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Gene-based association analysis is a powerful tool for identifying genes that explain trait variability. An essential step of this analysis is a conditional analysis. It aims to eliminate the influence of SNPs outside the gene, which are in linkage disequilibrium with intragenic SNPs. The popular conditional analysis method, GCTA-COJO, accounts for the influence of several top independently associated SNPs outside the gene, correcting the z statistics for intragenic SNPs. We suggest a new TauCOR method for conditional gene-based analysis using summary statistics. This method accounts the influence of the full regional polygenic background, correcting the genotype correlations between intragenic SNPs. As a result, the distribution of z statistics for intragenic SNPs becomes conditionally independent of distribution for extragenic SNPs. TauCOR is compatible with any gene-based association test. TauCOR was tested on summary statistics simulated under different scenarios and on real summary statistics for a 'gold standard' gene list from the Open Targets Genetics project. TauCOR proved to be effective in all modelling scenarios and on real data. The TauCOR's strategy showed comparable sensitivity and higher specificity and accuracy than GCTA-COJO on both simulated and real data. The method can be successfully used to improve the effectiveness of gene-based association analyses. [ABSTRACT FROM AUTHOR]

When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants are known for a gene but we do not know the correlations between genotypes of variants, one of which is imputed, and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK Biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT, and PCA methods on reconstructed and real matrices using modelled summary statistics and calculated summary statistics on real phenotypes showed high quality of reconstruction and robustness of the method to different gene structures. [ABSTRACT FROM AUTHOR]

Abstract: Back pain is the leading cause of years lived with disability worldwide, yet surprisingly, little is known regarding the biology underlying this condition. The impact of genetics is known for chronic back pain: its heritability is estimated to be at least 40%. Large genome-wide association studies have shown that common variation may account for up to 35% of chronic back pain heritability; rare variants may explain a portion of the heritability not explained by common variants. In this study, we performed the first gene-based association analysis of chronic back pain using UK Biobank imputed data including rare variants with moderate imputation quality. We discovered 2 genes, SOX5 and PANX3, influencing chronic back pain. The SOX5 gene is a well-known back pain gene. The PANX3 gene has not previously been described as having a role in chronic back pain. We showed that the association of PANX3 with chronic back pain is driven by rare noncoding intronic polymorphisms. This result was replicated in an independent sample from UK Biobank and validated using a similar phenotype, dorsalgia, from FinnGen Biobank. We also found that the PANX3 gene is associated with intervertebral disk disorders. We can speculate that a possible mechanism of action of PANX3 on back pain is due to its effect on the intervertebral disks. [ABSTRACT FROM AUTHOR]

Abstract Background Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear. Methods We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders. Results We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders. Conclusions Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10−6, PeMAGMA = 1.94 × 10−7 for whole blood), IL-6R (PFUSION = 8.63 × 10−7, PeMAGMA = 2.74 × 10−6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10−5, PeMAGMA = 1.08 × 10−6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10−7, PeMAGMA = 3.93 × 10−8 for Liver), LAT (PFUSION = 1.53 × 10−4, PeMAGMA = 4.62 × 10−7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10−5, PeMAGMA = 1.73 × 10−7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) patients display hippocampal atrophy, memory impairment, and cognitive decline. New neurons are generated throughout adulthood in 2 regions of the brain implicated in AD, the dentate gyrus of the hippocampus and the subventricular zone of the olfactory bulb. Disruption of this process contributes to neurodegenerative diseases including AD, and many of the molecular players in AD are also modulators of adult neurogenesis. However, the genetic mechanisms underlying adult neurogenesis in AD have been underexplored. To address this gap, we performed a gene-based association analysis in cognitively normal and impaired participants using neurogenesis pathway-related candidate genes curated from existing databases, literature mining, and large-scale genome-wide association study findings. A gene-based association analysis identified adenosine A2a receptor (ADORA2A) as significantly associated with hippocampal volume and the association between rs9608282 within ADORA2A and hippocampal volume was replicated in the meta-analysis after multiple comparison adjustments (p = 7.88 × 10-6). The minor allele of rs9608282 in ADORA2A is associated with larger hippocampal volumes and better memory.

Abstract Background Maize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding. Results In this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis. Conclusions Our results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection.

Background: Previous studies demonstrated a positive relationship between birthweight and breast cancer; however, inconsistent, sometimes even controversial, observations also emerged, and the nature of such relationship remains unknown.Methods: Using summary statistics of birthweight and breast cancer, we assessed the fetal/maternal-specific genetic correlation between them via LDSC and prioritized fetal/maternal-specific pleiotropic genes through MAIUP. Relying on summary statistics we conducted Mendelian randomization (MR) to evaluate the fetal/maternal-specific origin of causal relationship between birthweight, age of menarche, age at menopause and breast cancer.Results: With summary statistics we identified a positive genetic correlation between fetal-specific birthweight and breast cancer (rg = 0.123 and P = 0.013) as well as a negative but insignificant correlation between maternal-specific birthweight and breast cancer (rg = - 0.068, P = 0.206); and detected 84 pleiotropic genes shared by fetal-specific birthweight and breast cancer, 49 shared by maternal-specific birthweight and breast cancer. We also revealed fetal-specific birthweight indirectly influenced breast cancer risk in adulthood via the path of age of menarche or age at menopause in terms of MR-based mediation analysis.Conclusion: This study reveals that shared genetic foundation and causal mediation commonly drive the connection between the two traits, and that fetal/maternal-specific birthweight plays substantially distinct roles in such relationship. However, our work offers little supportive evidence for the fetal origins hypothesis of breast cancer originating in utero. [ABSTRACT FROM AUTHOR]

Genome‐wide association study (GWAS) have identified more than 300 single nucleotide polymorphisms at 163 independent loci associated with coronary artery disease (CAD). However, there is no full understanding about the causal genes for CAD and the mechanisms of their action. We aimed to perform a post GWAS analysis to identify genes whose polymorphism may influence the risk of CAD. Using the UK Biobank GWAS summary statistics, we performed a gene‐based association analysis. We found 63 genes significantly associated with CAD due to their within‐gene polymorphisms. Many of these genes are well known. Some known CAD genes such as FURIN and SORT1 did not show the gene‐based association because their variants had low GWAS signals or gene‐based association was inflated by the strong GWAS signal outside the gene. For several known CAD genes, we demonstrated that their effects could be explained not only or not at all by their own variants but by the variants within the neighboring genes controlling their expression. Using several bioinformatics techniques, we suggested potential mechanisms underlying gene‐CAD associations. Three genes, CDK19, NCALD, and ARHGEF12 were not previously associated with CAD. The role of these genes should be clarified in further studies. [ABSTRACT FROM AUTHOR]

Background: Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.Methods: We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.Results: We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.Conclusions: Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment. [ABSTRACT FROM AUTHOR]

To date, more than 100 loci associated with coronary artery disease (CAD) have been detected in large-scale genome-wide studies. For some of the several hundreds of genes located in these loci, roles in the pathogenesis of the disease have been shown. However, the genetic mechanisms and specific genes controlling this disease are still not fully understood. This study is aimed at in silico search for new CAD genes. We performed a gene-based association analysis, where all polymorphic variants within a gene are analyzed simultaneously. The analysis was based on the results of the genome-wide association studies (GWAS) available from the open databases MICAD (120,575 people, 85,112 markers) and UK Biobank (337,199 people, 10,894,597 markers). We used the sumFREGAT package implementing a wide range of new methods for gene-based association analysis using summary statistics. We found 88 genes demonstrating significant gene-based associations. Forty-four of the identified genes were already known as CAD genes. Furthermore, we identified 28 additional genes in the known CAD loci. They can be considered as new candidate genes. Finally, we identified sixteen new genes (AGPAT4, ARHGEF12, BDP1, DHX58, EHBP1, FBF1, HSPB9, NPBWR2, PDLIM5, PLCB3, PLEKHM2, POU2F3, PRKD2, TMEM136, TTC29 and UTP20) outside the known loci. Information about the functional role of these genes allows us to consider many of them as candidates for CAD. The 41 identified genes did not have significant GWAS signals and they were identified only due to simultaneous consideration of all variants within the gene in the framework of gene-based analysis. These results demonstrate that gene-based association analysis is a powerful tool for gene mapping. The method can utilize huge amounts of GWAS results accumulated in the world to map different traits and diseases. This type of studies is widely available, as it does not require additional material costs.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10−6, PeMAGMA = 1.94 × 10−7 for whole blood), IL-6R (PFUSION = 8.63 × 10−7, PeMAGMA = 2.74 × 10−6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10−5, PeMAGMA = 1.08 × 10−6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10−7, PeMAGMA = 3.93 × 10−8 for Liver), LAT (PFUSION = 1.53 × 10−4, PeMAGMA = 4.62 × 10−7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10−5, PeMAGMA = 1.73 × 10−7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

Abstract Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat (Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes.

Trehalose 6‐phosphate (T6P) signalling regulates carbon use and allocation and is a target to improve crop yields. However, the specific contributions of trehalose phosphate synthase (TPS) and trehalose phosphate phosphatase (TPP) genes to source‐ and sink‐related traits remain largely unknown. We used enrichment capture sequencing on TPS and TPP genes to estimate and partition the genetic variation of yield‐related traits in a spring wheat (Triticum aestivum) breeding panel specifically built to capture the diversity across the 75,000 CIMMYT wheat cultivar collection. Twelve phenotypes were correlated to variation in TPS and TPP genes including plant height and biomass (source), spikelets per spike, spike growth and grain filling traits (sink) which showed indications of both positive and negative gene selection. Individual genes explained proportions of heritability for biomass and grain‐related traits. Three TPS1 homologues were particularly significant for trait variation. Epistatic interactions were found within and between the TPS and TPP gene families for both plant height and grain‐related traits. Gene‐based prediction improved predictive ability for grain weight when gene effects were combined with the whole‐genome markers. Our study has generated a wealth of information on natural variation of TPS and TPP genes related to yield potential which confirms the role for T6P in resource allocation and in affecting traits such as grain number and size confirming other studies which now opens up the possibility of harnessing natural genetic variation more widely to better understand the contribution of native genes to yield traits for incorporation into breeding programmes. [ABSTRACT FROM AUTHOR]

Background: Maize (Zea mays ssp. mays) is the most abundantly cultivated and highly valued food commodity in the world. Oil from maize kernels is highly nutritious and important for the diet and health of humans, and it can be used as a source of bioenergy. A better understanding of genetic basis for maize kernel oil can help improve the oil content and quality when applied in breeding. Results: In this study, a KUI3/SC55 recombinant inbred line (RIL) population, consisting of 180 individuals was constructed from a cross between inbred lines KUI3 and SC55. We phenotyped 19 oil-related traits and subsequently dissected the genetic architecture of oil-related traits in maize kernels based on a high-density genetic map. In total, 62 quantitative trait loci (QTLs), with 2 to 5 QTLs per trait, were detected in the KUI3/SC55 RIL population. Each QTL accounted for 6.7% (qSTOL1) to 31.02% (qBELI6) of phenotypic variation and the total phenotypic variation explained (PVE) of all detected QTLs for each trait ranged from 12.5% (OIL) to 52.5% (C16:0/C16:1). Of all these identified QTLs, only 5 were major QTLs located in three genomic regions on chromosome 6 and 9. In addition, two pairs of epistatic QTLs with additive effects were detected and they explained 3.3 and 2.4% of the phenotypic variation, respectively. Colocalization with a previous GWAS on oil-related traits, identified 19 genes. Of these genes, two important candidate genes, GRMZM2G101515 and GRMZM2G022558, were further verified to be associated with C20:0/C22:0 and C18:0/C20:0, respectively, according to a gene-based association analysis. The first gene encodes a kinase-related protein with unknown function, while the second gene encodes fatty acid elongase 2 (fae2) and directly participates in the biosynthesis of very long chain fatty acids in Arabidopsis. Conclusions: Our results provide insights on the genetic basis of oil-related traits and a theoretical basis for improving maize quality by marker-assisted selection. [ABSTRACT FROM AUTHOR]

Neuroticism is a personality trait, which is an important risk factor for psychiatric disorders. Recent genome-wide studies have reported about 600 genes potentially influencing neuroticism. This list consists of both genes whose polymorphisms directly regulate the level of neuroticism and genes whose expression is controlled by genetic variants outside the gene. The first group of genes is more interesting because within-gene association signals are usually easy to interpret and less probable to be false positives. We analyzed and compared the results of two published studies to estimate the number of such genes in the list of neuroticism genes. We demonstrated that 98 known genes were associated with neuroticism owing to their polymorphisms and 134 genes were associated with neuroticism owing to the genetic variants outside the gene. For the remaining genes, we estimated the chance to be in the first group. As a result, according to our estimates, only from 153 to 198 genes of almost 600 known neuroticism genes affect the trait owing to their within-gene polymorphisms. Thus, using neuroticism as an example, we demonstrated that only 25–33% of the genes identified by the widely used methods control the trait by the within-gene variation. This estimate predicts the effectiveness of the whole exome sequencing to increase the power of association analysis. [ABSTRACT FROM AUTHOR]

Nicotine dependence (ND) is a chronic brain disorder that causes heavy social and economic burdens. Although many susceptibility genetic loci have been reported, they can explain only approximately 5%-10% of the genetic variance for the disease. To further explore the genetic etiology of ND, we genotyped 242 764 SNPs using an exome chip from both European-American (N = 1572) and African-American (N = 3371) samples. Gene-based association analysis revealed 29 genes associated significantly with ND. Of the genes in the AA sample, six (i.e., PKD1L2, LAMA5, MUC16, MROH5, ATP8B1, and FREM1) were replicated in the EA sample with p values ranging from 0.0031 to 0.0346. Subsequently, gene enrichment analysis revealed that cell adhesion-related pathways were significantly associated with ND in both the AA and EA samples. Considering that LAMA5 is the most significant gene in cell adhesion-related pathways, we did in vitro functional analysis of this gene, which showed that nicotine significantly suppressed its mRNA expression in HEK293T cells (p < 0.001). Further, our cell migration experiment showed that the migration rate was significantly different in wild-type and LAMA5-knockout (LAMA5-KO)-HEK293T cells. Importantly, nicotine-induced cell migration was abolished in LAMA5-KO cells. Taken together, these findings indicate that LAMA5, as well as cell adhesion-related pathways, play an important role in the etiology of smoking addiction, which warrants further investigation. [ABSTRACT FROM AUTHOR]

The coexistence of coronary artery disease (CAD) and chronic kidney disease (CKD) implies overlapped genetic foundation. However, the common genetic determination between the two diseases remains largely unknown. Relying on summary statistics publicly available from large scale genome-wide association studies (n = 184,305 for CAD and n = 567,460 for CKD), we observed significant positive genetic correlation between CAD and CKD (rg = 0.173, p = 0.024) via the linkage disequilibrium score regression. Next, we implemented gene-based association analysis for each disease through MAGMA (Multi-marker Analysis of GenoMic Annotation) and detected 763 and 827 genes associated with CAD or CKD (FDR < 0.05). Among those 72 genes were shared between the two diseases. Furthermore, by integrating the overlapped genetic information between CAD and CKD, we implemented two pleiotropy-informed informatics approaches including cFDR (conditional false discovery rate) and GPA (Genetic analysis incorporating Pleiotropy and Annotation), and identified 169 and 504 shared genes (FDR < 0.05), of which 121 genes were simultaneously discovered by cFDR and GPA. Importantly, we found 11 potentially new pleiotropic genes related to both CAD and CKD (i.e., ARHGEF19, RSG1, NDST2, CAMK2G, VCL, LRP10, RBM23, USP10, WNT9B, GOSR2, and RPRML). Five of the newly identified pleiotropic genes were further repeated via an additional dataset CAD available from UK Biobank. Our functional enrichment analysis showed that those pleiotropic genes were enriched in diverse relevant pathway processes including quaternary ammonium group transmembrane transporter, dopamine transport. Overall, this study identifies common genetic architectures overlapped between CAD and CKD and will help to advance understanding of the molecular mechanisms underlying the comorbidity of the two diseases.

The coexistence of coronary artery disease (CAD) and chronic kidney disease (CKD) implies overlapped genetic foundation. However, the common genetic determination between the two diseases remains largely unknown. Relying on summary statistics publicly available from large scale genome-wide association studies (n = 184,305 for CAD and n = 567,460 for CKD), we observed significant positive genetic correlation between CAD and CKD (r g = 0.173, p = 0.024) via the linkage disequilibrium score regression. Next, we implemented gene-based association analysis for each disease through MAGMA (Multi-marker Analysis of GenoMic Annotation) and detected 763 and 827 genes associated with CAD or CKD (FDR < 0.05). Among those 72 genes were shared between the two diseases. Furthermore, by integrating the overlapped genetic information between CAD and CKD, we implemented two pleiotropy-informed informatics approaches including cFDR (conditional false discovery rate) and GPA (Genetic analysis incorporating Pleiotropy and Annotation), and identified 169 and 504 shared genes (FDR < 0.05), of which 121 genes were simultaneously discovered by cFDR and GPA. Importantly, we found 11 potentially new pleiotropic genes related to both CAD and CKD (i.e., ARHGEF19, RSG1, NDST2, CAMK2G, VCL, LRP10, RBM23, USP10, WNT9B, GOSR2 , and RPRML). Five of the newly identified pleiotropic genes were further repeated via an additional dataset CAD available from UK Biobank. Our functional enrichment analysis showed that those pleiotropic genes were enriched in diverse relevant pathway processes including quaternary ammonium group transmembrane transporter, dopamine transport. Overall, this study identifies common genetic architectures overlapped between CAD and CKD and will help to advance understanding of the molecular mechanisms underlying the comorbidity of the two diseases. [ABSTRACT FROM AUTHOR]

Grain shape is an important agronomic character of rice, which affects the appearance, processing, and the edible quality. Screening and identifying more new genes associated with grain shape is beneficial to further understanding the genetic basis of grain shape and provides more gene resources for genetic breeding. This study has a natural population containing 623 indica rice cultivars. Genome-wide association studies/GWAS of several traits related to grain shape (grain length/GL, grain width/GW, grain length to width ratio/GLWR, grain circumferences/GC, and grain size/grain area/GS) were conducted by combining phenotypic data from four environments and the second-generation resequencing data, which have identified 39 important Quantitative trait locus/QTLs. We analyzed the 39 QTLs using three methods: gene-based association analysis, haplotype analysis, and functional annotation and identified three cloned genes (GS3, GW5, OsDER1) and seven new candidate genes in the candidate interval. At the same time, to effectively utilize the genes in the grain shape-related gene bank, we have also analyzed the allelic combinations of the three cloned genes. Finally, the extreme allele combination corresponding to each trait was found through statistical analysis. This study’s novel candidate genes and allele combinations will provide a valuable reference for future breeding work.

Several gene‐based association tests for time‐to‐event traits have been proposed recently to detect whether a gene region (containing multiple variants), as a set, is associated with the survival outcome. However, for bivariate survival outcomes, to the best of our knowledge, there is no statistical method that can be directly applied for gene‐based association analysis. Motivated by a genetic study to discover the gene regions associated with the progression of a bilateral eye disease, age‐related macular degeneration (AMD), we implement a novel functional regression (FR) method under the copula framework. Specifically, the effects of variants within a gene region are modeled through a functional linear model, which then contributes to the marginal survival functions within the copula. Generalized score test statistics are derived to test for the association between bivariate survival traits and the genetic region. Extensive simulation studies are conducted to evaluate the type I error control and power performance of the proposed approach, with comparisons to several existing methods for a single survival trait, as well as the marginal Cox FR model using the robust sandwich estimator for bivariate survival traits. Finally, we apply our method to a large AMD study, the Age‐related Eye Disease Study, and to identify the gene regions that are associated with AMD progression. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegenerative disorder characterized by a heterogeneous distribution of pathological changes in the brain. Cortical thickness is one of the most sensitive imaging biomarkers for AD representing structural atrophy. The purpose of this study is to identify novel genes associated with cortical thickness. We measured the whole-brain mean cortical thickness from magnetic resonance imaging (MRI) scans in 919 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, including 163 AD patients, 488 mild cognitive impairment patients, and 268 cognitively normal participants. Based on the single-nucleotide polymorphism (SNP)-based genome-wide association study, we performed gene-based association analysis for mean cortical thickness. Furthermore, we performed expression quantitative trait loci, protein-protein interaction network, and pathway analysis to identify biologically functional information. We identified four genes (B4GALNT1, RAB44, LOC101927583, and SLC26A10), two pathways (cyclin-dependent protein kinase holoenzyme complex and nuclear cyclin-dependent protein kinase holoenzyme complex), and one protein-protein interaction (B4GALNT1 and GALNT8 pair). These genes are involved in protein degradation, GTPase activity, neuronal loss, and apoptosis. The identified pathways are involved in the cellular processes and neuronal differentiation, which contribute to neuronal loss that is responsible for AD. Furthermore, the most significant SNP (rs12320537) in B4GALNT1 is associated with expression levels of B4GALNT1 in several brain regions. Thus, the identified genes and pathways provide deeper mechanistic insight into the molecular basis of brain atrophy in AD. [ABSTRACT FROM AUTHOR]

Appearance and milling quality are two crucial properties of rice grains affecting its market acceptability. Understanding the genetic base of rice grain quality could considerably improve the high quality breeding. Here, we carried out an association analysis to identify QTL affecting nine rice grain appearance and milling quality traits using a diverse panel of 258 accessions selected from 3K Rice Genome Project and evaluated in two environments Sanya and Shenzhen. Genome-wide association analyses using 22,488 high quality SNPs identified 72 QTL affecting the nine traits. Combined gene-based association and haplotype analyses plus functional annotation allowed us to shortlist 19 candidate genes for seven important QTL regions affecting the grain quality traits, including two cloned genes (GS3 and TUD), two fine mapped QTL (qGRL7.1 and qPGWC7) and three newly identified QTL (qGL3.4, qGW1.1, and qGW10.2). The most likely candidate gene(s) for each important QTL were also discussed. This research demonstrated the superior power to shortlist candidate genes affecting complex phenotypes by the strategy of combined GWAS, gene-based association and haplotype analyses. The identified candidate genes provided valuable sources for future functional characterization and genetic improvement of rice appearance and milling quality. [ABSTRACT FROM AUTHOR]

Grain shape is an important agronomic character of rice, which affects the appearance, processing, and the edible quality. Screening and identifying more new genes associated with grain shape is beneficial to further understanding the genetic basis of grain shape and provides more gene resources for genetic breeding. This study has a natural population containing 623 indica rice cultivars. Genome-wide association studies/GWAS of several traits related to grain shape (grain length/GL, grain width/GW, grain length to width ratio/GLWR, grain circumferences/GC, and grain size/grain area/GS) were conducted by combining phenotypic data from four environments and the second-generation resequencing data, which have identified 39 important Quantitative trait locus/QTLs. We analyzed the 39 QTLs using three methods: gene-based association analysis, haplotype analysis, and functional annotation and identified three cloned genes (GS3, GW5, OsDER1) and seven new candidate genes in the candidate interval. At the same time, to effectively utilize the genes in the grain shape-related gene bank, we have also analyzed the allelic combinations of the three cloned genes. Finally, the extreme allele combination corresponding to each trait was found through statistical analysis. This study's novel candidate genes and allele combinations will provide a valuable reference for future breeding work. [ABSTRACT FROM AUTHOR]

Lodging reduces rice yield, but increasing lodging resistance (LR) usually limits yield potential. Stem strength and leaf type are major traits related to LR and yield, respectively. Hence, understanding the genetic basis of stem strength and leaf type is of help to reduce lodging and increase yield in LR breeding. Here, we carried out an association analysis to identify quantitative trait locus (QTLs) affecting stem strength-related traits (internode length/IL, stem wall thickness/SWT, stem outer diameter/SOD, and stem inner diameter/SID) and leaf type-associated traits (Flag leaf length/FLL, Flag leaf angle/FLA, Flag leaf width/FLW, leaf-rolling/LFR and SPAD/Soil, and plant analyzer development) using a diverse panel of 550 accessions and evaluated over two years. Genome-wide association study (GWAS) using 4,076,837 high-quality single-nucleotide polymorphisms (SNPs) identified 89 QTLs for the nine traits. Next, through "gene-based association analysis, haplotype analysis, and functional annotation", the scope was narrowed down step by step. Finally, we identified 21 candidate genes in 9 important QTLs that included four reported genes (TUT1, OsCCC1, CFL1, and ACL-D), and seventeen novel candidate genes. Introgression of alleles, which are beneficial for both stem strength and leaf type, or pyramiding stem strength alleles and leaf type alleles, can be employed for LR breeding. All in all, the experimental data and the identified candidate genes in this study provide a useful reference for the genetic improvement of rice LR. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) patients display hippocampal atrophy, memory impairment, and cognitive decline. New neurons are generated throughout adulthood in 2 regions of the brain implicated in AD, the dentate gyrus of the hippocampus and the subventricular zone of the olfactory bulb. Disruption of this process contributes to neurodegenerative diseases including AD, and many of the molecular players in AD are also modulators of adult neurogenesis. However, the genetic mechanisms underlying adult neurogenesis in AD have been underexplored. To address this gap, we performed a gene-based association analysis in cognitively normal and impaired participants using neurogenesis pathway-related candidate genes curated from existing databases, literature mining, and large-scale genome-wide association study findings. A gene-based association analysis identified adenosine A2a receptor (ADORA2A) as significantly associated with hippocampal volume and the association between rs9608282 within ADORA2A and hippocampal volume was replicated in the meta-analysis after multiple comparison adjustments (p = 7.88 × 10 -6 ). The minor allele of rs9608282 in ADORA2A is associated with larger hippocampal volumes and better memory., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Appearance and milling quality are two crucial properties of rice grains affecting its market acceptability. Understanding the genetic base of rice grain quality could considerably improve the high quality breeding. Here, we carried out an association analysis to identify QTL affecting nine rice grain appearance and milling quality traits using a diverse panel of 258 accessions selected from 3K Rice Genome Project and evaluated in two environments Sanya and Shenzhen. Genome-wide association analyses using 22,488 high quality SNPs identified 72 QTL affecting the nine traits. Combined gene-based association and haplotype analyses plus functional annotation allowed us to shortlist 19 candidate genes for seven important QTL regions affecting the grain quality traits, including two cloned genes ( GS3 and TUD ), two fine mapped QTL ( qGRL7.1 and qPGWC7 ) and three newly identified QTL ( qGL3.4, qGW1.1 , and qGW10.2 ). The most likely candidate gene(s) for each important QTL were also discussed. This research demonstrated the superior power to shortlist candidate genes affecting complex phenotypes by the strategy of combined GWAS, gene-based association and haplotype analyses. The identified candidate genes provided valuable sources for future functional characterization and genetic improvement of rice appearance and milling quality.