Complex Genetic Traits Research Articles

Formation and diversification of a paradigm biosynthetic gene cluster in plants

Numerous examples of biosynthetic gene clusters (BGCs), including for compounds of agricultural and medicinal importance, have now been discovered in plant genomes. However, little is known about how these complex traits are assembled and diversified. Here, we examine a large number of variants within and between species for a paradigm BGC (the thalianol cluster), which has evolved recently in a common ancestor of the Arabidopsis genus. Comparisons at the species level reveal differences in BGC organization and involvement of auxiliary genes, resulting in production of species-specific triterpenes. Within species, the thalianol cluster is primarily fixed, showing a low frequency of deleterious haplotypes. We further identify chromosomal inversion as a molecular mechanism that may shuffle more distant genes into the cluster, so enabling cluster compaction. Antagonistic natural selection pressures are likely involved in shaping the occurrence and maintenance of this BGC. Our work sheds light on the birth, life and death of complex genetic and metabolic traits in plants.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance.

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Assessment and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis.

Hypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power, sample overlap or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments across human phenome. We developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed causal architecture plot. We apply this pipeline to body mass index (BMI) and lipid traits as exemplars of traits where there is strong prior expectation for causal effects, and to dental caries and periodontitis as exemplars of traits where there is a need for causal inference. The results for lipids and BMI suggest that these traits are best viewed as contributing factors on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. The automated pipeline is implemented in the Complex-Traits Genetics Virtual Lab ( https://vl.genoma.io ) and results are available in https://view.genoma.io . We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a new way visualizing the overall causal map of the human phenome.

Comparing local ancestry inference models in populations of two- and three-way admixture.

Local ancestry estimation infers the regional ancestral origin of chromosomal segments in admixed populations using reference populations and a variety of statistical models. Integrating local ancestry into complex trait genetics has the potential to increase detection of genetic associations and improve genetic prediction models in understudied admixed populations, including African Americans and Hispanics. Five methods for local ancestry estimation that have been used in human complex trait genetics are LAMP-LD (2012), RFMix (2013), ELAI (2014), Loter (2018), and MOSAIC (2019). As users rather than developers, we sought to perform direct comparisons of accuracy, runtime, memory usage, and usability of these software tools to determine which is best for incorporation into association study pipelines. We find that in the majority of cases RFMix has the highest median accuracy with the ranking of the remaining software dependent on the ancestral architecture of the population tested. Additionally, we estimate the O(n) of both memory and runtime for each software and find that for both time and memory most software increase linearly with respect to sample size. The only exception is RFMix, which increases quadratically with respect to runtime and linearly with respect to memory. Effective local ancestry estimation tools are necessary to increase diversity and prevent population disparities in human genetics studies. RFMix performs the best across methods, however, depending on application, other methods perform just as well with the benefit of shorter runtimes. Scripts used to format data, run software, and estimate accuracy can be found at https://github.com/WheelerLab/LAI_benchmarking.

Chromosome drives via CRISPR-Cas9 in yeast

Self-propagating drive systems are capable of causing non-Mendelian inheritance. Here, we report a drive system in yeast referred to as a chromosome drive that eliminates the target chromosome via CRISPR-Cas9, enabling the transmission of the desired chromosome. Our results show that the entire Saccharomyces cerevisiae chromosome can be eliminated efficiently through only one double-strand break around the centromere via CRISPR-Cas9. As a proof-of-concept experiment of this CRISPR-Cas9 chromosome drive system, the synthetic yeast chromosome X is completely eliminated, and the counterpart wild-type chromosome X harboring a green fluorescent protein gene or the components of a synthetic violacein pathway are duplicated by sexual reproduction. We also demonstrate the use of chromosome drive to preferentially transmit complex genetic traits in yeast. Chromosome drive enables entire chromosome elimination and biased inheritance on a chromosomal scale, facilitating genomic engineering and chromosome-scale genetic mapping, and extending applications of self-propagating drives.

Abstract 3396: Multiethnic genome-wide meta-analysis of 34,329 cases and 35,732 controls identifies cross-ancestry loci for lung cancer susceptibility

Abstract Genome-wide association studies (GWAS) have revealed susceptible genetic risk factors for lung cancer, highlighting the role of smoking, family history, and DNA damage repair genes in disease etiology. Many studies have focused on European populations; however, lung cancer is a leading cause of cancer incidence and mortality around the world. Previous GWAS analyses have been focusing on a single population-based analyses to exclude the confounding effects such as the presence of systematic allele frequency differences between populations. Another efficient tool for GWAS of complex genetic diseases and traits is meta-analysis providing a practical strategy for detecting genetic variants with modest effect sizes. This study aimed to identify novel genetic susceptibility loci in a large, multiethnic GWAS of lung cancer. The HRC imputation of lung GWAS was carried out in the Sanger Imputation Server. The imputed GWAS with 34,429 cases and 35,732 controls from OncoArray lung cancer GWAS data were used in the study. We applied Fastpop to infer the ancestry membership in three intercontinental populations. We conducted genome-wide association meta-analyses (METAL) in up to 70,161 individuals of European (26,683 cases/25,278 controls), African (1,987 cases/3,779 controls), or Asian (7,062 cases/5,372 controls) ancestry using HRC imputed lung cancer data. The novel variants in or near DCBLD1 on 6q22.1 (OR=0.93,P=2.11 × 10−10), IRF4 on 6p25.3 (OR=1.11,P=3.96 × 10−8), PPIL6 on 6q21 (OR=1.10,P=4.41 × 10−9), ROS1 on 6p22.1 (OR=0.94,P=2.49 × 10−9) for overall lung cancer, ACTR2 on 2p14 OR=0.89,P=2.96 × 10−9), ATM on 11q22.3 (OR=3.61,P=8.88 × 10−10), PSMA4 on 15q25.1 (OR=0.88,P=1.07 × 10−12) for lung adenocarcinoma, ABCF1 (OR=1.35,P=4.54 × 10−12), C2 (OR=1.37,P=6.65 × 10−13), CFB (OR=1.37,P=5.44 × 10−13), CYP21A2 (OR=1.35,P=1.71 × 10−10), VWA7 (OR=1.36,P=1.3 × 10−12) on 6p21.33, HCG9 on 6p22.1 (OR=1.30,P=2.34 × 10−10), IREB2 on 15q25.1 (OR=1.20,P=9.25 × 10−19), TTC28 (OR=0.29,P=2.84 × 10−11), ZNRF3 (OR=0.37,P=3.55 × 10−10) on 22q12.1 for lung squamous cell carcinoma, and ZC3H15 on 2q32.1 (OR=2.73,P=2.62 × 10−8), NECTIN1 on 11q23.3 (OR=8.96,P=3.27 × 10−8) for lung small cell carcinoma were identified at a genome-wide level of significance in lung cancer. Our large, multiethnic GWAS meta-analysis of lung cancer has identified several novel genetic associations. Further work is required to elucidate the biological mechanisms underlying these associations. Our results suggest that multiethnic meta-analysis of larger lung cancer datasets may yield additional genetic risk loci of moderate effect size. Citation Format: Jinyoung Byun, Xiangjun Xiao, Younghun Han, Yafang Li, Ryan Sun, Xihao Li, Hufeng Zhou, Xihong Lin, James McKay, Rayjean Hung, Christopher Amos, INTEGRAL Consortium. Multiethnic genome-wide meta-analysis of 34,329 cases and 35,732 controls identifies cross-ancestry loci for lung cancer susceptibility [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3396.

Efficient variance components analysis across millions of genomes

While variance components analysis has emerged as a powerful tool in complex trait genetics, existing methods for fitting variance components do not scale well to large-scale datasets of genetic variation. Here, we present a method for variance components analysis that is accurate and efficient: capable of estimating one hundred variance components on a million individuals genotyped at a million SNPs in a few hours. We illustrate the utility of our method in estimating and partitioning variation in a trait explained by genotyped SNPs (SNP-heritability). Analyzing 22 traits with genotypes from 300,000 individuals across about 8 million common and low frequency SNPs, we observe that per-allele squared effect size increases with decreasing minor allele frequency (MAF) and linkage disequilibrium (LD) consistent with the action of negative selection. Partitioning heritability across 28 functional annotations, we observe enrichment of heritability in FANTOM5 enhancers in asthma, eczema, thyroid and autoimmune disorders.

Allelic variation of gliadin-encoding genes in a collection of tetraploid wheat genotypes

Wheat is one of the main crops bred worldwide. Durum wheat, specifically, is a key element of the Mediterranean diet, representing an élite crop grown in Italy. Durum wheat nutritional and technological values are largely due to the grain protein content (GPC), a complex genetic trait strongly affected by environmental factors and management practices. In the last decades, several breeding programs have been focused on improving GPC by both traditional and innovative approaches. Among seed storage proteins, prolamins, including both gliadins and glutenins, represent the major component. These two classes of proteins are indeed responsible of gluten formation and confer the extensibility and elasticity to the dough. Besides being of crucial importance for both technological properties and rheological characteristics, prolamins, and especially gliadins, have been found to be major triggers for human health, as involved in a number of wheat consumption-related conditions, such as the celiac disease, non-celiac gluten sensitivity, defined as the onset of a variety of manifestations related to wheat, rye and barley ingestion, and wheat allergies, both due to wheat ingestion or inhalation (of flour or pollen). The identification of loci responsible for the gliadin expression, and particularly of polymorphism in the aforementioned genes, which could result in a lower immunogenic/toxic potential, could be of great importance in breeding programs. For this purpose, we screened a collection of tetraploid wheat genotypes for allelic variants of annotated gliadin genes in the durum wheat genome, in order to identify genetic resources available to breeders to improve wheat nutritional and technological properties. Phylogenetic analysis among different species of Triticum genus and an in silico expression data analysis may also be useful in the exploitation of the complex scenario of gliadin–glutenin interaction and gluten role in the adverse reactions due to wheat consumption.

Precision medicine for pancreatic diseases.

We describe and contrast the strengths of precision medicine with Western medicine, and complex trait genetics with Mendelian genetics. Classic genetics focuses on highly penetrant pathogenic variants in a single gene believed to cause or confer a high risk for well-defined phenotypes. However, a minority of disorders have a single gene cause. Further, even individuals with identical Mendelian disease-associated genotypes may exhibit substantial phenotypic variability indicative of genetic and environmental modifiers. Still, most diseases are considered complex traits (or complex diseases). New insights into the genetic underpinnings of complex traits provide opportunities for advances in diagnosis and management. Precision medicine provides the framework for integrating complex trait knowledge into clinical care through a sophisticated analysis pipeline. Multidimensional modeling of acquired diseases includes multiple genetic risks scattered over many genes and gene regulators that must be interpreted on the basis of functional evidence (e.g., genomics, transcriptomics) with structured models and expert systems; strengthened with machine learning and artificial intelligence. The choice of genotyping approaches (shotgun sequencing, single nucleotide polymorphism chips, targeted panels) is discussed. The result of a good precision medicine tool is clinical-decision support and guidance to tackle complex disorders such as pancreatitis, diabetes, and pancreatic cancer oncogenesis.

Four Loci Are Associated with Cardiorespiratory Fitness and Endurance Performance in Young Chinese Females

Cardiorespiratory fitness (CRF) and endurance performance are characterized by a complex genetic trait with high heritability. Although research has identified many physiological and environmental correlates with CRF, the genetic architecture contributing to CRF remains unclear, especially in non-athlete population. A total of 762 Chinese young female participants were recruited and an endurance run test was used to determine CRF. We used a fixed model of genome-wide association studies (GWAS) for CRF. Genotyping was performed using the Affymetrix Axiom and illumina 1 M arrays. After quality control and imputation, a linear regression-based association analysis was conducted using a total of 5,149,327 variants. Four loci associated with CRF were identified to reach genome-wide significance (P < 5.0 × 10-8), which located in 15q21.3 (rs17240160, P = 1.73 × 10-9, GCOM1), 3q25.31 (rs819865, P = 8.56 × 10-9, GMPS), 21q22.3 (rs117828698, P = 9.59 × 10-9, COL18A1), and 17q24.2 (rs79806428, P = 3.85 × 10-8, PRKCA). These loci (GCOM1, GMPS, COL18A1 and PRKCA) associated with cardiorespiratory fitness and endurance performance in Chinese non-athlete young females. Our results suggest that these gene polymorphisms provide further genetic evidence for the polygenetic nature of cardiorespiratory endurance and be used as genetic biomarkers for future research.

Spice crops tolerant to salinity and alkalinity

Tolerance and yield of a crop are complex genetic traits, which are difficult to maintain simultaneously since salt stress may occur as a catastrophic agent, be imposed continuously or intermittently or become gradually more severe. Salinity and alkalinity stress have a major impact on spices in the form of their growth, development and yield.Adverse effects of salinity might be due to ion cytotoxicity and osmatic stress, which disrupt homeostasis in water potential and ionic distribution due to disordering in cohesions of membrane lipids and proteins and influence various physiological and biochemical processes. To review the tolerance of spices to salinity and alkalinity, the present paper collates the existing experimental data sets, establishing the salt tolerance limits under saline or alkali environment either in soil root zone or which is created due to the application of saline or alkali irrigation water for crop production. Studies show that the salt affected areas and saline irrigation water can be utilized satisfactorily to raise forest and fruit tree species, forage grasses, conventional and non-conventional crops, oil seed crops, spice crops of high economic value, petro-crops and flower plants.

Spice crops tolerant to salinity and alkalinity

Tolerance and yield of a crop are complex genetic traits, which are difficult to maintain simultaneously since salt stress may occur as a catastrophic agent, be imposed continuously or intermittently or become gradually more severe. Salinity and alkalinity stress have a major impact on spices in the form of their growth, development and yield.Adverse effects of salinity might be due to ion cytotoxicity and osmatic stress, which disrupt homeostasis in water potential and ionic distribution due to disordering in cohesions of membrane lipids and proteins and influence various physiological and biochemical processes. To review the tolerance of spices to salinity and alkalinity, the present paper collates the existing experimental data sets, establishing the salt tolerance limits under saline or alkali environment either in soil root zone or which is created due to the application of saline or alkali irrigation water for crop production. Studies show that the salt affected areas and saline irrigation water can be utilized satisfactorily to raise forest and fruit tree species, forage grasses, conventional and non-conventional crops, oil seed crops, spice crops of high economic value, petro-crops and flower plants.

Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics

In complex trait genetics, the ability to predict phenotype from genotype is the ultimate measure of our understanding of genetic architecture underlying the heritability of a trait. A complete understanding of the genetic basis of a trait should allow for predictive methods with accuracies approaching the trait's heritability. The highly polygenic nature of quantitative traits and most common phenotypes has motivated the development of statistical strategies focused on combining myriad individually non-significant genetic effects. Now that predictive accuracies are improving, there is a growing interest in the practical utility of such methods for predicting risk of common diseases responsive to early therapeutic intervention. However, existing methods require individual-level genotypes or depend on accurately specifying the genetic architecture underlying each disease to be predicted. Here, we propose a polygenic risk prediction method that does not require explicitly modeling any underlying genetic architecture. We start with summary statistics in the form of SNP effect sizes from a large GWAS cohort. We then remove the correlation structure across summary statistics arising due to linkage disequilibrium and apply a piecewise linear interpolation on conditional mean effects. In both simulated and real datasets, this new non-parametric shrinkage (NPS) method can reliably allow for linkage disequilibrium in summary statistics of 5 million dense genome-wide markers and consistently improves prediction accuracy. We show that NPS improves the identification of groups at high risk for breast cancer, type 2 diabetes, inflammatory bowel disease, and coronary heart disease, all of which have available early intervention or prevention treatments.

Host genetic susceptibility to mycetoma.

Mycetoma is one of the badly neglected tropical diseases, characterised by subcutaneous painless swelling, multiple sinuses, and discharge containing aggregates of the infecting organism known as grains. Risk factors conferring susceptibility to mycetoma include environmental factors and pathogen factors such as virulence and the infecting dose, in addition to host factors such as immunological and genetic predisposition. Epidemiological evidence suggests that host genetic factors may regulate susceptibility to mycetoma and other fungal infections, but they are likely to be complex genetic traits in which multiple genes interact with each other and environmental factors, as well as the pathogen, to cause disease. This paper reviews what is known about genetic predisposition to fungal infections that might be relevant to mycetoma, as well as all studies carried out to explore host genetic susceptibility to mycetoma. Most studies were investigating polymorphisms in candidate genes related to the host immune response. A total of 13 genes had allelic variants found to be associated with mycetoma, and these genes lie in different pathways and systems such as innate and adaptive immune systems, sex hormone biosynthesis, and some genes coding for host enzymes. None of these studies have been replicated. Advances in genomic science and the supporting technology have paved the way for large-scale genome-wide association and next generation sequencing (NGS) studies, underpinning a new strategy to systematically interrogate the genome for variants associated with mycetoma. Dissecting the contribution of host genetic variation to susceptibility to mycetoma will enable the identification of pathways that are potential targets for new treatments for mycetoma and will also enhance the ability to stratify ‘at-risk’ individuals, allowing the possibility of developing preventive and personalised clinical care strategies in the future.

Impact of Vitamin D Binding Protein Levels on Alzheimer's Disease: A Mendelian Randomization Study.

Until now, observational studies, randomized controlled trials (RCTs), and Mendelian randomization (MR) studies have explored the impact of vitamin D on Alzheimer's disease (AD), and reported inconsistent findings. In MR studies, the sensitivity analysis by removing GC rs2282679 variant highlighted no association of 25OHD levels with AD risk, which indicates that vitamin D-binding protein (DBP) encoded by GC may have distinct effects on AD risk. Here, we aim to clarify this assumption. We selected the GC rs2282679 variant associated with DBP levels (p = 3.30E-76) as the instrumental variable, and extracted the summary statistics of rs2282679 variant in multiple AD GWAS datasets from IGAP, Complex Trait Genetics (CTG) lab, and UK Biobank. We then performed a MR study to investigate the causal association between DBP levels and AD. In IGAP, MR analysis showed that the genetically DBP levels (per 1 standard deviation (SD) increase 50 mg/L) were significantly associated with reduced AD risk (OR = 0.63, 95% CI: 0.45-0.89, p = 0.009). Importantly, the estimates from two sensitivity analyses were consistent with the main estimate in terms of direction and magnitude. Meanwhile, we found no causal association between DBP levels and other four AD phenotypes in CTG lab and UK Biobank. In summary, we highlight the role of DBP levels in AD risk, and provide strong support evidence that DBP may be the therapeutic agent for the treatment of AD. Meanwhile, our findings clarify the assumption that DBP may drive the observed relationship between 25OHD levels and AD.

Immunophenotypic Profiles in Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) a long-known endocrinopathy and one of the most common endocrine-reproductive-metabolic disorders in women, which can lead to infertility. Although the precise etiology remains unclear, PCOS is considered as a complex genetic trait, with a high degree of heterogeneity. Besides, hormones and immune cells, including both innate and adaptive immune cells, are reportedly a cross talk in PCOS. Chronic low-grade inflammation increases autoimmune disease risk. This proinflammatory condition may, in turn, affect vital physiological processes that ultimately cause infertility, such as ovulation failure and embryo implantation. Here, we review the accumulating evidence linking PCOS with inflammatory status providing an overview of the underlying hormone-mediated dysregulation of immune cells. We mainly focus on the correlational evidence of associations between immune status in women and the increased prevalence of PCOS, along with the specific changes in immune responses. Further recognition and exploration of these interactions may help elucidate PCOS pathophysiology and highlight targets for its treatment and prevention.

Alzheimer-related genes show accelerated evolution

Alzheimerʼs disease (AD) is a neurodegenerative disorder of unknown cause with complex genetic and environmental traits. While AD is extremely prevalent in human elderly, it hardly occurs in non-primate mammals and even non-human-primates develop only an incomplete form of the disease. This specificity of AD to human clearly implies a phylogenetic aspect. Still, the evolutionary dimension of AD pathomechanism remains difficult to prove and has not been established so far. To analyze the evolutionary age and dynamics of AD-associated-genes, we established the AD-associated genome-wide RNA-profile comprising both protein-coding and non-protein-coding transcripts. We than applied a systematic analysis on the conservation of splice-sites as a measure of gene-structure based on multiple alignments across vertebrates of homologs of AD-associated-genes. Here, we show that nearly all AD-associated-genes are evolutionarily old and did not originate later in evolution than not-AD-associated-genes. However, the gene-structures of loci, that exhibit AD-associated changes in their expression, evolve faster than the genome at large. While protein-coding-loci exhibit an enhanced rate of small changes in gene structure, non-coding loci show even much larger changes. The accelerated evolution of AD-associated-genes indicates a more rapid functional adaptation of these genes. In particular AD-associated non-coding-genes play an important, as yet largely unexplored, role in AD. This phylogenetic trait indicates that recent adaptive evolution of human brain is causally involved in basic principles of neurodegeneration. It highlights the necessity for a paradigmatic change of our disease-concepts and to reconsider the appropriateness of current animal-models to develop disease-modifying strategies that can be translated to human.

Marginal Lands to Grow Novel Bio-Based Crops: A Plant Breeding Perspective.

The biomass demand to fuel a growing global bio-based economy is expected to tremendously increase over the next decades, and projections indicate that dedicated biomass crops will satisfy a large portion of it. The establishment of dedicated biomass crops raises huge concerns, as they can subtract land that is required for food production, undermining food security. In this context, perennial biomass crops suitable for cultivation on marginal lands (MALs) raise attraction, as they could supply biomass without competing for land with food supply. While these crops withstand marginal conditions well, their biomass yield and quality do not ensure acceptable economic returns to farmers and cost-effective biomass conversion into bio-based products, claiming genetic improvement. However, this is constrained by the lack of genetic resources for most of these crops. Here we first review the advantages of cultivating novel perennial biomass crops on MALs, highlighting management practices to enhance the environmental and economic sustainability of these agro-systems. Subsequently, we discuss the preeminent breeding targets to improve the yield and quality of the biomass obtainable from these crops, as well as the stability of biomass production under MALs conditions. These targets include crop architecture and phenology, efficiency in the use of resources, lignocellulose composition in relation to bio-based applications, and tolerance to abiotic stresses. For each target trait, we outline optimal ideotypes, discuss the available breeding resources in the context of (orphan) biomass crops, and provide meaningful examples of genetic improvement. Finally, we discuss the available tools to breed novel perennial biomass crops. These comprise conventional breeding methods (recurrent selection and hybridization), molecular techniques to dissect the genetics of complex traits, speed up selection, and perform transgenic modification (genetic mapping, QTL and GWAS analysis, marker-assisted selection, genomic selection, transformation protocols), and novel high-throughput phenotyping platforms. Furthermore, novel tools to transfer genetic knowledge from model to orphan crops (i.e., universal markers) are also conceptualized, with the belief that their development will enhance the efficiency of plant breeding in orphan biomass crops, enabling a sustainable use of MALs for biomass provision.

Associations Between Genomic Variants in lncRNA-TRPM2-AS and lncRNA-HNF1A-AS1 Genes and Risk of Multiple Sclerosis.

Multiple sclerosis (MS) is a complex genetic trait characterized by demyelination of central nervous system (CNS), inflammation, and progressive neurological dysfunction. There is evidenced that autophagy and stress mechanisms are tightly linked with MS. Previous studies have demonstrated that LncRNAs TRPM2-AS and HNF1A-AS1 are involved in oxidative stress and autophagy, respectively. In the current study, we investigated the association of TRPM2-AS and HNF1A-AS1 single nucleotide polymorphisms (SNPs) with MS risk in 300 Iranian patients and 300 healthy controls. Our results have shown that T allele of the rs933151 was statistically significant underrepresented in MS patients compared with healthy subjects (OR (95% CI) = 0.696 (0.532-0.911), P = 0.005). This SNP was associated with lower MS risk in codominant and dominant models (OR (95% CI) = 0.68 (0.48-0.96), P value = 0.032; OR (95% CI) = 0.65 (0.47-0.91), P value = 0.012, respectively). The rs7953249 was not associated with MS susceptibility in any inheritance models (P values of 0.73, 0.46, 0.61, and 0.71 for codominant, dominant, recessive, and overdominant models, respectively). Present study highlighted a novel association at the TRPM2-AS gene (SNP rs933151) with MS susceptibility.

Genetics' Piece of the PI: Inferring the Origin of Complex Traits and Diseases from Proteome-Wide Protein-Protein Interaction Dynamics.

How do common and rare genetic polymorphisms contribute to quantitative traits or disease risk and progression? Multiple human traits have been extensively characterized at the genomic level, revealing their complex genetic architecture. However, it is difficult to resolve the mechanisms by which specific variants contribute to a phenotype. Recently, analyses of variant effects on molecular traits have uncovered intermediate mechanisms that link sequence variation to phenotypic changes. Yet, these methods only capture a fraction of genetic contributions to phenotype. Here, in reviewing the field, it is proposed that complex traits can be understood by characterizing the dynamics of biochemical networks within living cells, and that the effects of genetic variation can be captured on these networks by using protein-protein interaction (PPI) methodologies. This synergy between PPI methodologies and the genetics of complex traits opens new avenues to investigate the molecular etiology of human diseases and to facilitate their prevention or treatment.