Progress and promise of genome-wide association studies for human complex trait genetics

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

El Hierro Genome Study: A Genomic and Health Study in an Isolated Canary Island Population

El Hierro is the smallest and westernmost island of the Canary Islands, whose population derives from an admixture of different ancestral components and that has been subjected to genetic isolation. We established the "El Hierro Genome Study" to characterize the health status and the genetic composition of ~10% of the current population of the island, accounting for a total of 1054 participants. Detailed demographic and clinical data and a blood sample for DNA extraction were obtained from each participant. Genomic genotyping was performed with the Global Screening Array (Illumina). The genetic composition of El Hierro was analyzed in a subset of 416 unrelated individuals by characterizing the mitochondrial DNA (mtDNA) and Y-chromosome haplogroups and performing principal component analyses (PCAs). In order to explore signatures of isolation, runs of homozygosity (ROHs) were also estimated. Among the participants, high blood pressure, hypercholesterolemia, and diabetes were the most prevalent conditions. The most common mtDNA haplogroups observed were of North African indigenous origin, while the Y-chromosome ones were mainly European. The PCA showed that the El Hierro population clusters near 1000 Genomes' European population but with a shift toward African populations. Moreover, the ROH analysis revealed some individuals with an important portion of their genomes with ROHs exceeding 400 Mb. Overall, these results confirmed that the "El Hierro Genome" cohort offers an opportunity to study the genetic basis of several diseases in an unexplored isolated population.

Leveraging sex-genetic interactions to understand brain disorders: recent advances and current gaps

It is established that there are sex differences in terms of prevalence, age of onset, clinical manifestations, and response to treatment for a variety of brain disorders, including neurodevelopmental, psychiatric, and neurodegenerative disorders. Cohorts of increasing sample sizes with diverse data types collected, including genetic, transcriptomic and/or phenotypic data, are providing the building blocks to permit analytical designs to test for sex-biased genetic variant-trait associations, and for sex-biased transcriptional regulation. Such molecular assessments can contribute to our understanding of the manifested phenotypic differences between the sexes for brain disorders, offering the future possibility of delivering personalized therapy for females and males. With the intention of raising the profile of this field as a research priority, this review aims to shed light on the importance of investigating sex-genetic interactions for brain disorders, focusing on two areas: (i) variant-trait associations and (ii) transcriptomics (i.e. gene expression, transcript usage and regulation). We specifically discuss recent advances in the field, current gaps and provide considerations for future studies.

Shaping immunity: The influence of natural selection on population immune diversity

Humans exhibit considerable variability in their immune responses to the same immune challenges. Such variation is widespread and affects individual and population-level susceptibility to infectious diseases and immune disorders. Although the factors influencing immune response diversity are partially understood, what mechanisms lead to the wide range of immune traits in healthy individuals remain largely unexplained. Here, we discuss the role that natural selection has played in driving phenotypic differences in immune responses across populations and present-day susceptibility to immune-related disorders. Further, we touch on future directions in the field of immunogenomics, highlighting the value of expanding this work to human populations globally, the utility of modeling the immune response as a dynamic process, and the importance of considering the potential polygenic nature of natural selection. Identifying loci acted upon by evolution may further pinpoint variants critically involved in disease etiology, and designing studies to capture these effects will enrich our understanding of the genetic contributions to immunity and immune dysregulation.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES) data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Minimum entropy framework identifies a novel class of genomic functional elements and reveals regulatory mechanisms at human disease loci

We introduce CoRE-BED, a framework trained using 19 epigenomic features in 33 major cell and tissue types to predict cell-type-specific regulatory function. CoRE-BED identifies nine functional classes de-novo, capturing both known and new regulatory categories. Notably, we describe a previously undercharacterized class that we term Development Associated Elements (DAEs), which are highly enriched in cell types with elevated regenerative potential and distinguished by the dual presence of either H3K4me2 and H3K9ac (an epigenetic signature associated with kinetochore assembly) or H3K79me3 and H4K20me1 (a signature associated with transcriptional pause release). Unlike bivalent promoters, which represent a transitory state between active and silenced promoters, DAEs transition directly to or from a non-functional state during stem cell differentiation and are proximal to highly expressed genes. CoRE-BED's interpretability facilitates causal inference and functional prioritization. Across 70 complex traits, distal insulators account for the largest mean proportion of SNP heritability (~49%) captured by the GWAS. Collectively, our results demonstrate the value of exploring non-conventional ways of regulatory classification that enrich for trait heritability, to complement existing approaches for cis-regulatory prediction.

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

SparsePro: An efficient fine-mapping method integrating summary statistics and functional annotations

Identifying causal variants from genome-wide association studies (GWAS) is challenging due to widespread linkage disequilibrium (LD) and the possible existence of multiple causal variants in the same genomic locus. Functional annotations of the genome may help to prioritize variants that are biologically relevant and thus improve fine-mapping of GWAS results. Classical fine-mapping methods conducting an exhaustive search of variant-level causal configurations have a high computational cost, especially when the underlying genetic architecture and LD patterns are complex. SuSiE provided an iterative Bayesian stepwise selection algorithm for efficient fine-mapping. In this work, we build connections between SuSiE and a paired mean field variational inference algorithm through the implementation of a sparse projection, and propose effective strategies for estimating hyperparameters and summarizing posterior probabilities. Moreover, we incorporate functional annotations into fine-mapping by jointly estimating enrichment weights to derive functionally-informed priors. We evaluate the performance of SparsePro through extensive simulations using resources from the UK Biobank. Compared to state-of-the-art methods, SparsePro achieved improved power for fine-mapping with reduced computation time. We demonstrate the utility of SparsePro through fine-mapping of five functional biomarkers of clinically relevant phenotypes. In summary, we have developed an efficient fine-mapping method for integrating summary statistics and functional annotations. Our method can have wide utility in understanding the genetics of complex traits and increasing the yield of functional follow-up studies of GWAS. SparsePro software is available on GitHub at https://github.com/zhwm/SparsePro.

Racial disparity in the genomics of precision oncology of prostate cancer

BACKGROUND

Significant racial disparities in prostate cancer incidence and mortality have been reported between African American Men (AAM), who are at increased risk for prostate cancer, and European American Men (EAM). In most of the studies carried out on prostate cancer, this population is underrepresented. With the advancement of genome-wide association studies, several genetic predictor models of prostate cancer risk have been elaborated, as well as numerous studies that identify both germline and somatic mutations with clinical utility.

RECENT FINDINGS

Despite significant advances, the AAM population continues to be underrepresented in genomic studies, which can limit generalizability and potentially widen disparities. Here we outline racial disparities in currently available genomic applications that are used to estimate the risk of individuals developing prostate cancer and to identify personalized oncology treatment strategies. While the incidence and mortality of prostate cancer are different between AAM and EAM, samples from AAM remain to be unrepresented in different studies.

CONCLUSION

This disparity impacts the available genomic data on prostate cancer. As a result, the disparity can limit the predictive utility of the genomic applications and may lead to the widening of the existing disparities. More studies with substantially higher recruitment and engagement of African American patients are necessary to overcome this disparity.

Evidence of causality of low body mass index on risk of adolescent idiopathic scoliosis: a Mendelian randomization study

Introduction

Adolescent idiopathic scoliosis (AIS) is a disorder with a three-dimensional spinal deformity and is a common disease affecting 1-5% of adolescents. AIS is also known as a complex disease involved in environmental and genetic factors. A relation between AIS and body mass index (BMI) has been epidemiologically and genetically suggested. However, the causal relationship between AIS and BMI remains to be elucidated.

Material and methods

Mendelian randomization (MR) analysis was performed using summary statistics from genome-wide association studies (GWASs) of AIS (Japanese cohort, 5,327 cases, 73,884 controls; US cohort: 1,468 cases, 20,158 controls) and BMI (Biobank Japan: 173430 individual; meta-analysis of genetic investigation of anthropometric traits and UK Biobank: 806334 individuals; European Children cohort: 39620 individuals; Population Architecture using Genomics and Epidemiology: 49335 individuals). In MR analyses evaluating the effect of BMI on AIS, the association between BMI and AIS summary statistics was evaluated using the inverse-variance weighted (IVW) method, weighted median method, and Egger regression (MR-Egger) methods in Japanese.

Results

Significant causality of genetically decreased BMI on risk of AIS was estimated: IVW method (Estimate (beta) [SE] = -0.56 [0.16], p = 1.8 × 10-3), weighted median method (beta = -0.56 [0.18], p = 8.5 × 10-3) and MR-Egger method (beta = -1.50 [0.43], p = 4.7 × 10-3), respectively. Consistent results were also observed when using the US AIS summary statistic in three MR methods; however, no significant causality was observed when evaluating the effect of AIS on BMI.

Conclusions

Our Mendelian randomization analysis using large studies of AIS and GWAS for BMI summary statistics revealed that genetic variants contributing to low BMI have a causal effect on the onset of AIS. This result was consistent with those of epidemiological studies and would contribute to the early detection of AIS.

Genetic variation in chromatin state across multiple tissues in Drosophila melanogaster

We use ATAC-seq to examine chromatin accessibility for four different tissues in Drosophila melanogaster: adult female brain, ovaries, and both wing and eye-antennal imaginal discs from males. Each tissue is assayed in eight different inbred strain genetic backgrounds, seven associated with a reference quality genome assembly. We develop a method for the quantile normalization of ATAC-seq fragments and test for differences in coverage among genotypes, tissues, and their interaction at 44099 peaks throughout the euchromatic genome. For the strains with reference quality genome assemblies, we correct ATAC-seq profiles for read mis-mapping due to nearby polymorphic structural variants (SVs). Comparing coverage among genotypes without accounting for SVs results in a highly elevated rate (55%) of identifying false positive differences in chromatin state between genotypes. After SV correction, we identify 1050, 30383, and 4508 regions whose peak heights are polymorphic among genotypes, among tissues, or exhibit genotype-by-tissue interactions, respectively. Finally, we identify 3988 candidate causative variants that explain at least 80% of the variance in chromatin state at nearby ATAC-seq peaks.

The genomics of visuospatial neurocognition in obsessive-compulsive disorder: A preliminary GWAS

BACKGROUND

The study of Obsessive-Compulsive Disorder (OCD) genomics has primarily been tackled by Genome-wide association studies (GWAS), which have encountered troubles in identifying replicable single nucleotide polymorphisms (SNPs). Endophenotypes have emerged as a promising avenue of study in trying to elucidate the genomic bases of complex traits such as OCD.

METHODS

We analyzed the association of SNPs across the whole genome with the construction of visuospatial information and executive performance through four neurocognitive variables assessed by the Rey-Osterrieth Complex Figure Test (ROCFT) in a sample of 133 OCD probands. Analyses were performed at SNP- and gene-level.

RESULTS

No SNP reached genome-wide significance, although there was one SNP almost reaching significant association with copy organization (rs60360940; P = 9.98E-08). Suggestive signals were found for the four variables at both SNP- (P < 1E-05) and gene-levels (P < 1E-04). Most of the suggestive signals pointed to genes and genomic regions previously associated with neurological function and neuropsychological traits.

LIMITATIONS

Our main limitations were the sample size, which was limited to identify associated signals at a genome-wide level, and the composition of the sample, more representative of rather severe OCD cases than a population-based OCD sample with a broad severity spectrum.

CONCLUSIONS

Our results suggest that studying neurocognitive variables in GWAS would be more informative on the genetic basis of OCD than the classical case/control GWAS, facilitating the genetic characterization of OCD and its different clinical profiles, the development of individualized treatment approaches, and the improvement of prognosis and treatment response.

Genome-Wide Association Study of Growth and Sex Traits Provides Insight into Heritable Mechanisms Underlying Growth Development of Macrobrachium nipponense (Oriental River Prawn)

Male hybrid oriental river prawns grow significantly faster than hybrid females. In this study, the growth and sex traits of 181 individuals of Macrobrachium nipponense were recorded, and each individual genotype was evaluated using the 2b-RAD sequencing method. The genetic parameters for growth and sex traits were estimated. A genome-wide association analysis (GWAS) of these traits was performed. In total, 18 growth-related SNPs were detected from 12 chromosomes using a mixed linear model. The most significant loci of weight are located on the position of the SNP (102638935, chromosome 13), which can explain 11.87% of the phenotypic variation. A total of 11 significant SNPs were detected on four chromosomes associated with sex trait (three on chromosome 4, one on chromosome 7 and seven on chromosome 17). The heritability of this trait is 0.8998 and belongs to the range of ultra-high heritability. Genetic correlations were prevalent among the 11 traits examined, the genetic coefficient between sex and body weight reached a significant level of −0.23. This study is the first GWAS for sex of binary and growth traits in oriental river prawn. Our results provide a set of markers for the genetic selection of growth traits and help us to further understand the genetic mechanisms of growth in Macrobrachium nipponense.

Neurodegenerative disorders: From clinicopathology convergence to systems biology divergence

Neurodegenerative diseases are multifactorial. This means that several genetic, epigenetic, and environmental factors contribute to their emergence. Therefore, for the future management of these highly prevalent diseases, it is necessary to change perspective. If a holistic viewpoint is assumed, the phenotype (the clinicopathological convergence) emerges from the perturbation of a complex system of functional interactions among proteins (systems biology divergence). The systems biology top-down approach starts with the unbiased collection of sets of data generated through one or more -omics techniques and has the aim to identify the networks and the components that participate in the generation of a phenotype (disease), often without any available a priori knowledge. The principle behind the top-down method is that the molecular components that respond similarly to experimental perturbations are somehow functionally related. This allows the study of complex and relatively poorly characterized diseases without requiring extensive knowledge of the processes under investigation. In this chapter, the use of a global approach will be applied to the comprehension of neurodegeneration, with a particular focus on the two most prevalent ones, Alzheimer's and Parkinson's diseases. The final purpose is to distinguish disease subtypes (even with similar clinical manifestations) to launch a future of precision medicine for patients with these disorders.

Unraveling Pathophysiology of Takotsubo Syndrome: The Emerging Role of the Oxidative Stress's Systemic Status

Takotsubo Syndrome (TTS) is usually triggered by emotional or physical stressors, thus suggesting that an increased sympathetic activity, leading to myocardial perfusion abnormalities and ventricular dysfunction, plays a major pathogenetic role. However, it remains to be elucidated why severe emotional and physical stress might trigger TTS in certain individuals but not others. Clinical research has been focused mainly on mechanisms underlying the activation of the sympathetic nervous system and the occurrence of myocardial ischemia in TTS. However, scientific evidence shows that additional factors might play a pathophysiologic role in the condition's occurrence. In this regard, a significant contribution arrived from metabolomics studies that followed the systemic response to TTS. Specifically, preliminary data clearly show that there is an interplay between inflammation, genetics, and oxidative status which might explain susceptibility to the condition. This review aims to sum up the established pathogenetic factors underlying TTS and to appraise emerging mechanisms, with particular emphasis on oxidative status, which might better explain susceptibility to the condition.

Predicting Eye and Hair Color in a Turkish Population Using the HIrisPlex System

Forensic DNA Phenotyping (FDP) can reveal the appearance of an unknown individual by predicting the ancestry, phenotype (i.e., hair, eye, skin color), and age from DNA obtained at the crime scene. The HIrisPlex system has been developed to simultaneously predict eye and hair color. However, the prediction accuracy of the system needs to be assessed for the tested population before implementing FDP in casework. In this study, we evaluated the performance of the HIrisPlex system on 149 individuals from the Turkish population. We applied the single-based extension (SNaPshot chemistry) method and used the HIrisPlex online tool to test the prediction of the eye and hair colors. The accuracy of the HIrisPlex system was assessed through the calculation of the area under the receiver characteristic operating curves (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The results showed that the proposed method successfully predicted the eye and hair color, especially for blue (100%) and brown (95.60%) eye and black (95.23) and brown (98.94) hair colors. As observed in previous studies, the system failed to predict intermediate eye color, representing 25% in our cohort. The majority of incorrect predictions were observed for blond hair color (40.7%). Previous HIrisPlex studies have also noted difficulties with these phenotypes. Our study shows that the HIrisPlex system can be applied to forensic casework in Turkey with careful interpretation of the data, particularly intermediate eye color and blond hair color.

Genome-wide association mapping of ethanol sensitivity in the Diversity Outbred mouse population

BACKGROUND

A strong predictor for the development of alcohol use disorder (AUD) is altered sensitivity to the intoxicating effects of alcohol. Individual differences in the initial sensitivity to alcohol are controlled in part by genetic factors. Mice offer a powerful tool to elucidate the genetic basis of behavioral and physiological traits relevant to AUD, but conventional experimental crosses have only been able to identify large chromosomal regions rather than specific genes. Genetically diverse, highly recombinant mouse populations make it possible to observe a wider range of phenotypic variation, offer greater mapping precision, and thus increase the potential for efficient gene identification.

METHODS

We have taken advantage of the Diversity Outbred (DO) mouse population to identify and precisely map quantitative trait loci (QTL) associated with ethanol sensitivity. We phenotyped 798 male J:DO mice for three measures of ethanol sensitivity: ataxia, hypothermia, and loss of the righting response. We used high-density MegaMUGA and GigaMUGA to obtain genotypes ranging from 77,808 to 143,259 SNPs. We also performed RNA sequencing in striatum to map expression QTLs and identify gene expression-trait correlations. We then applied a systems genetic strategy to identify narrow QTLs and construct the network of correlations that exists between DNA sequence, gene expression values, and ethanol-related phenotypes to prioritize our list of positional candidate genes.

RESULTS

We observed large amounts of phenotypic variation with the DO population and identified suggestive and significant QTLs associated with ethanol sensitivity on chromosomes 1, 2, and 16. The implicated regions were narrow (4.5-6.9 Mb in size) and each QTL explained ~4-5% of the variance.

CONCLUSIONS

Our results can be used to identify alleles that contribute to AUD in humans, elucidate causative biological mechanisms, or assist in the development of novel therapeutic interventions.

Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts

Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.

Identification of genetic loci associated with rough dwarf disease resistance in maize by integrating GWAS and linkage mapping

Maize rough dwarf disease (MRDD) is a viral disease that causes substantial yield loss, especially in China's summer planted maize area. Discovery of resistance genes would help in developing high-yielding resistant maize hybrids. Genome-wide association studies (GWASs) have advanced quickly and are now a powerful tool for dissecting complex genetic architectures. In this study, the disease severity index (DSI) of 292 maize inbred lines and an F₆ linkage population were investigated across multiple environments for two years. Using the genotypes obtained from the Maize SNP 50K chip, a GWAS was performed with four analytical models. The results showed that 22 SNPs distributed on chromosomes 1, 3, 4, 6, 7 and 8 were significantly associated with resistance to MRDD (P<0.0001). The SNPs on chromosomes 3, 6 and 8 were consistent with the quantitative trait locus (QTL) regions from linkage mapping in an RIL population. Candidate genes identified by GWAS included an LRR receptor-like serine/threonine-protein kinase (GRMZM2G141288), and a DRE-binding protein (GRMZM2G006745). In addition, we performed an allele variation analysis of the SNP loci selected by GWAS and linkage mapping and found that the main alleles of the two SNP loci PZE_101170408 and PZE_106082685 on chromosome 1 differed in terms of disease-resistant materials and disease-susceptible materials. The identified SNPs and genes provide useful information for MRDD-related gene cloning and insights on the underlying disease resistance mechanisms, and they can be used in marker-assisted breeding to develop MRDD-resistant maize.

Developments in forensic DNA analysis

The analysis of DNA from biological evidence recovered in the course of criminal investigations can provide very powerful evidence when a recovered profile matches one found on a DNA database or generated from a suspect. However, when no profile match is found, when the amount of DNA in a sample is too low, or the DNA too degraded to be analysed, traditional STR profiling may be of limited value. The rapidly expanding field of forensic genetics has introduced various novel methodologies that enable the analysis of challenging forensic samples, and that can generate intelligence about the donor of a biological sample. This article reviews some of the most important recent advances in the field, including the application of massively parallel sequencing to the analysis of STRs and other marker types, advancements in DNA mixture interpretation, particularly the use of probabilistic genotyping methods, the profiling of different RNA types for the identification of body fluids, the interrogation of SNP markers for predicting forensically relevant phenotypes, epigenetics and the analysis of DNA methylation to determine tissue type and estimate age, and the emerging field of forensic genetic genealogy. A key challenge will be for researchers to consider carefully how these innovations can be implemented into forensic practice to ensure their potential benefits are maximised.

Packpred: Predicting the Functional Effect of Missense Mutations

Predicting the functional consequences of single point mutations has relevance to protein function annotation and to clinical analysis/diagnosis. We developed and tested Packpred that makes use of a multi-body clique statistical potential in combination with a depth-dependent amino acid substitution matrix (FADHM) and positional Shannon entropy to predict the functional consequences of point mutations in proteins. Parameters were trained over a saturation mutagenesis data set of T4-lysozyme (1,966 mutations). The method was tested over another saturation mutagenesis data set (CcdB; 1,534 mutations) and the Missense3D data set (4,099 mutations). The performance of Packpred was compared against those of six other contemporary methods. With MCC values of 0.42, 0.47, and 0.36 on the training and testing data sets, respectively, Packpred outperforms all methods in all data sets, with the exception of marginally underperforming in comparison to FADHM in the CcdB data set. A meta server analysis was performed that chose best performing methods of wild-type amino acids and for wild-type mutant amino acid pairs. This led to an increase in the MCC value of 0.40 and 0.51 for the two meta predictors, respectively, on the Missense3D data set. We conjecture that it is possible to improve accuracy with better meta predictors as among the seven methods compared, at least one method or another is able to correctly predict ∼99% of the data.

Natural genetic variation in Drosophila melanogaster reveals genes associated with Coxiella burnetii infection

The gram-negative bacterium Coxiella burnetii is the causative agent of Query (Q) fever in humans and coxiellosis in livestock. Host genetics are associated with C. burnetii pathogenesis both in humans and animals; however, it remains unknown if specific genes are associated with severity of infection. We employed the Drosophila Genetics Reference Panel to perform a genome-wide association study to identify host genetic variants that affect host survival to C. burnetii infection. The genome-wide association study identified 64 unique variants (P < 10⁻⁵) associated with 25 candidate genes. We examined the role each candidate gene contributes to host survival during C. burnetii infection using flies carrying a null mutation or RNAi knockdown of each candidate. We validated 15 of the 25 candidate genes using at least one method. This is the first report establishing involvement of many of these genes or their homologs with C. burnetii susceptibility in any system. Among the validated genes, FER and tara play roles in the JAK/STAT, JNK, and decapentaplegic/TGF-β signaling pathways which are components of known innate immune responses to C. burnetii infection. CG42673 and DIP-ε play roles in bacterial infection and synaptic signaling but have no previous association with C. burnetii pathogenesis. Furthermore, since the mammalian ortholog of CG13404 (PLGRKT) is an important regulator of macrophage function, CG13404 could play a role in host susceptibility to C. burnetii through hemocyte regulation. These insights provide a foundation for further investigation regarding the genetics of C. burnetii susceptibility across a wide variety of hosts.

Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity

More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney-specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development.

PIP-SNP: a pipeline for processing SNP data featured as linkage disequilibrium bin mapping, genotype imputing and marker synthesizing

Genome-wide association study data analyses often face two significant challenges: (i) high dimensionality of single-nucleotide polymorphism (SNP) genotypes and (ii) imputation of missing values. SNPs are not independent due to physical linkage and natural selection. The correlation of nearby SNPs is known as linkage disequilibrium (LD), which can be used for LD conceptual SNP bin mapping, missing genotype inferencing and SNP dimension reduction. We used a stochastic process to describe the SNP signals and proposed two types of autocorrelations to measure nearby SNPs' information redundancy. Based on the calculated autocorrelation coefficients, we constructed LD bins. We adopted a k-nearest neighbors algorithm (kNN) to impute the missing genotypes. We proposed several novel methods to find the optimal synthetic marker to represent the SNP bin. We also proposed methods to evaluate the information loss or information conservation between using the original genome-wide markers and using dimension-reduced synthetic markers. Our performance assessments on the real-life SNP data from a rice recombinant inbred line (RIL) population and a rice HapMap project show that the new methods produce satisfactory results. We implemented these functional modules in C/C++ and streamlined them into a web-based pipeline named PIP-SNP (https://bioinfo.noble.org/PIP_SNP/) for processing SNP data.

Evolutionary Changes in Pathways and Networks of Genes Expressed in the Brains of Humans and Macaques

As the key organ that separates humans from nonhuman primates, the brain has continuously evolved to adapt to environmental and climatic changes. Although humans share most genetic, molecular, and cellular features with other primates such as macaques, there are significant differences in the structure and function of the brain between humans and these species. Thus, exploring the differences between the brains of human and nonhuman primates in the context of evolution will provide insights into the development, functionality, and diseases of the human central nervous system (CNS). Since the genes involved in many aspects of the human brain are under common pressures of natural selection, their evolutionary features can be analyzed collectively at the pathway level. In this study, the molecular mechanisms underlying human brain capabilities were explored by comparing the evolution features of pathways enriched in genes expressed in the human brain and the macaque brain. We identified 31 pathways with differential evolutionary properties, including those related to neurological diseases, signal transduction, immunological response, and metabolic processes. By analyzing genes differentially expressed in brain regions or development stages between humans and macaques,  9 and 4 pathways with differential evolutionary properties were detected, respectively. We further performed crosstalk analysis on the pathways to obtain an intuitive correlation between the pathways, which is helpful in understanding the mechanisms of interaction between pathways. Our results provide on a comprehensive view of the evolutionary pathways of the human CNS and can serve as a reference for the study of human brain development.

Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

Background

Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer’s disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population.

Methods

One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs.

Results

In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10⁻⁸). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74–0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain.

Conclusion

The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-021-00854-z.

A Variant Noncoding Region Regulates Prrx1 and Predisposes to Atrial Arrhythmias

[Figure: see text].

Ethanol-Related Behaviors in Mouse Lines Selectively Bred for Drinking to Intoxication

Alcohol use disorder (AUD) is a devastating psychiatric disorder that has significant wide-reaching effects on individuals and society. Selectively bred mouse lines are an effective means of exploring the genetic and neuronal mechanisms underlying AUD and such studies are translationally important for identifying treatment options. Here, we report on behavioral characterization of two replicate lines of mice that drink to intoxication, the High Drinking in the Dark (HDID)-1 and -2 mice, which have been selectively bred (20+ generations) for the primary phenotype of reaching high blood alcohol levels (BALs) during the drinking in the dark (DID) task, a binge-like drinking assay. Along with their genetically heterogenous progenitor line, Hs/Npt, we tested these mice on: DID and drinking in the light (DIL); temporal drinking patterns; ethanol sensitivity, through loss of righting reflex (LORR); and operant self-administration, including fixed ratio (FR1), fixed ratio 3:1 (FR3), extinction/reinstatement, and progressive ratio (PR). All mice consumed more ethanol during the dark than the light and both HDID lines consumed more ethanol than Hs/Npt during DIL and DID. In the dark, we found that the HDID lines achieved high blood alcohol levels early into a drinking session, suggesting that they exhibit front loading like drinking behavior in the absence of the chronicity usually required for such behavior. Surprisingly, HDID-1 (female and male) and HDID-2 (male) mice were more sensitive to the intoxicating effects of ethanol during the dark (as determined by LORR), while Hs/Npt (female and male) and HDID-2 (female) mice appeared less sensitive. We observed lower HDID-1 ethanol intake compared to either HDID-2 or Hs/Npt during operant ethanol self-administration. There were no genotype differences for either progressive ratio responding, or cue-induced ethanol reinstatement, though the latter is complicated by a lack of extinguished responding behavior. Taken together, these findings suggest that genes affecting one AUD-related behavior do not necessarily affect other AUD-related behaviors. Moreover, these findings highlight that alcohol-related behaviors can also differ between lines selectively bred for the same phenotype, and even between sexes within those same line.

The correlation of salivary telomere length and single nucleotide polymorphisms of the ADIPOQ, SIRT1 and FOXO3A genes with lifestyle-related diseases in a Japanese population

Background

It has been reported that genetic factors are associated with risk factors and onset of lifestyle-related diseases, but this finding is still the subject of much debate.

Objective

The aim of the present study was to investigate the correlation of genetic factors, including salivary telomere length and three single nucleotide polymorphisms (SNPs) that may influence lifestyle-related diseases, with lifestyle-related diseases themselves.

Methods

In one year at a single facility, relative telomere length and SNPs were determined by using monochrome multiplex quantitative polymerase chain reaction and TaqMan SNP Genotyping Assays, respectively, and were compared with lifestyle-related diseases in 120 Japanese individuals near our university.

Results

In men and all participants, age was inversely correlated with relative telomere length with respective p values of 0.049 and 0.034. In men, the frequency of hypertension was significantly higher in the short relative telomere length group than in the long group with unadjusted p value of 0.039, and the difference in the frequency of hypertension between the two groups was of borderline statistical significance after adjustment for age (p = 0.057). Furthermore, in men and all participants, the sum of the number of affected lifestyle-related diseases, including hypertension, was significantly higher in the short relative telomere length group than in the long group, with p values of 0.004 and 0.029, respectively. For ADIPOQ rs1501299, men’s ankle brachial index was higher in the T/T genotype than in the G/G and G/T genotypes, with p values of 0.001 and 0.000, respectively. For SIRT1 rs7895833, men’s body mass index and waist circumference and all participants’ brachial-ankle pulse wave velocity were higher in the A/G genotype than in the G/G genotype, with respective p values of 0.048, 0.032 and 0.035. For FOXO3A rs2802292, women’s body temperature and all participants’ saturation of peripheral oxygen were lower in the G/T genotype than in the T/T genotype, with respective p values of 0.039 and 0.032. However, relative telomere length was not associated with physiological or anthropometric measurements except for height in men (p = 0.016). ADIPOQ rs1501299 in men, but not the other two SNPs, was significantly associated with the sum of the number of affected lifestyle-related diseases (p = 0.013), by genotype. For each SNPs, there was no significant difference in the frequency of hypertension or relative telomere length by genotype.

Conclusion

Relative telomere length and the three types of SNPs determined using saliva have been shown to be differentially associated with onset of and measured risk factors for lifestyle-related diseases consisting mainly of cardiovascular diseases and cancer.

Decoding Protein-protein Interactions: An Overview

Drug discovery has focused on the paradigm "one drug, one target" for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Genetic approaches toward understanding the individual variation in cardiac structure, function and responses to exercise training

Cardiovascular disease (CVD) accounts for approximately 30% of all deaths worldwide and its prevalence is constantly increasing despite advancements in medical treatments. Cardiac remodeling and dysfunction are independent risk factors for CVD. Recent studies have demonstrated that cardiac structure and function are genetically influenced, suggesting that understanding the genetic basis for cardiac structure and function could provide new insights into developing novel therapeutic targets for CVD. Regular exercise has long been considered a robust non-therapeutic method of treating or preventing CVD. However, recent studies also indicate that there is inter-individual variation in response to exercise. Nevertheless, the genetic basis for cardiac structure and function as well as their responses to exercise training have yet to be fully elucidated. Therefore, this review summarizes accumulated evidence supporting the genetic contribution to these traits, including findings from population-based studies and unbiased large genomic-scale studies in humans.

Genetic and Non-genetic Determinants of Cardiovascular Disease in South Asians

South Asians (SAs), people from the Indian subcontinent (e.g., India, Pakistan, Bangladesh, Sri Lanka, and Nepal) have a higher prevalence of cardiovascular disease (CVD) and suffer from a greater risk of CVD-associated mortality compared to other global populations. These problems are compounded by the alterations in lifestyles due to urbanization and changing cultural, social, economic, and political environments. Current methods of CV risk prediction are based on white populations that under-estimate the CVD risk in SAs. Prospective studies are required to obtain actual CVD morbidity/mortality rates so that comparisons between predicted CVD risk can be made with actual events. Overwhelming data support a strong influence of genetic factors. Genome-Wide Association Studies (GWAS) serve as a starting point for future genetic and functional studies since the mechanisms of action by which these associated loci influence CVD is still unclear. It is difficult to predict the potential implication of these findings in clinical settings. This review provides a systematic assessment of the risk factors, genetics, and environmental causes of CV health disparity in SAs, and highlights progress made in clinical and genomics discoveries in the rapidly evolving field, which has the potential to show clinical relevance in the near future.

Applying an association weight matrix in weighted genomic prediction of boar taint compounds

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome-wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome-wide association studies (GWAS) in data set containing single- (SL) or a multi-line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM-WssGBLUP). The single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work.

Quantifying simultaneous innovations in evolutionary medicine

To what extent do simultaneous innovations occur and are independently from each other? In this paper we use a novel persistent keyword framework to systematically identify innovations in a large corpus containing academic papers in evolutionary medicine between 2007 and 2011. We examine whether innovative papers occurring simultaneously are independent from each other by evaluating the citation and co-authorship information gathered from the corpus metadata. We find that 19 out of 22 simultaneous innovative papers do, in fact, occur independently from each other. In particular, co-authors of simultaneous innovative papers are no more geographically concentrated than the co-authors of similar non-innovative papers in the field. Our result suggests producing innovative work draws from a collective knowledge pool, rather than from knowledge circulating in distinct localized collaboration networks. Therefore, new ideas can appear at multiple locations and with geographically dispersed co-authorship networks. Our findings support the perspective that simultaneous innovations are the outcome of collective behavior.

Genomic Heritability and Genome-Wide Association Studies of Plasma Metabolites in Crossbred Beef Cattle

Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.

Functional annotation of melanoma risk loci identifies novel susceptibility genes

Genome-wide association study (GWAS)-identified single-nucleotide polymorphisms (SNPs) are tag SNPs located in both transcribed and non-coding regulatory DNA regions, rather than representing causal or functional variants for disease. To identify functional variants or genes for melanoma susceptibility, we used functional mapping and annotation (FUMA) to perform functional annotation of the summary statistics of 2541 significant melanoma risk SNPs (P < 5 × 10-8) identified by GWAS. The original GWAS melanoma study included 15 990 cases and 26 409 controls, representing the largest international meta-analysis of melanoma susceptibility. We prioritized 330 unique genes, including those in immune cytokine signaling pathways, from 19 loci through positional, expression quantitative trait locus, and chromatin interaction mapping. In comparison, only 38 melanoma-related genes were identified in the original meta-analysis. In addition to the well-known melanoma susceptibility genes confirmed in the meta-analysis (MC1R, CDKN2A, TERT, OCA2 and ARNT/SETDB1), we also identified additional novel genes using FUMA to map SNPs to genes. Through chromatin interaction mapping, we prioritized IFNA7, IFNA10, IFNA16, IFNA17, IFNA14, IFNA6, IFNA21, IFNA4, IFNE and IFNA5; these 10 most significant genes are all involved in immune system and cytokine signaling pathways. In the gene analysis, we identified 72 genes with a P < 2.5 × 10-6. The genes associated with melanoma risk were DEF8 (P = 1.09 × 10-57), DBNDD1 (P = 2.19 × 10-42), SPATA33 (P = 3.54 × 10-38) and MC1R (P = 1.04 × 10-36). In summary, this study identifies novel putative melanoma susceptibility genes and provides a guide for further experimental validation of functional variants and disease-related genes.

Alcohol Sensitivity as an Endophenotype of Alcohol Use Disorder: Exploring Its Translational Utility between Rodents and Humans

Alcohol use disorder (AUD) is a complex, chronic, relapsing disorder with multiple interacting genetic and environmental influences. Numerous studies have verified the influence of genetics on AUD, yet the underlying biological pathways remain unknown. One strategy to interrogate complex diseases is the use of endophenotypes, which deconstruct current diagnostic categories into component traits that may be more amenable to genetic research. In this review, we explore how an endophenotype such as sensitivity to alcohol can be used in conjunction with rodent models to provide mechanistic insights into AUD. We evaluate three alcohol sensitivity endophenotypes (stimulation, intoxication, and aversion) for their translatability across human and rodent research by examining the underlying neurobiology and its relationship to consumption and AUD. We show examples in which results gleaned from rodents are successfully integrated with information from human studies to gain insight in the genetic underpinnings of AUD and AUD-related endophenotypes. Finally, we identify areas for future translational research that could greatly expand our knowledge of the biological and molecular aspects of the transition to AUD with the broad hope of finding better ways to treat this devastating disorder.

Evaluation of the Genetic Variance of Alzheimer's Disease Explained by the Disease-Associated Chromosomal Regions

Heritability analysis of complex traits/diseases is commonly performed to obtain illustrative information about the potential contribution of the genetic factors to their phenotypic variances. In this study, we investigated the narrow-sense heritability (h2) of Alzheimer's disease (AD) using genome-wide single-nucleotide polymorphisms (SNPs) data from three independent studies in the linear mixed models framework. Our meta-analyses demonstrated that the estimated h2 values (and their standard errors) of AD in liability scale were 0.280 (0.091), 0.348 (0.113), and 0.389 (0.126) assuming AD prevalence rates of 10%, 20%, or 30% at ages of 65+, 75+, and 85+ years, respectively. We also found that chromosomal regions containing two or more AD-associated SNPs at p < 5E-08 could collectively explain 37% of the additive genetic variance of AD in our samples. AD-associated regions in which at least one SNP had attained p < 5E-08 explained 56% of the additive genetic variance of AD. These regions harbored 3% and 11% of SNPs in our analyses. Also, the chromosomal regions containing two or more and one or more AD-associated SNPs at p < 5E-06 accounted for 72% and 94% of the additive genetic variance of AD, respectively. These regions harbored 27% and 44% of SNPs in our analyses. Our findings showed that the overall contribution of the additive genetic effects to the AD liability was moderate and age-dependent. Also, they supported the importance of focusing on known AD-associated chromosomal regions to investigate the genetic basis of AD, e.g., through haplotype analysis, analysis of heterogeneity, and functional studies.

Inherited genetic variants associated with glucocorticoid sensitivity in leukaemia cells

Identification of genetic variants associated with glucocorticoids (GC) sensitivity of leukaemia cells may provide insight into potential drug targets and tailored therapy. In the present study, within 72 leukaemic cell lines derived from Japanese patients with B-cell precursor acute lymphoblastic leukaemia (ALL), we conducted genome-wide genotyping of single nucleotide polymorphisms (SNP) and attempted to identify genetic variants associated with GC sensitivity and NR3C1 (GC receptor) gene expression. IC50 measures for prednisolone (Pred) and dexamethasone (Dex) were available using an alamarBlue cell viability assay. IC50 values of Pred showed the strongest association with rs904419 (P = 4.34 × 10^-8 ), located between the FRMD4B and MITF genes. The median IC50 values of prednisolone for cell lines with rs904419 AA (n = 13), AG (n = 31) and GG (n = 28) genotypes were 0.089, 0.139 and 297 µmol/L, respectively. For dexamethasone sensitivity, suggestive association was observed for SNP rs2306888 (P = 1.43 × 10^-6 ), a synonymous SNP of the TGFBR3 gene. For NR3C1 gene expression, suggestive association was observed for SNP rs11982167 (P = 6.44 × 10^-8 ), located in the PLEKHA8 gene. These genetic variants may affect GC sensitivity of ALL cells and may give rise to opportunities in personalized medicine for effective and safe chemotherapy in ALL patients.

Is individual genetic susceptibility a link between silica exposure and development or severity of silicosis? A systematic review

BACKGROUND

Silicosis is a lung disease of fibrotic nature resulting from the inhalation and deposition of dust containing crystalline silica. Subjects exposed to the same environmental factors may show distinct radiological manifestations, and since silicosis is known as a multifactorial disease, it is plausible that individual genetic susceptibility may play a role in the pathology. This review of the literature aims to provide an assessment of the present data on the genetic association studies in silicosis and describe the genes that potentially might influence silicosis susceptibility in silica-exposed individuals.

METHODS

We accessed the database of PubMed for articles published in English about interindividual genetic susceptibility to silicosis using terms related to the subject matter.

RESULTS

Following the evaluation process, 28 studies were included in this systematic review, including 23 original studies and 5 meta-analyses.

CONCLUSIONS

Regardless of the advances in the knowledge of the importance of gene variations in silicosis, more studies need to be performed, in particular, special polygenic and genome-wide investigations.

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data

BACKGROUND

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

METHODS

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.

RESULTS

Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10^-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10^-12), and synaptic transmission (P = 6.28 × 10^-8).

CONCLUSIONS

Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data

BACKGROUND

We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data.

METHODS

We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided.

RESULTS

Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8).

CONCLUSIONS

Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.

Genetic variants beyond amyloid and tau associated with cognitive decline: A cohort study

OBJECTIVE

To identify single nucleotide polymorphisms (SNPs) associated with cognitive decline independent of β-amyloid (Aβ) and tau pathology in Alzheimer disease (AD).

METHODS

Discovery and replication datasets consisting of 414 individuals (94 cognitively normal control [CN], 185 with mild cognitive impairment [MCI], and 135 with AD) and 72 individuals (22 CN, 39 with MCI, and 11 with AD), respectively, were obtained from the Alzheimer's Disease Neuroimaging Initiative database. Genome-wide association analysis was conducted to identify SNPs associated with individual cognitive function (measured with the Mini-Mental State Examination and Alzheimer's Disease Assessment Scale-Cognitive Subscale ) while controlling for the level of Aβ and tau (measured as CSF phosphorylated-tau/Aβ_1-42). Gene ontology analysis was performed on SNP-associated genes.

RESULTS

We identified 1 significant (rs55906536, β = -1.91, standard error 0.34, p = 4.07 × 10^-8) and 4 suggestive variants on chromosome 6 that were associated with poorer cognitive function. Congruent results were found in the replication data. A structural equation model showed that the identified SNP deteriorated cognitive function partially through cortical thinning of the brain in a region-specific manner. Furthermore, a bioinformatics analysis showed that the identified SNPs were associated with genes related to glutathione metabolism.

CONCLUSIONS

In this study, we identified SNPs related to cognitive decline in a manner that could not be explained by Aβ and tau levels. Our findings provide insight into the complexity of AD pathogenesis and support the growing literature on the role of glutathione in AD.

Insulin Potentiates JAK/STAT Signaling to Broadly Inhibit Flavivirus Replication in Insect Vectors

The World Health Organization estimates that more than half of the world’s population is at risk for vector-borne diseases, including arboviruses. Because many arboviruses are mosquito borne, investigation of the insect immune response will help identify targets to reduce the spread of arboviruses. Here, we use a genetic screening approach to identify an insulin-like receptor as a component of the immune response to arboviral infection. We determine that vertebrate insulin reduces West Nile virus (WNV) replication in Drosophila melanogaster as well as WNV, Zika, and dengue virus titers in mosquito cells. Mechanistically, we show that insulin signaling activates the JAK/STAT, but not RNAi, pathway via ERK to control infection in Drosophila cells and Culex mosquitoes through an integrated immune response. Finally, we validate that insulin priming of adult female Culex mosquitoes through a blood meal reduces WNV infection, demonstrating an essential role for insulin signaling in insect antiviral responses to human pathogens.

The world’s population is at risk for infection with several flaviviruses. Ahlers et al. use a living library of insects to determine that an insulin-like receptor controls West Nile virus infection. Insulin signaling is antiviral via the JAK/STAT pathway in both fly and mosquito models and against a range of flaviviruses.

Protein-Protein interactions uncover candidate 'core genes' within omnigenic disease networks

Genome wide association studies (GWAS) of human diseases have generally identified many loci associated with risk with relatively small effect sizes. The omnigenic model attempts to explain this observation by suggesting that diseases can be thought of as networks, where genes with direct involvement in disease-relevant biological pathways are named ‘core genes’, while peripheral genes influence disease risk via their interactions or regulatory effects on core genes. Here, we demonstrate a method for identifying candidate core genes solely from genes in or near disease-associated SNPs (GWAS hits) in conjunction with protein-protein interaction network data. Applied to 1,381 GWAS studies from 5 ancestries, we identify a total of 1,865 candidate core genes in 343 GWAS studies. Our analysis identifies several well-known disease-related genes that are not identified by GWAS, including BRCA1 in Breast Cancer, Amyloid Precursor Protein (APP) in Alzheimer’s Disease, INS in A1C measurement and Type 2 Diabetes, and PCSK9 in LDL cholesterol, amongst others. Notably candidate core genes are preferentially enriched for disease relevance over GWAS hits and are enriched for both Clinvar pathogenic variants and known drug targets—consistent with the predictions of the omnigenic model. We subsequently use parent term annotations provided by the GWAS catalog, to merge related GWAS studies and identify candidate core genes in over-arching disease processes such as cancer–where we identify 109 candidate core genes.

A recent theory suggests that only a small number of genes underpin the biology of a disease, these genes are called ‘core genes’, and for most diseases, these core genes remain unknown. The suggested methods for finding them requires complex and expensive experiments. We reasoned that if we merge currently available datasets in smart ways, we may be able to uncover these ‘core genes’. Our method finds “hub” proteins by merging lists of genes previously linked with disease to information on how proteins interact with each other. We found that many of these hub proteins have central roles in disease, such as insulin for both A1C measurement and Type 2 Diabetes, BRCA1 in Breast cancer, and Amyloid Precursor Protein in Alzheimer’s Disease. We think these ‘hub’ proteins are candidate ‘core genes’, and offer our method as a way to find ‘core genes’ by utilizing publicly available reference datasets.

Quantitative genome-wide association study of six phenotypic subdomains identifies novel genome-wide significant variants in autism spectrum disorder

Autism spectrum disorders (ASD) are highly heritable and are characterized by deficits in social communication and restricted and repetitive behaviors. Twin studies on phenotypic subdomains suggest a differing underlying genetic etiology. Studying genetic variation explaining phenotypic variance will help to identify specific underlying pathomechanisms. We investigated the effect of common variation on ASD subdomains in two cohorts including >2500 individuals. Based on the Autism Diagnostic Interview-Revised (ADI-R), we identified and confirmed six subdomains with a SNP-based genetic heritability h²_SNP = 0.2-0.4. The subdomains nonverbal communication (NVC), social interaction (SI), and peer interaction (PI) shared genetic risk factors, while the subdomains of repetitive sensory-motor behavior (RB) and restricted interests (RI) were genetically independent of each other. The polygenic risk score (PRS) for ASD as categorical diagnosis explained 2.3-3.3% of the variance of SI, joint attention (JA), and PI, 4.5% for RI, 1.2% of RB, but only 0.7% of NVC. We report eight genome-wide significant hits-partially replicating previous findings-and 292 known and novel candidate genes. The underlying biological mechanisms were related to neuronal transmission and development. At the SNP and gene level, all subdomains showed overlap, with the exception of RB. However, no overlap was observed at the functional level. In summary, the ADI-R algorithm-derived subdomains related to social communication show a shared genetic etiology in contrast to restricted and repetitive behaviors. The ASD-specific PRS overlapped only partially, suggesting an additional role of specific common variation in shaping the phenotypic expression of ASD subdomains.

Genetic variations in G-protein signal pathways influence progression of coronary artery calcification: Results from the Heinz Nixdorf Recall study

BACKGROUND AND AIMS

Coronary artery calcification (CAC) is one of the most sensitive and specific markers of coronary atherosclerosis and believed to be heritable. We hypothesized that functionally relevant single-nucleotide polymorphisms (SNPs) in the G-protein signal pathway, which have been previously related to coronary artery disease, are associated with CAC progression.

METHODS

3108 participants from the Heinz Nixdorf Recall study with CAC measurements at both baseline (CACb) and 5-year follow-up (CAC5y) were included. We genotyped SNPs rs1042714 (ADRB2), rs6026584 and rs12481583 (GNAS), and rs5443 (GNB3) and defined a priori risk alleles derived from literature data. Regression analyses were applied to measures of 5-year CAC progression, unadjusted, adjusted for age, sex, and adjusted for age, sex, log(CACb+1) as well as for cardiovascular risk factors.

RESULTS

The presence of one or more risk alleles was associated with a 26.9% (95% CI 5.5-52.4) increase in 5-year CAC progression (p = 0.011) and a 29.2% (95% CI 5.9-57.6) accelerated increase of CAC over the 5-year period compared to what was expected with respect to the baseline CAC percentile value (p = 0.012). Each of those risk alleles increased the 5-year CAC progression by 4.4% (95% CI 1.3-7.6, p = 0.006) and resulted in a 4.9% accelerated increase of CAC over the 5-year period (95% CI 1.6-8.4, p = 0.004). These unadjusted data did not change after adjustment.

CONCLUSIONS

Genetic variations in the G-protein signal pathway are associated with CAC progression in a cumulative fashion, indicating the importance of the pathway for genetic heritability in CAC progression and coronary artery disease.