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Rare variants, comprising the vast majority of human genetic variations, are likely to have more deleterious impact in the context of human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases. Author summary: Existing rare variants association methods often lack statistical power when sample sizes are small. Here we propose a novel integrative statistical framework, the carrier statistic, which can leverage paired genotype and gene expression data to quantify the functional impact of rare variants and enhance detection power of rare variants responsible for disease. Extensive simulations demonstrate that carrier statistic provides well-calibrated false discovery rates, shows substantially higher sensitivity compared to existing methods, and remains robust under unbalanced case-control ratios. Through analyzing real multi-omics dataset for Alzheimer's disease, we identified 16 rare variants within 15 genes with extreme carrier statistics. We hope that the results presented in this paper can highlight the promise of the carrier statistic approach and will encourage future disease studies to collect both genotype and gene expression data for the same individuals. As multi-omics and genome sequencing data continue to expand, we anticipate that carrier statistics will be a valuable tool for elucidating the molecular mechanism underlying human complex diseases. [ABSTRACT FROM AUTHOR]

Background: Blastomycosis is a pulmonary disease caused by Blastomyces spp., a group of pathogenic dimorphic fungi endemic to a number of geographic regions, specifically Manitoba and northwestern Ontario, Canada. Immunosuppression is a major risk factor affecting disease susceptibility, yet host immunity is not well understood. Genetic immunodeficiencies can also influence disease, with variants in IL6, GATA2 and VDBP shown to influence susceptibility. Additional genetic factors in disease susceptibility and severity remain undetected. Our study seeks to identify potential genetic risk factors in a blastomycosis case–control cohort from Manitoba and northwestern Ontario, Canada. Methods: Exomes from 18 blastomycosis cases and 9 controls were sequenced, variants were identified and filtered for accuracy and quality. We performed candidate gene prioritisation and variant aggregation to identify genetic associations and explored the full exome dataset. Results: Ninety‐nine genetic variants in 42 candidate genes were identified in the exome dataset. No variants associated with susceptibility were identified in a single‐variant analysis although two non‐synonymous variants in TYK2 were enriched among cases suggesting a possible role in susceptibility. Gene‐based association analysis found variants in TLR1 enriched in controls (p = 0.024) suggesting a possible protective effect. Gene cluster analysis identified genetic variants in genes of chromatin remodelling, proteasome and intraflagellar transport significantly enriched in cases (false discovery rates < 14%). Conclusions: The findings in this study show novel associations with blastomycosis susceptibility. A better understanding of host immunity and genetic predisposition to Blastomyces infection can help to inform clinical practice for improved outcomes. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to investigate whether genetic variations in the OPRD1 gene affect psychopathological symptoms and personality dimensions in eating disorders (ED) patients and/or contribute to ED risk. Methods: The study involved 221 female patients with anorexia nervosa (AN), 88 with bulimia nervosa (BN), and 396 controls. Sixteen tag-single nucleotide polymorphisms (SNPs) in OPRD1 were identified. Psychometric evaluations were conducted using the Symptom Checklist 90 Revised (SCL-90R) and the Eating Disorders Inventory Test-2 (EDI-2). p-values obtained by regression models were corrected for multiple testing by the False Discovery Rate (FDR) method. Results: In AN patients, genotypes rs204077TT and rs169450TT were linked to lower body-mass index (BMI) values (FDR-q = 0.035 and 0.017, respectively), as was rs2234918 in a log-additive model (BMI: 18.0 ± 0.28, 17.22 ± 0.18 and 16.59 ± 0.39 for TT, TC and CC carriers, FDR-q = 0.012). Additionally, AN patients carrying the rs72665504AA genotype had higher scores in interpersonal distrust (FDR-q = 0.030), whilst BN carriers of rs513269TT and rs2873795TT showed lower scores in ineffectiveness (FDR-q = 0.041 and FDR-q = 0.021). In the AN group, BMI correlated with variability in a distal haplotype (rs508448/rs204077/rs223491, FDR-q = 0.028), which was also associated with the global positive symptom total (PST) index of SCL-90R (FDR-q = 0.048). Associations were more noticeable in BN patients; again, the distal region of the gene was linked to EDI-2 total scores (FDR-q = 0.004–0.048 for the four last haplotypes) and two global SCL-90R indices (GSI: FDR-q = 0.011 and positive symptom distress index (PSDI): FDR-q = 0.003 for the last s204077/rs2234918/rs169450 combination). No associations with ED risk were observed. Conclusions: Genetic variation in the OPRD1 gene, particularly in its distal region, is associated with BMI and psychopathological comorbidities in ED patients. [ABSTRACT FROM AUTHOR]

The interaction between the immune system and the brain, crucial for blood–brain barrier integrity, is a potential factor in migraine development. Although there's evidence of a connection between immune dysregulation and migraine, a clear causal link has been lacking. To bridge this knowledge gap, we performed a two-sample Mendelian randomization (MR) analysis of 731 immune cell phenotypes to determine their causality with migraine, of which parameters included fluorescence, cell abundance, count, and morphology. Sensitivity and pleiotropy checks validated our findings. After applying a false discovery rate correction, our MR study identified 35 of 731 immune phenotypes with a significant causal link to migraine (p < 0.05). Of these, 24 showed a protective effect (inverse variance weighting : p < 0.05, odds ratio <1), and 11 were risk factors (inverse variance weighting : p < 0.05, odds ratio >1). Although limited by population sample size and potential population-specific genetic variations, our study uncovers a significant genetic link between certain immune cell markers and migraine, providing new insights into the disorder's pathophysiology. These discoveries are crucial for developing targeted biomarkers and personalized treatments. The research enhances our understanding of immune cells' role in migraine and may substantially improve patient outcomes and lessen its socio-economic impact. [ABSTRACT FROM AUTHOR]

Both trio and population designs are popular study designs for identifying risk genetic variants in genome-wide association studies (GWASs). The trio design, as a family-based design, is robust to confounding due to population structure, whereas the population design is often more powerful due to larger sample sizes. Here, we propose KnockoffHybrid, a knockoff-based statistical method for hybrid analysis of both the trio and population designs. KnockoffHybrid provides a unified framework that brings together the advantages of both designs and produces powerful hybrid analysis while controlling the false discovery rate (FDR) in the presence of linkage disequilibrium and population structure. Furthermore, KnockoffHybrid has the flexibility to leverage different types of summary statistics for hybrid analyses, including expression quantitative trait loci (eQTL) and GWAS summary statistics. We demonstrate in simulations that KnockoffHybrid offers power gains over non-hybrid methods for the trio and population designs with the same number of cases while controlling the FDR with complex correlation among variants and population structure among subjects. In hybrid analyses of three trio cohorts for autism spectrum disorders (ASDs) from the Autism Speaks MSSNG, Autism Sequencing Consortium, and Autism Genome Project with GWAS summary statistics from the iPSYCH project and eQTL summary statistics from the MetaBrain project, KnockoffHybrid outperforms conventional methods by replicating several known risk genes for ASDs and identifying additional associations with variants in other genes, including the PRAME family genes involved in axon guidance and which may act as common targets for human speech/language evolution and related disorders. We introduce KnockoffHybrid, a statistical method to identify risk genetic variants for the population and trio designs built upon a recently developed knockoff framework. KnockoffHybrid controls the false discovery rate, protects against confounding due to population structure, and is more powerful than conventional methods that control the family-wise error rate. [ABSTRACT FROM AUTHOR]

Background: Expression quantitative trait locus (eQTL) analysis aims to detect the genetic variants that influence the expression of one or more genes. Gene-level eQTL testing forms a natural grouped-hypothesis testing strategy with clear biological importance. Methods to control family-wise error rate or false discovery rate for group testing have been proposed earlier, but may not be powerful or easily apply to eQTL data, for which certain structured alternatives may be defensible and may enable the researcher to avoid overly conservative approaches. Results: In an empirical Bayesian setting, we propose a new method to control the false discovery rate (FDR) for grouped hypotheses. Here, each gene forms a group, with SNPs annotated to the gene corresponding to individual hypotheses. The heterogeneity of effect sizes in different groups is considered by the introduction of a random effects component. Our method, entitled Random Effects model and testing procedure for Group-level FDR control (REG-FDR), assumes a model for alternative hypotheses for the eQTL data and controls the FDR by adaptive thresholding. As a convenient alternate approach, we also propose Z-REG-FDR, an approximate version of REG-FDR, that uses only Z-statistics of association between genotype and expression for each gene-SNP pair. The performance of Z-REG-FDR is evaluated using both simulated and real data. Simulations demonstrate that Z-REG-FDR performs similarly to REG-FDR, but with much improved computational speed. Conclusion: Our results demonstrate that the Z-REG-FDR method performs favorably compared to other methods in terms of statistical power and control of FDR. It can be of great practical use for grouped hypothesis testing for eQTL analysis or similar problems in statistical genomics due to its fast computation and ability to be fit using only summary data. [ABSTRACT FROM AUTHOR]

Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize (Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of "biological process", and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize. [ABSTRACT FROM AUTHOR]

Background: Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping. Results: In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%−32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154). Conclusion: The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding. [ABSTRACT FROM AUTHOR]

Background: Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation. Joint calling is routinely used to combine identified variants across multiple related samples. However, the improvement of variants identification using the mutual support information from multiple samples remains quite limited for population-scale genotyping. Results: In this study, we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples' data. The variants were accurately identified from multiple samples by using four steps: (1) Probabilities of variants from two widely used algorithms, GATK and Freebayes, were calculated by Poisson model incorporating base sequencing error potential; (2) The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification (rHID) variants database; (3) The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate (FDR) using rHID database; (4) To avoid the elimination of potentially true variants from rHID database, the variants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants. The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%−32% compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number (GPC5), scrapie pathology (PAPSS2), seasonal reproduction and litter size (GRM1), coat color (RAB27A), and lentivirus susceptibility (TMEM154). Conclusion: The new method used the computational strategy to reduce the number of false positives, and simultaneously improve the identification of genetic variants. This strategy did not incur any extra cost by using any additional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding. [ABSTRACT FROM AUTHOR]

Low cardiorespiratory fitness, measured as maximal oxygen uptake (...), is associated with all-cause mortality and diseasespecific morbidity and mortality and is estimated to have a large genetic component (~60%). However, the underlying mechanisms explaining the associations are not known, and no association study has assessed shared genetics between directly measured ... and disease. We believe that identifying the mechanisms explaining how low ... is related to increased disease risk can contribute to prevention and therapy. We used a phenome-wide association study approach to test for shared genetics. A total of 64,479 participants from the Trøndelag Health Study (HUNT) were included. Genetic variants previously linked to ... were tested for association with diseases related to the cardiovascular system, diabetes, dementia, mental disorders, and cancer as well as clinical measurements and biomarkers from HUNT. In the total population, three single-nucleotide polymorphisms (SNPs) in and near the follicle-stimulating hormone receptor gene (FSHR) were found to be associated (false discovery rate < 0.05) with serum creatinine levels and one intronic SNP in the Rap-associating DIL domain gene (RADIL) with diabetes type 1 with neurological manifestations. In males, four intronic SNPs in the PBX/knotted homeobox 2 gene (PKNOX2) were found to be associated with endocarditis. None of the association tests in the female population reached overall statistical significance; the associations with the lowest P values included other cardiac conduction disorders, subdural hemorrhage, and myocarditis. The results might suggest shared genetics between ... and disease. However, further effort should be put into investigating the potential shared genetics between inborn ... and disease in larger cohorts to increase statistical power. [ABSTRACT FROM AUTHOR]

Common genetic variants associated with lung cancer have been well studied in the past decade. However, only 12.3% heritability has been explained by these variants. In this study, we investigate the contribution of rare variants (RVs) (minor allele frequency <0.01) to lung cancer through two large whole exome sequencing case-control studies. We first performed gene-based association tests using a novel Bayes Factor statistic in the International Lung Cancer Consortium, the discovery study (European, 1042 cases vs. 881 controls). The top genes identified are further assessed in the UK Biobank (European, 630 cases vs. 172 864 controls), the replication study. After controlling for the false discovery rate, we found two genes, CTSL and APOE, significantly associated with lung cancer in both studies. Single variant tests in UK Biobank identified 4 RVs (3 missense variants) in CTSL and 2 RVs (1 missense variant) in APOE stongly associated with lung cancer (OR between 2.0 and 139.0). The role of these genetic variants in the regulation of CTSL or APOE expression remains unclear. If such a role is established, this could have important therapeutic implications for lung cancer patients. Author summary: Lung cancer (LC) is the leading cause of cancer death accounting for 18% of all cancer deaths. Previous studies have suggested genetic contribution to the disease. Common genetic variants associated with LC have been well studied through large, collaborative, genome-wide association studies (GWASs) in the past decade. However, they explained only about 12.3% of LC heritability. It is therefore hypothesized that the unexplained variability might be partially due to rare variants (RVs). In this study, we applied a novel gene-based test statistic based on a Bayes Factor approach, to whole exome sequencing data from the International Lung Cancer consortium (ILCCO). Independent replication of the top genes identified was performed using the UK Biobank data. We found two genes, CTSL and APOE, significantly associated with LC in both studies. Within these two genes, several RVs showed strong associations with lung cancer in the UK Biobank data. These findings could suggest potential molecular mechanisms leading to lung cancer and more importantly, possible therapeutic targets for personalized treatment. [ABSTRACT FROM AUTHOR]

Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize (Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of "biological process", and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize. [ABSTRACT FROM AUTHOR]

The molecular mechanisms underlying posttraumatic stress disorder (PTSD) are yet to be fully elucidated, especially in underrepresented population groups. Expression quantitative trait loci (eQTLs) are DNA sequence variants that influence gene expression, in a local (cis‐) or distal (trans‐) manner, and subsequently impact cellular, tissue, and system physiology. This study aims to identify genetic loci associated with gene expression changes in a South African PTSD cohort. Genome‐wide genotype and RNA‐sequencing data were obtained from 32 trauma‐exposed controls and 35 PTSD cases of mixed‐ancestry, as part of the SHARED ROOTS project. The first approach utilised 108 937 single‐nucleotide polymorphisms (SNPs) (MAF > 10%) and 11 312 genes with Matrix eQTL to map potential eQTLs, while controlling for covariates as appropriate. The second analysis was focused on 5638 SNPs related to a previously calculated PTSD polygenic risk score for this cohort. SNP‐gene pairs were considered eQTLs if they surpassed Bonferroni correction and had a false discovery rate <0.05. We did not identify eQTLs that significantly influenced gene expression in a PTSD‐dependent manner. However, several known cis‐eQTLs, independent of PTSD diagnosis, were observed. rs8521 (C > T) was associated with TAGLN and SIDT2 expression, and rs11085906 (C > T) was associated with ZNF333 expression. This exploratory study provides insight into the molecular mechanisms associated with PTSD in a non‐European, admixed sample population. This study was limited by the cross‐sectional design and insufficient statistical power. Overall, this study should encourage further multi‐omics approaches towards investigating PTSD in diverse populations. [ABSTRACT FROM AUTHOR]

Background: Irritable bowel syndrome (IBS) often co-occurs with psychiatric and gastrointestinal disorders. A recent genome-wide association study (GWAS) identified several genetic risk variants for IBS. However, most of the heritability remains unidentified, and the genetic overlap with psychiatric and somatic disorders is not quantified beyond genome-wide genetic correlations. Here, we characterize the genetic architecture of IBS, further, investigate its genetic overlap with psychiatric and gastrointestinal phenotypes, and identify novel genomic risk loci. Methods: Using GWAS summary statistics of IBS (53,400 cases and 433,201 controls), and psychiatric and gastrointestinal phenotypes, we performed bivariate casual mixture model analysis to characterize the genetic architecture and genetic overlap between these phenotypes. We leveraged identified genetic overlap to boost the discovery of genomic loci associated with IBS, and to identify specific shared loci associated with both IBS and psychiatric and gastrointestinal phenotypes, using the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework. We used functional mapping and gene annotation (FUMA) for functional analyses. Results: IBS was highly polygenic with 12k trait-influencing variants. We found extensive polygenic overlap between IBS and psychiatric disorders and to a lesser extent with gastrointestinal diseases. We identified 132 independent IBS-associated loci (condFDR < 0.05) by conditioning on psychiatric disorders (n = 127) and gastrointestinal diseases (n = 24). Using conjFDR, 70 unique loci were shared between IBS and psychiatric disorders. Functional analyses of shared loci revealed enrichment for biological pathways of the nervous and immune systems. Genetic correlations and shared loci between psychiatric disorders and IBS subtypes were different. Conclusions: We found extensive polygenic overlap of IBS and psychiatric and gastrointestinal phenotypes beyond what was revealed with genetic correlations. Leveraging the overlap, we discovered genetic loci associated with IBS which implicate a wide range of biological pathways beyond the gut-brain axis. Genetic differences may underlie the clinical subtype of IBS. These results increase our understanding of the pathophysiology of IBS which may form the basis for the development of individualized interventions. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently. Results: By leveraging summary statistics of 37 traits from East Asian (Nmax=254,373) or European (Nmax=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρg) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs. Conclusions: Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping. [ABSTRACT FROM AUTHOR]

Motivation The findings from genome-wide association studies (GWASs) have greatly helped us to understand the genetic basis of human complex traits and diseases. Despite the tremendous progress, much effects are still needed to address several major challenges arising in GWAS. First, most GWAS hits are located in the non-coding region of human genome, and thus their biological functions largely remain unknown. Second, due to the polygenicity of human complex traits and diseases, many genetic risk variants with weak or moderate effects have not been identified yet. Results To address the above challenges, we propose a powerful and adaptive latent model (PALM) to integrate cell-type/tissue-specific functional annotations with GWAS summary statistics. Unlike existing methods, which are mainly based on linear models, PALM leverages a tree ensemble to adaptively characterize non-linear relationship between functional annotations and the association status of genetic variants. To make PALM scalable to millions of variants and hundreds of functional annotations, we develop a functional gradient-based expectation–maximization algorithm, to fit the tree-based non-linear model in a stable manner. Through comprehensive simulation studies, we show that PALM not only controls false discovery rate well, but also improves statistical power of identifying risk variants. We also apply PALM to integrate summary statistics of 30 GWASs with 127 cell type/tissue-specific functional annotations. The results indicate that PALM can identify more risk variants as well as rank the importance of functional annotations, yielding better interpretation of GWAS results. Availability and implementation The source code is available at https://github.com/YangLabHKUST/PALM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

In this paper, the genetic diversities of 12 rice genotypes (Oryza sativa L.), representing Indica, Japonica, and Indica–Japonica varieties, were assessed using twelve ISSR and five SSR markers. In addition, the rice genotypes were evaluated for 11 agro-morphological traits in a two-year trial. Association mapping was performed to detect any association between the DNA markers and the agro-morphological traits. An association analysis was conducted considering the relative kinship among the genotypes and accounting for the population structure using the unified mixed-model approach to avoid possible false-positive associations. Seventy-three alleles were collectively produced by ISSRs and SSRs, with an average of 6.3 and 2.8 alleles per locus, respectively. Both marker systems were informative, and the average polymorphism information content (PIC) was 0.222 and 0.352 for ISSRs and SSRs, respectively. The average expected heterozygosity (Hexp) was 0.264 for ISSRs compared to 0.457 for SSRs. After using the false discovery rate (FDR) method, the association analysis revealed a total of 12 significant marker–trait associations with six agro-morphological traits, including the no. of unfiled grains panicle−1, panicle length, panicle weight, the no. of panicles plant−1, the no. of tillers plant−1, and 1000-grain weight. ISSRs showed seven significant associations with five markers, while SSRs showed three significant associations with three markers. The phenotypic variance (R2) explained by each marker ranged between 29.2% for the ISSR marker HB11 (associated with 1000-grain weight) and 49.3% for the ISSR marker HB8 (associated with the no. of tillers plant−1). The identified marker–trait associations reported herein may improve the expected gain of future molecular-based rice-breeding programs, particularly those designed for improving grain-related or harvest-related traits. [ABSTRACT FROM AUTHOR]

Background: Single marker analysis (SMA) with linear mixed models for genome wide association studies has uncovered the contribution of genetic variants to many observed phenotypes. However, SMA has weak false discovery control. In addition, when a few variants have large effect sizes, SMA has low statistical power to detect small and medium effect sizes, leading to low recall of true causal single nucleotide polymorphisms (SNPs). Results: We present the Bayesian Iterative Conditional Stochastic Search (BICOSS) method that controls false discovery rate and increases recall of variants with small and medium effect sizes. BICOSS iterates between a screening step and a Bayesian model selection step. A simulation study shows that, when compared to SMA, BICOSS dramatically reduces false discovery rate and allows for smaller effect sizes to be discovered. Finally, two real world applications show the utility and flexibility of BICOSS. Conclusions: When compared to widely used SMA, BICOSS provides higher recall of true SNPs while dramatically reducing false discovery rate. [ABSTRACT FROM AUTHOR]

The emergence of genetic data coupled to longitudinal electronic medical records (EMRs) offers the possibility of phenome-wide association studies (PheWAS). In PheWAS, the whole phenome can be divided into numerous phenotypic categories according to the genetic architecture across phenotypes. Currently, statistical analyses for PheWAS are mainly univariate analyses, which test the association between one genetic variant and one phenotype at a time. In this article, we derived a novel and powerful multivariate method for PheWAS. The proposed method involves three steps. In the first step, we apply the bottom-up hierarchical clustering method to partition a large number of phenotypes into disjoint clusters within each phenotypic category. In the second step, the clustering linear combination method is used to combine test statistics within each category based on the phenotypic clusters and obtain p-values from each phenotypic category. In the third step, we propose a new false discovery rate (FDR) control approach. We perform extensive simulation studies to compare the performance of our method with that of other existing methods. The results show that our proposed method controls FDR very well and outperforms other methods we compared with. We also apply the proposed approach to a set of EMR-based phenotypes across more than 300,000 samples from the UK Biobank. We find that the proposed approach not only can well-control FDR at a nominal level but also successfully identify 1,244 significant SNPs that are reported to be associated with some phenotypes in the GWAS catalog. Our open-access tools and instructions on how to implement HCLC-FC are available at https://github.com/XiaoyuLiang/HCLCFC. [ABSTRACT FROM AUTHOR]

Mastitis, the inflammation of the mammary gland caused by both gram-positive and gram-negative bacteria, causes the global dairy industry over $20 billion annually in losses due to changes in milk yield and quality. The goal of this study was to identify genetic variants, genes, and biological pathways that are associated with mastitis. We performed genome-wide association studies (GWAS) on 1,858 cows from two lactations with both medium (n = 87,493) and high density (n = 624,300) single nucleotide polymorphisms (SNP) using a single SNP mixed linear model implemented in GCTA software. A total of 288 SNPs across all evaluated models passed the 5% genome-wise false discovery rate threshold across all autosomes with emphasis on chromosomes 3, 14, 16, 12, 6, and 2. These SNPs were mapped to their corresponding genes using the bovine genome. Gene enrichment analysis was then performed to identify significant biological pathways associated with mastitis. The current study identified potential genes that are involved in the MAPK, C5a, and ion binding pathways, which have been previously associated with immune function and inflammation. Further validation and investigation are required to confirm the identified genes and pathways before implementation in breeding programs. [ABSTRACT FROM AUTHOR]

Family-based designs can eliminate confounding due to population substructure and can distinguish direct from indirect genetic effects, but these designs are underpowered due to limited sample sizes. Here, we propose KnockoffTrio, a statistical method to identify putative causal genetic variants for father-mother-child trio design built upon a recently developed knockoff framework in statistics. KnockoffTrio controls the false discovery rate (FDR) in the presence of arbitrary correlations among tests and is less conservative and thus more powerful than the conventional methods that control the family-wise error rate via Bonferroni correction. Furthermore, KnockoffTrio is not restricted to family-based association tests and can be used in conjunction with more powerful, potentially nonlinear models to improve the power of standard family-based tests. We show, using empirical simulations, that KnockoffTrio can prioritize causal variants over associations due to linkage disequilibrium and can provide protection against confounding due to population stratification. In applications to 14,200 trios from three study cohorts for autism spectrum disorders (ASDs), including AGP, SPARK, and SSC, we show that KnockoffTrio can identify multiple significant associations that are missed by conventional tests applied to the same data. In particular, we replicate known ASD association signals with variants in several genes such as MACROD2 , NRXN1 , PRKAR1B , CADM2 , PCDH9 , and DOCK4 and identify additional associations with variants in other genes including ARHGEF10 , SLC28A1 , ZNF589 , and HINT1 at FDR 10 %. We introduce KnockoffTrio, a statistical method to identify putative causal genetic variants for the father-mother-child trio design built upon a recently developed knockoff framework. KnockoffTrio controls the false discovery rate, protects against confounding due to population stratification, and is more powerful than conventional methods that control the family-wise error rate. [ABSTRACT FROM AUTHOR]

Quantitative genetics in Caenorhabditis elegans seeks to identify naturally segregating genetic variants that underlie complex traits. Genome-wide association studies scan the genome for individual genetic variants that are significantly correlated with phenotypic variation in a population, or quantitative trait loci. Genome-wide association studies are a popular choice for quantitative genetic analyses because the quantitative trait loci that are discovered segregate in natural populations. Despite numerous successful mapping experiments, the empirical performance of genome-wide association study has not, to date, been formally evaluated in C. elegans. We developed an opensource genome-wide association study pipeline called NemaScan and used a simulation-based approach to provide benchmarks of mapping performance in collections of wild C. elegans strains. Simulated trait heritability and complexity determined the spectrum of quantitative trait loci detected by genome-wide association studies. Power to detect smaller-effect quantitative trait loci increased with the number of strains sampled from the C. elegans Natural Diversity Resource. Population structure was a major driver of variation in mapping performance, with populations shaped by recent selection exhibiting significantly lower false discovery rates than populations composed of more divergent strains. We also recapitulated previous genome-wide association studies of experimentally validated quantitative trait variants. Our simulation-based evaluation of performance provides the community with critical context to pursue quantitative genetic studies using the C. elegans Natural Diversity Resource to elucidate the genetic basis of complex traits in C. elegans natural populations. [ABSTRACT FROM AUTHOR]

We consider in this article detection of signal regions associated with disease outcomes in whole genome association studies. Gene- or region-based methods have become increasingly popular in whole genome association analysis as a complementary approach to traditional individual variant analysis. However, these methods test for the association between an outcome and the genetic variants in a prespecified region, for example, a gene. In view of massive intergenic regions in whole genome sequencing (WGS) studies, we propose a computationally efficient quadratic scan (Q-SCAN) statistic based method to detect the existence and the locations of signal regions by scanning the genome continuously. The proposed method accounts for the correlation (linkage disequilibrium) among genetic variants, and allows for signal regions to have both causal and neutral variants, and the effects of signal variants to be in different directions. We study the asymptotic properties of the proposed Q-SCAN statistics. We derive an empirical threshold that controls for the family-wise error rate, and show that under regularity conditions the proposed method consistently selects the true signal regions. We perform simulation studies to evaluate the finite sample performance of the proposed method. Our simulation results show that the proposed procedure outperforms the existing methods, especially when signal regions have causal variants whose effects are in different directions, or are contaminated with neutral variants. We illustrate Q-SCAN by analyzing the WGS data from the Atherosclerosis Risk in Communities study. for this article are available online. [ABSTRACT FROM AUTHOR]

Neurocognitive impairment is a common and debilitating complication of sickle cell disease (SCD) resulting from a combination of biological and environmental factors. The catechol-O-methyltransferase (COMT) gene modulates levels of dopamine availability in the prefrontal cortex. COMT has repeatedly been implicated in the perception of pain stimuli and frequency of pain crises in patients with SCD and is known to be associated with neurocognitive functioning in the general population. The current study aimed to examine the associations of genetic variants in COMT and neurocognitive functioning in patients with SCD. The Sickle Cell Clinical Research and Intervention Program (SCCRIP) longitudinal cohort was used as a discovery cohort (n = 166). The genotypes for 5 SNPs (rs6269, rs4633, rs4818, rs4680, and rs165599) in COMT were extracted from whole genome sequencing data and analyzed using a dominant model. A polygenic score for COMT (PGSCOMT) integrating these 5 SNPs was analyzed as a continuous variable. The Cooperative Study of Sickle Cell Disease (CSSCD, n = 156) and the Silent Cerebral Infarction Transfusion (SIT, n = 114) Trial were used as 2 independent replication cohorts. Due to previously reported sex differences, all analyses were conducted separately in males and females. The Benjamini and Hochberg approach was used to calculate false discovery rate adjusted p-value (q-value). In SCCRIP, 1 out of 5 SNPs (rs165599) was associated with IQ at q <0.05 in males but not females, and 2 other SNPs (rs4633 and rs4680) were marginally associated with sustained attention at p <0.05 in males only but did not maintain at q <0.05. PGS COMT was negatively associated with IQ and sustained attention at p <0.05 in males only. Using 3 cohorts' data, 4 out of 5 SNPs (rs6269, rs4633, rs4680, rs165599) were associated with IQ (minimum q-value = 0.0036) at q <0.05 among male participants but not female participants. The PGS COMT was negatively associated with IQ performance among males but not females across all cohorts. Select COMT SNPs are associated with neurocognitive abilities in males with SCD. By identifying genetic predictors of neurocognitive performance in SCD, it may be possible to risk-stratify patients from a young age to guide implementation of early interventions. [ABSTRACT FROM AUTHOR]

Objective: We aimed to identify genes associated with genetic generalized epilepsy (GGE) by combining large cohorts enriched with individuals with a positive family history. Secondarily, we set out to compare the association of genes independently with familial and sporadic GGE. Methods: We performed a case–control whole exome sequencing study in unrelated individuals of European descent diagnosed with GGE (previously recruited and sequenced through multiple international collaborations) and ancestry‐matched controls. The association of ultra‐rare variants (URVs; in 18 834 protein‐coding genes) with epilepsy was examined in 1928 individuals with GGE (vs. 8578 controls), then separately in 945 individuals with familial GGE (vs. 8626 controls), and finally in 1005 individuals with sporadic GGE (vs. 8621 controls). We additionally examined the association of URVs with familial and sporadic GGE in two gene sets important for inhibitory signaling (19 genes encoding γ‐aminobutyric acid type A [GABAA] receptors, 113 genes representing the GABAergic pathway). Results: GABRG2 was associated with GGE (p = 1.8 × 10−5), approaching study‐wide significance in familial GGE (p = 3.0 × 10−6), whereas no gene approached a significant association with sporadic GGE. Deleterious URVs in the most intolerant subgenic regions in genes encoding GABAA receptors were associated with familial GGE (odds ratio [OR] = 3.9, 95% confidence interval [CI] = 1.9–7.8, false discovery rate [FDR]‐adjusted p =.0024), whereas their association with sporadic GGE had marginally lower odds (OR = 3.1, 95% CI = 1.3–6.7, FDR‐adjusted p =.022). URVs in GABAergic pathway genes were associated with familial GGE (OR = 1.8, 95% CI = 1.3–2.5, FDR‐adjusted p =.0024) but not with sporadic GGE (OR = 1.3, 95% CI =.9–1.9, FDR‐adjusted p =.19). Significance: URVs in GABRG2 are likely an important risk factor for familial GGE. The association of gene sets of GABAergic signaling with familial GGE is more prominent than with sporadic GGE. [ABSTRACT FROM AUTHOR]

Background: Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits.Methods: We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods.Results: Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate.Conclusion: We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource. Author summary: Genome-wide association studies (GWAS) detect regions of the human genome that are associated with various traits, including complex diseases, but the power to detect these genomic regions is currently limited by sample size. The conditional false discovery rate (cFDR) provides a tool to leverage one GWAS study to improve power in another. The motivation is that if two traits have some genetic correlation, then our interpretation of a low but not significant p-value for the trait of interest will differ depending on whether that SNP shows strong or absent evidence of association with the related trait. Here, we describe an extension to the cFDR framework, called "Flexible cFDR", that controls the FDR and supports auxiliary data from arbitrary distributions, surpassing current implementations of cFDR which are restricted to leveraging GWAS p-values from related traits. In practice, our method can be used to iteratively leverage various types of functional genomic data with GWAS data to increase power for GWAS discovery. We describe the use of Flexible cFDR to supplement data from a GWAS of asthma with auxiliary data from functional genomic experiments. We identify associations novel to the original GWAS and validate these discoveries with reference to a larger, more highly-powered GWAS of asthma. [ABSTRACT FROM AUTHOR]

Background: The prevalence of obesity and its related chronic diseases have been increasing especially in Asian countries. Obesity-related genetic variants have been identified, but these explain little of the variation in BMI. Recent studies reported associations between DNA methylation and obesity, mostly in non-Asian populations. Methods: We performed an epigenome-wide association study (EWAS) on general adiposity (body mass index, BMI) and abdominal adiposity (waist circumference, WC) in 409 multi-ethnic Asian individuals and replicated BMI and waist-associated DNA methylation CpGs identified in other populations. The cross-lagged panel model and Mendelian randomization were used to assess the temporal relationship between methylation and BMI. The temporal relationship between the identified CpGs and inflammation and metabolic markers was also examined. Results: EWAS identified 116 DNA methylation CpGs independently associated with BMI and eight independently associated with WC at false discovery rate PFDR < 0.05 in 409 Asian samples. We replicated 110 BMI-associated CpGs previously reported in Europeans and identified six novel BMI-associated CpGs and two novel WC-associated CpGs. We observed high consistency in association direction of effect compared to studies in other populations. Causal relationship analyses indicated that BMI was more likely to be the cause of DNA methylation alteration, rather than the consequence. The causal analyses using BMI-associated methylation risk score also suggested that higher levels of the inflammation marker IL-6 were likely the consequence of methylation change. Conclusion: Our study provides evidence of an association between obesity and DNA methylation in multi-ethnic Asians and suggests that obesity can drive methylation change. The results also suggested possible causal influence that obesity-related methylation changes might have on inflammation and lipoprotein levels. [ABSTRACT FROM AUTHOR]

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly that is often accompanied by other anomalies. Although the role of genetics in the pathogenesis of CDH has been established, only a small number of disease-associated genes have been identified. To further investigate the genetics of CDH, we analyzed de novo coding variants in 827 proband-parent trios and confirmed an overall significant enrichment of damaging de novo variants, especially in constrained genes. We identified LONP1 (lon peptidase 1, mitochondrial) and ALYREF (Aly/REF export factor) as candidate CDH-associated genes on the basis of de novo variants at a false discovery rate below 0.05. We also performed ultra-rare variant association analyses in 748 affected individuals and 11,220 ancestry-matched population control individuals and identified LONP1 as a risk gene contributing to CDH through both de novo and ultra-rare inherited largely heterozygous variants clustered in the core of the domains and segregating with CDH in affected familial individuals. Approximately 3% of our CDH cohort who are heterozygous with ultra-rare predicted damaging variants in LONP1 have a range of clinical phenotypes, including other anomalies in some individuals and higher mortality and requirement for extracorporeal membrane oxygenation. Mice with lung epithelium-specific deletion of Lonp1 die immediately after birth, most likely because of the observed severe reduction of lung growth, a known contributor to the high mortality in humans. Our findings of both de novo and inherited rare variants in the same gene may have implications in the design and analysis for other genetic studies of congenital anomalies. [ABSTRACT FROM AUTHOR]

Longitudinal observational studies and Mendelian randomization research have obtained contradictory conclusions regarding the association between C-reactive protein (CRP) level and schizophrenia risk. However, the shared genetic mechanisms underlying CRP and schizophrenia remain poorly understood. Here, we examined the global and local genetic correlations using summary statistics from large-scale genome-wide association studies (GWAS) on CRP level and schizophrenia. Furthermore, we identified their shared genetic variants by applying the conditional false discovery rate approach and performed functional analyses of shared variants to explore the shared genetic mechanisms underlying CRP level and schizophrenia. We found a significant negative genetic correlation at the whole genome level and five significant local genetic correlations between CRP level and schizophrenia. Eight-three shared genetic loci were identified, from which single-nucleotide polymorphism (SNP) presents mixed effects on the increased CRP level and schizophrenia risk. Additionally, we identified 64 and 73 candidate genes that were mapped from SNPs with"concordant effect"(ceSNPs) and"discordant effect"(deSNPs) on the CRP level and schizophrenia risk respectively. Functional analyses revealed that genes mapped from ceSNPs and deSNPs exhibited similar patterns of human brain developmental expression trajectories and biological processes, but differed in expression levels and cell-type-specific enrichment in brain tissues. Our findings demonstrated mixed effects of shared genetic architecture between CRP level and schizophrenia, proving a deeper insight into the shared genetic aetiology underlying the CRP level and schizophrenia. • There is a global negative genetic correlation and five local genetic correlations between CRP level and schizophrenia. • Shared genetic variants have mixed effects on the CRP level and schizophrenia risk. • Our results highlight the complexity of shared genetic mechanisms between CRP level and schizophrenia risk. [ABSTRACT FROM AUTHOR]

• First genetic variants associated with treatment resistant schizophrenia. • Putative molecular pathways related to treatment resistant schizophrenia. • Improved polygenic prediction of treatment resistant schizophrenia. • Discovery of shared genetics between treatment resistance and metabolic function. Treatment resistant schizophrenia (TRS) is characterized by repeated treatment failure with antipsychotics. A recent genome-wide association study (GWAS) of TRS showed a polygenic architecture, but no significant loci were identified. Clozapine is shown to be the superior drug in terms of clinical effect in TRS; at the same time it has a serious side effect profile, including weight gain. Here, we sought to increase power for genetic discovery and improve polygenic prediction of TRS, by leveraging genetic overlap with Body Mass Index (BMI). We analysed GWAS summary statistics for TRS and BMI applying the conditional false discovery rate (cFDR) framework. We observed cross-trait polygenic enrichment for TRS conditioned on associations with BMI. Leveraging this cross-trait enrichment, we identified 2 novel loci for TRS at cFDR <0.01, suggesting a role of MAP2K1 and ZDBF2. Further , polygenic prediction based on the cFDR analysis explained more variance in TRS when compared to the standard TRS GWAS. These findings highlight putative molecular pathways which may distinguish TRS patients from treatment responsive patients. Moreover, these findings confirm that shared genetic mechanisms influence both TRS and BMI and provide new insights into the biological underpinnings of metabolic dysfunction and antipsychotic treatment. [ABSTRACT FROM AUTHOR]

Some gene polymorphisms have been previously associated individually with tacrolimus efficacy and toxicity, but no long-term study to determine the role of pharmacogene variants in the clinical evolution of liver-transplanted patients has been addressed so far. In the present work, we analyzed the relation between highly-evidenced genetic polymorphisms located in relevant pharmacogenes and the risk of suffering premature death and other comorbidities such as cancer, diabetes mellitus, arterial hypertension, graft rejection, infections and nephrotoxicities in a cohort of 87 patients (8 were excluded due to early loss of follow-up) transplanted at Hospital La Fe in Valencia (Spain) during a 12-year follow-up. Employing a logistic regression model with false discovery rate penalization and Kaplan–Meier analyses, we observed significant association between survival rates and metabolizer genes. In this sense, our results show an association between MTHFR gene variants in donor rs1801133 (HR: 7.90; p-value: 0.032) and recipient rs1801131 (HR: 7.34; p-value: 0.036) and the group of patients who died during the follow-up period, supporting the interest of confirming these results with larger patient cohorts. In addition, donor polymorphisms in UGT1A9 metabolizer gene rs6714486 (OR: 0.13; p-value: 0.032) were associated with a lower risk of suffering from de novo cancer. Genetic variants in CYP2B6 metabolizer gene rs2279343 demonstrated an association with a risk of infection. Other variants in different locations of SLCO1A2, ABCC2 and ABCB1 transporter genes were associated with a lower risk of suffering from type 2 diabetes mellitus, chronic and acute nephrotoxicities and arterial hypertension. Results suggest that pharmacogenetics-derived information may be an important support for personalized drug prescription, clinical follow-up and the evolution of liver-transplanted patients. [ABSTRACT FROM AUTHOR]