits considered have a genetic basis (rather than phenotypic variation being solely due to phenotypic plasticity). Coupled with the previous results showing significant phenotypic differentiation between the island and mainland groups in this system, this study suggests that the ISH can hold even on a small spatial scale.

Identification of Up-Regulated ANXA3 Resulting in Fracture Non-Union in Patients With T2DM

Diabetes mellitus is a metabolic disorder that increases fracture risk and interferes with bone formation and impairs fracture healing. Genomic studies on diabetes and fracture healing are lacking. We used a weighted co-expression network analysis (WGCNA) method to identify susceptibility modules and hub genes associated with T2DM and fracture healing. First, we downloaded the GSE95849, GSE93213, GSE93215, and GSE142786 data from the Gene Expression Omnibus (GEO) website, analyzed differential expression genes and constructed a WGCNA network. Second, we screened out 30 hub genes, which were found to be enriched in neutrophil activation, translational initiation, RAGE receptor binding, propanoate metabolism, and other pathways through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses. Third, we searched for genes related to bone metabolism and fracture healing in the published genome-wide single nucleotide polymorphism (SNP) data, built a protein-protein interaction (PPI) network with hub genes, and found that they were associated with metabolic process, blood vessel development, and extracellular matrix organization. ANXA3 was identified as the biomarker based on gene expression and correlation analysis. And the AUC value of it was 0.947. Fourth, we explored that ANXA3 was associated with neutrophils in fracture healing process by single-cell RNA sequencing analysis. Finally, we collected clinical patient samples and verified the expression of ANXA3 by qRT-PCR in patents with T2DM and fracture non-union. In conclusion, this is the first genomics study on the effect of T2DM on fracture healing. Our study identified some characteristic modules and hub genes in the etiology of T2DM-associated fracture non-union, which may help to further investigate the molecular mechanisms. Up-regulated ANXA3 potentially contributed to fracture non-union in T2DM by mediating neutrophils. It can be a prognostic biomarker and potential therapeutic target.

Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle

Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3, MED28, NCAPG, LCORL, SLIT2, and IBSP, which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle.

Brain Immunoinformatics: A Symmetrical Link between Informatics, Wet Lab and the Clinic

Breakthrough advances in informatics over the last decade have thoroughly influenced the field of immunology. The intermingling of machine learning with wet lab applications and clinical results has hatched the newly defined immunoinformatics society. Immunoinformatics of the central neural system, referred to as neuroimmunoinformatics (NII), investigates symmetrical and asymmetrical interactions of the brain-immune interface. This interdisciplinary overview on NII is addressed to bioscientists and computer scientists. We delineate the dominating trajectories and field-shaping achievements and elaborate on future directions using bridging language and terminology. Computation, varying from linear modeling to complex deep learning approaches, fuels neuroimmunology through three core directions. Firstly, by providing big-data analysis software for high-throughput methods such as next-generation sequencing and genome-wide association studies. Secondly, by designing models for the prediction of protein morphology, functions, and symmetrical and asymmetrical protein–protein interactions. Finally, NII boosts the output of quantitative pathology by enabling the automatization of tedious processes such as cell counting, tracing, and arbor analysis. The new classification of microglia, the brain’s innate immune cells, was an NII achievement. Deep sequencing classifies microglia in “sensotypes” to accurately describe the versatility of immune responses to physiological and pathological challenges, as well as to experimental conditions such as xenografting and organoids. NII approaches complex tasks in the brain-immune interface, recognizes patterns and allows for hypothesis-free predictions with ultimate targeted individualized treatment strategies, and personalizes disease prognosis and treatment response.

African Gene Flow Reduces Beta-Ionone Anosmia/Hyposmia Prevalence in Admixed Malagasy Populations

While recent advances in genetics make it possible to follow the genetic exchanges between populations and their phenotypic consequences, the impact of the genetic exchanges on the sensory perception of populations has yet to be explored. From this perspective, the present study investigated the consequences of African gene flow on odor perception in a Malagasy population with a predominantly East Asian genetic background. To this end, we combined psychophysical tests with genotype data of 235 individuals who were asked to smell the odorant molecule beta-ionone (βI). Results showed that in this population the ancestry of the OR5A1 gene significantly influences the ability to detect βI. At the individual level, African ancestry significantly protects against specific anosmia/hyposmia due to the higher frequency of the functional gene (OR ratios = 14, CI: 1.8–110, p-value = 0.012). At the population level, African introgression decreased the prevalence of specific anosmia/hyposmia to this odorous compound. Taken together, these findings validate the conjecture that in addition to cultural exchanges, genetic transfer may also influence the sensory perception of the population in contact.

Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data

Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of "common disease common variant hypothesis," that suggests that if a disease that is heritable is common in the population (a prevalence >1-5%), then the genetic contributors-specific variations in the genetic code-will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.

The Quest for Genes Involved in Adaptation to Climate Change in Ruminant Livestock

Livestock radiated out from domestication centres to most regions of the world, gradually adapting to diverse environments, from very hot to sub-zero temperatures and from wet and humid conditions to deserts. The climate is changing; generally global temperature is increasing, although there are also more extreme cold periods, storms, and higher solar radiation. These changes impact livestock welfare and productivity. This review describes advances in the methodology for studying livestock genomes and the impact of the environment on animal production, giving examples of discoveries made. Sequencing livestock genomes has facilitated genome-wide association studies to localize genes controlling many traits, and population genetics has identified genomic regions under selection or introgressed from one breed into another to improve production or facilitate adaptation. Landscape genomics, which combines global positioning and genomics, has identified genomic features that enable animals to adapt to local environments. Combining the advances in genomics and methods for predicting changes in climate is generating an explosion of data which calls for innovations in the way big data sets are treated. Artificial intelligence and machine learning are now being used to study the interactions between the genome and the environment to identify historic effects on the genome and to model future scenarios.

The Paratuberculosis Paradigm Examined: A Review of Host Genetic Resistance and Innate Immune Fitness in Mycobacterium avium subsp. Paratuberculosis Infection

Paratuberculosis, or Johne's Disease (JD) is a debilitating chronic enteritis mainly affecting ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP). This organism causes worldwide economic losses to the livestock industry, and is of public health importance due to the potential zoonotic risk between MAP and Crohn's disease (CD) in humans. Without economical treatments, or a vaccine capable of preventing infection without causing cross-reactions with bovine tuberculosis, test-and-cull methods for disease control are imperative. Unfortunately, difficulties in diagnostics and long subclinical stage hinder adequate control and is further complicated by variation in MAP exposure outcome. Interestingly, the majority of infections result in asymptomatic presentation and never progress to clinical disease. One contributing factor is host genetics, where polymorphisms in innate immune genes have been found to influence resistance and susceptibility to disease. Candidate genes identified across studies overlap with those found in CD and tuberculosis including; Solute carrier family 11 member 1 gene (SLC11A1), Nucleotide-binding-oligomerization domain containing gene 2 (NOD2), Major histocompatibility complex type II (MHC-II), and Toll-like receptor (TLR) genes. This review will highlight evidence supporting the vital role of these genes in MAP infection outcome, associated challenges, and implications for the future of JD research.

Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle

Carcass merits are widely considered as economically important traits affecting beef production in the beef cattle industry. However, the genetic basis of carcass traits remains to be well understood. Here, we applied multiple methods, including the Composite of Likelihood Ratio (CLR) and Genome-wide Association Study (GWAS), to explore the selection signatures and candidate variants affecting carcass traits. We identified 11,600 selected regions overlapping with 2214 candidate genes, and most of those were enriched in binding and gene regulation. Notably, we identified 66 and 110 potential variants significantly associated with carcass traits using single-trait and multi-traits analyses, respectively. By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 putative genes, respectively. Several highly conserved missense variants were identified in OR5M13D, NCAPG, and TEX2. Our study supported polygenic genetic architecture of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle.

Genome-wide association studies: assessing trait characteristics in model and crop plants

GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.

Emerging issues in genomic selection

Genomic selection (GS) is now practiced successfully across many species. However, many questions remain, such as long-term effects, estimations of genomic parameters, robustness of genome-wide association study (GWAS) with small and large datasets, and stability of genomic predictions. This study summarizes presentations from the authors at the 2020 American Society of Animal Science (ASAS) symposium. The focus of many studies until now is on linkage disequilibrium between two loci. Ignoring higher-level equilibrium may lead to phantom dominance and epistasis. The Bulmer effect leads to a reduction of the additive variance; however, the selection for increased recombination rate can release anew genetic variance. With genomic information, estimates of genetic parameters may be biased by genomic preselection, but costs of estimation can increase drastically due to the dense form of the genomic information. To make the computation of estimates feasible, genotypes could be retained only for the most important animals, and methods of estimation should use algorithms that can recognize dense blocks in sparse matrices. GWASs using small genomic datasets frequently find many marker-trait associations, whereas studies using much bigger datasets find only a few. Most of the current tools use very simple models for GWAS, possibly causing artifacts. These models are adequate for large datasets where pseudo-phenotypes such as deregressed proofs indirectly account for important effects for traits of interest. Artifacts arising in GWAS with small datasets can be minimized by using data from all animals (whether genotyped or not), realistic models, and methods that account for population structure. Recent developments permit the computation of P-values from genomic best linear unbiased prediction (GBLUP), where models can be arbitrarily complex but restricted to genotyped animals only, and single-step GBLUP that also uses phenotypes from ungenotyped animals. Stability was an important part of nongenomic evaluations, where genetic predictions were stable in the absence of new data even with low prediction accuracies. Unfortunately, genomic evaluations for such animals change because all animals with genotypes are connected. A top-ranked animal can easily drop in the next evaluation, causing a crisis of confidence in genomic evaluations. While correlations between consecutive genomic evaluations are high, outliers can have differences as high as 1 SD. A solution to fluctuating genomic evaluations is to base selection decisions on groups of animals. Although many issues in GS have been solved, many new issues that require additional research continue to surface.

Identification of candidate genes associated with slaughter traits in F2 chicken population using genome-wide association study

Slaughter traits are crucial economic traits of chickens. We performed a GWAS to discover critical loci and candidate genes for 21 slaughter traits in an F2 chicken population resulting from crossing Luxi gamecocks and recessive white feather broilers. We found some SNPs and genes which were significantly associated with keel length, head length, body slope length, bilateral leg weight without shin, bilateral foot weight, subcutaneous fat thickness, heart weight, muscular stomach weight and glandular stomach weight. This study provides references for further investigation of slaughter traits and molecular breeding in chicken.

Detection of genomic regions underlying milk production traits in Valle del Belice dairy sheep using regional heritability mapping

The aim of this study was to identify genomic regions underlying milk production traits in the Valle del Belice dairy sheep using regional heritability mapping (RHM). Repeated measurements for milk yield (MY), fat percentage and yield (F% and FY) and protein percentage and yield (P% and PY), collected over a period of 6 years (2006–2012) on 481 Valle del Belice ewes, were used for the analysis. Animals were genotyped with the Illumina 50k SNP chip. Variance components, heritabilities and repeatabilities within and across lactations were estimated, fitting parity, litter size, season of lambing and fortnights in milk, as fixed; and additive genetic, permanent environment within and across lactations, flock by test‐day interaction and residual as random effects. For the RHM analysis, the model included the same fixed and random effects as before, plus an additional regional genomic additive effect (specific for the region being tested) as random. While the whole genomic additive effect was estimated using the genomic relationship matrix (GRM) constructed from all SNPs, the regional genomic additive effect was estimated from a GRM matrix constructed from the SNPs within each region. Heritability estimates ranged between 0.06 and 0.15, with repeatabilities being between 0.14 and 0.24 across lactations and between 0.23 and 0.39 within lactation for all milk production traits. A substantial effect of flock‐test‐day on milk production traits was also estimated. Significant genomic regions at either genome‐wide (p < .05) or suggestive (i.e., one false positive per genome scan) level were identified on chromosome (OAR) 2, 3 and 20 for F% and on OAR3 for P%, with the regions on OAR3 in common between the two traits. Our results confirmed the role of LALBA and AQP genes, on OAR3, as candidate genes for milk production traits in sheep.

Mapping of Genetic Locus for Leaf Trichome Formation in Chinese Cabbage Based on Bulked Segregant Analysis

Chinese cabbage is a leafy vegetable, and its leaves are the main edible organs. The formation of trichomes on the leaves can significantly affect its taste, so studying this phenomenon is of great significance for improving the quality of Chinese cabbage. In this study, two varieties of Chinese cabbage, W30 with trichome leaves and 082 with glabrous leaves, were crossed to generate F₁ and F₁ plants, which were self-fertilized to develop segregating populations with trichome or glabrous morphotypes. The two bulks of the different segregating populations were used to conduct bulked segregant analysis (BSA). A total of 293.4 M clean reads were generated from the samples, and plants from the trichome leaves (AL) bulk and glabrous leaves (GL) bulk were identified. Between the two DNA pools generated from the trichome and glabrous plants, 55,048 SNPs and 272 indels were generated. In this study, three regions (on chromosomes 6, 10 and scaffold000100) were identified, and the annotation revealed three candidate genes that may participate in the formation of leaf trichomes. These findings suggest that the three genes-Bra025087 encoding a cyclin family protein, Bra035000 encoding an ATP-binding protein/kinase/protein kinase/protein serine/threonine kinase and Bra033370 encoding a WD-40 repeat family protein-influence the formation of trichomes by participating in trichome morphogenesis (GO: 0010090). These results demonstrate that BSA can be used to map genes associated with traits and provide new insights into the molecular mechanism of leafy trichome formation in Chinese cabbage.

Next generation sequencing of SARS-CoV-2 genomes: challenges, applications and opportunities

Various next generation sequencing (NGS) based strategies have been successfully used in the recent past for tracing origins and understanding the evolution of infectious agents, investigating the spread and transmission chains of outbreaks, as well as facilitating the development of effective and rapid molecular diagnostic tests and contributing to the hunt for treatments and vaccines. The ongoing COVID-19 pandemic poses one of the greatest global threats in modern history and has already caused severe social and economic costs. The development of efficient and rapid sequencing methods to reconstruct the genomic sequence of SARS-CoV-2, the etiological agent of COVID-19, has been fundamental for the design of diagnostic molecular tests and to devise effective measures and strategies to mitigate the diffusion of the pandemic. Diverse approaches and sequencing methods can, as testified by the number of available sequences, be applied to SARS-CoV-2 genomes. However, each technology and sequencing approach has its own advantages and limitations. In the current review, we will provide a brief, but hopefully comprehensive, account of currently available platforms and methodological approaches for the sequencing of SARS-CoV-2 genomes. We also present an outline of current repositories and databases that provide access to SARS-CoV-2 genomic data and associated metadata. Finally, we offer general advice and guidelines for the appropriate sharing and deposition of SARS-CoV-2 data and metadata, and suggest that more efficient and standardized integration of current and future SARS-CoV-2-related data would greatly facilitate the struggle against this new pathogen. We hope that our 'vademecum' for the production and handling of SARS-CoV-2-related sequencing data, will contribute to this objective.

Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects

Phenotypes of sessile organisms, such as plants, rely not only on their own genotypes but also on those of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known regarding how neighbor effects should be incorporated in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, designated "neighbor QTL," the algorithm of which includes: (1) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers; (2) calculating conditional neighbor genotypic identity using the self-genotype probabilities; and (3) estimating additive and dominance deviations for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range decreased. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines of Arabidopsis thaliana. Consistent with previous results, the pilot experiment detected a self-QTL effect on the herbivory at the GLABRA1 locus. Regarding neighbor QTL effects on herbivory, we observed a weak QTL on the top of chromosome 4, at which a weak self-bolting QTL was also identified. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

Review on Databases and Bioinformatic Approaches on Pharmacogenomics of Adverse Drug Reactions

Pharmacogenomics has been used effectively in studying adverse drug reactions by determining the person-specific genetic factors associated with individual response to a drug. Current approaches have revealed the significant importance of sequencing technologies and sequence analysis strategies for interpreting the contribution of genetic variation in developing adverse reactions. Advance in next generation sequencing and platform brings new opportunities in validating the genetic candidates in certain reactions, and could be used to develop the preemptive tests to predict the outcome of the variation in a personal response to a drug. With the highly accumulated available data recently, the in silico approach with data analysis and modeling plays as other important alternatives which significantly support the final decisions in the transformation from research to clinical applications such as diagnosis and treatments for various types of adverse responses.

BnaGVD: A genomic variation database of rapeseed (Brassica napus)

Rapeseed (Brassica napus L.) is a typical polyploid crop and one of the most important oilseed crops worldwide. With the rapid progress on high-throughput sequencing technologies and the reduction of sequencing cost, large-scale genomic data of a specific crop have become available. However, raw sequence data are mostly deposited in the sequence read archive of the National Center of Biotechnology Information (NCBI) and the European Nucleotide Archive (ENA), which is freely accessible to all researchers. Extensive tools for practical purposes should be developed to efficiently utilize these large raw data. Here, we report a web-based rapeseed genomic variation database (BnaGVD, http://rapeseed.biocloud.net/home) from which genomic variations, such as single nucleotide polymorphisms (SNPs) and insertions/deletions (InDels) across a world-wide collection of rapeseed accessions, can be referred. The current release of the BnaGVD contains 34,591,899 high-quality SNPs and 12,281,923 high-quality InDels and provides search tools to retrieve genomic variations and gene annotations across 1,007 accessions of worldwide rapeseed germplasm. We implement a variety of built-in tools (e.g., BnaGWAS, BnaPCA, and BnaStructure) to help users perform in-depth analyses. We recommend this web resource for accelerating studies on the functional genomics and screening of molecular markers for rapeseed breeding.

A multi-omics view of the complex mechanism of vascular calcification

Vascular calcification is a high incidence and high risk disease with increasing morbidity and high mortality, which is considered the consequence of smooth muscle cell transdifferentiation initiating the mechanism of accumulation of hydroxyl calcium phosphate. Vascular calcification is also thought to be strongly associated with poor outcomes in diabetes and chronic kidney disease. Numerous studies have been accomplished; however, the specific mechanism of the disease remains unclear. Development of the genome project enhanced the understanding of life science and has entered the post-genomic era resulting in a variety of omics techniques used in studies and a large amount of available data; thus, a new perspective on data analysis has been revealed. Omics has a broader perspective and is thus advantageous over a single pathway analysis in the study of complex vascular calcification mechanisms. This paper reviews in detail various omics studies including genomics, proteomics, transcriptomics, metabolomics and multiple group studies on vascular calcification. Advances and deficiencies in the use of omics to study vascular calcification are presented in a comprehensive view. We also review the methodology of the omics studies and omics data analysis and processing. In addition, the methodology and data processing presented here can be applied to other areas. An omics landscape perspective across the boundaries between genomics, transcriptomics, proteomics and metabolomics is used to examine the mechanisms of vascular calcification. The perspective combined with various technologies also provides a direction for the subsequent exploration of clinical significance.

Haplotype-based genome-wide association increases the predictability of leaf rust (Puccinia triticina) resistance in wheat

Resistance breeding is crucial for sustainable control of wheat leaf rust and single nucleotide polymorphism (SNP)-based genome-wide association studies (GWAS) are widely used to dissect leaf rust resistance. Unfortunately, GWAS based on SNPs often explained only a small proportion of the genetic variation. We compared SNP-based GWAS with a method based on functional haplotypes (FH) considering epistasis in a comprehensive hybrid wheat mapping population composed of 133 parents plus their 1574 hybrids and characterized with 626 245 high-quality SNPs. In total, 2408 and 1 139 828 significant associations were detected in the mapping population by using SNP-based and FH-based GWAS, respectively. These associations mapped to 25 and 69 candidate regions, correspondingly. SNP-based GWAS highlighted two already-known resistance genes, Lr22a and Lr34-B, while FH-based GWAS detected associations not only on these genes but also on two additional genes, Lr10 and Lr1. As revealed by a second hybrid wheat population for independent validation, the use of detected associations from SNP-based and FH-based GWAS reached predictabilities of 11.72% and 22.86%, respectively. Therefore, FH-based GWAS is not only more powerful for detecting associations, but also improves the accuracy of marker-assisted selection compared with the SNP-based approach.