Genome Association Studies Research Articles

The first transcriptome sequencing and analysis of the endangered plant species Picea neoveitchii Mast. and potential EST-SSR markers development

Picea neoveitchii Mast. is a rare and endangered evergreen coniferous tree species in China. P. neoveitchii grows within a specific ecological and geographical range. Here, we performed transcriptome sequencing of P. neoveitchii using the Illumina high-throughput deep sequencing technology with the following aims: (i) to perform de novo transcriptome assembly, (ii) to perform transcriptome assembly with functional annotation, (iii) to study the candidate genes involved in the biosynthesis of cellulose and lignin, (iv) to identify and analyse the transcription factors, and (v) to develop potential expressed sequence tag–simple sequence repeat (EST-SSR) markers for future population genomic and genome-wide association studies in P. neoveitchii. This is, to the best of our knowledge, the first study to provide a comprehensive transcriptome of P. neoveitchii. The transcriptome data contained 12.97 gigabase pairs (Gbp) for a total of 120,951 different transcripts belonging to 66,022 unigenes. In addition, we classified enzymes involved in the biosynthesis of cellulose and lignin and identified and analysed the transcription factors involved in stress responses, growth and development. Based on the larger than 1 kb unigenes, 1,814 EST-SSR markers were shown. Our results demonstrated that the transcriptome analysis is a valuable technology for non-model species with large genomes and this dataset will be an essential resource for future genetic and genomic research in P. neoveitchii.

Robust and Discriminative Brain Genome Association Study.

Brain Genome Association (BGA) study, which investigates the associations between brain structure/function (characterized by neuroimaging phenotypes) and genetic variations (characterized by Single Nucleotide Polymorphisms (SNPs)), is important in pathological analysis of neurological disease. However, the current BGA studies are limited as they did not explicitly consider the disease labels, source importance, and sample importance in their formulations. We address these issues by proposing a robust and discriminative BGA formulation. Specifically, we learn two transformation matrices for mapping two heterogeneous data sources (i.e., neuroimaging data and genetic data) into a common space, so that the samples from the same subject (but diffrent sources) are close to each other, and also the samples with diffrent labels are separable. In addition, we add a sparsity constraint on the transformation matrices to enable feature selection on both data sources. Furthermore, both sample importance and source importance are also considered in the formulation via adaptive parameter-free sample and source weightings. We have conducted various experiments, using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, to test how well the neuroimaging phenotypes and SNPs can represent each other in the common space.

Research Article Genomic association study for age at first calving and calving interval in Romosinuano and Costeño con Cuernos cattle

Research Article Genomic association study for age at first calving and calving interval in Romosinuano and Costeño con Cuernos cattle

M62 - TWO SIDES OF THE SAME COIN: SHARED POLYGENIC RISK OF PERSONALITY TRAITS AND SUICIDE ATTEMPT IMPLICATES COMMON PATHOMECHANISMS IN IMPAIRED EXECUTIVE FUNCTIONING AND EXTERNALIZING DISORDERS

Background Suicide undoubtedly is the most devastating complications of psychiatric disorders. The link between suicide and mental health problems is strong, particularly for mood disorders and personality disorders. Growing evidence suggest large extent of shared etiology and psychopathology between psychiatric and personality disorders, even indicating the theory of a unified, general psychopathological dimension behind all mental disorder. However, little is known about the common genetic risk factors of personality traits and suicidal behavior. The aim of our study was to assess the extent of the shared genetic etiology between suicidal behavior and the NEO Big Five personality traits (NEO-FFI) by conducting a Polygenic Risk Score prediction (PRS) analysis. Methods Target sample of the PRS analysis included subject with BPD from the GAIN Whole Genome Association Study of Bipolar Disorder (accession: phs000017.v3.p1, total n=999, suicidal n=358, control n=616, missing n=25) Suicidal attempt was assessed with the Diagnostic Interview for Genetic Studies versions 2,3 and 4. To increase the genomic overlap between the training and target datasets, GAIN BPD samples were imputed by using SHAPEIT and IMPUTE2 with 1000 Genomes Project's Phase I v3 reference genome. SNP-based associations were tested with PLINK, whereas PRS analyses were carried out by using PRSice. PRS scores for each NEO-FFI personality traits were computed from the publicly available summary result files of the first phase of the Genetics of Personality Consortium GWAS meta-analysis (GPC1, 10 cohorts, n=17,375 subjects). All PRS models were adjusted for sex, age and potential population stratification. Results Nominally significant PRS prediction of suicidal attempt in the target sample were only shown for Conscientiousness at pt Discussion Polygenic profile score analysis suggest shared genetic etiology between suicidal attempt and certain personality traits. Since Conscientiousness kinks both externalizing and internalizing behaviors, and low Conscientiousness scores and high Extraversion scores correlate with impaired executive functioning, our findings confirm the involvement of executive functions in the pathomechanism of suicide and support the theory of a general psychopathologic dimension. However, due to the relatively small size of the target sample, and the modest strength of PRS predictions, our findings should be interpreted cautiously.

Research Article Genomic association study for adaptability traits in four Colombian cattle breeds

Research Article Genomic association study for adaptability traits in four Colombian cattle breeds

Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the -distributed statistics provide a good control of the type I error rate. The -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels and . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

Urotensin-II gene rs228648 polymorphism associated with the risk of diabetes mellitus.

Background: Urotensin-II (UII) rs228648 polymorphism has been reported to be associated with the risk of diabetes mellitus (DM) with inconsistent results. The present study sought to reassess the relationship between this polymorphism and susceptibility to DM by meta-analysis.Methods: Relevant eligible studies and whole genome association study (GWAS) data electronically searched were pooled to evaluate the strength of the association with odds ratios (ORs) and 95% confidence intervals (CIs).Results: Seven case–control studies involving 894 cases and 1186 controls were finally included in the meta-analysis. Overall analyses indicated that UII gene rs228648 variant was significantly associated with reduced risk of DM (allele, A vs. G: OR = 0.68, 95%CI = 0.56–0.82; dominant, AA+GA vs. GG: OR = 0.70, 95%CI = 0.53–0.91; homozygote, AA vs. GG: OR = 0.41, 95%CI = 0.28–0.61; recessive, AA vs. GA+GG: OR = 0.36, 95%CI = 0.19–0.71). In subgroup analyses based on ethnicity, the results showed a significant association of rs228648 polymorphism with decreased risk of DM in Chinese population under all five genetic models as well as in non-Chinese population under heterozygote and recessive models. Stratified analyses by specific type of DM also presented a significant association for common diabetes mellitus (CDM) under allele and homozygote as well as gestational diabetes mellitus (GDM) under all genetic models except for homozygote model. However, the synthetic analysis with GWAS data suggested an increased risk of DM with rs228648 effect allele in European population (OR = 1.01, 95%CI = 1.00–1.02).Conclusion: The present meta-analysis preliminarily suggested a potentially opposite role of rs228648 polymorphism associated with DM risk in the Chinese and European population. Further studies are in great request to verify the results.

Genetic variants influence on the placenta regulatory landscape.

From genomic association studies, quantitative trait loci analysis, and epigenomic mapping, it is evident that significant efforts are necessary to define genetic-epigenetic interactions and understand their role in disease susceptibility and progression. For this reason, an analysis of the effects of genetic variation on gene expression and DNA methylation in human placentas at high resolution and whole-genome coverage will have multiple mechanistic and practical implications. By producing and analyzing DNA sequence variation (n = 303), DNA methylation (n = 303) and mRNA expression data (n = 80) from placentas from healthy women, we investigate the regulatory landscape of the human placenta and offer analytical approaches to integrate different types of genomic data and address some potential limitations of current platforms. We distinguish two profiles of interaction between expression and DNA methylation, revealing linear or bimodal effects, reflecting differences in genomic context, transcription factor recruitment, and possibly cell subpopulations. These findings help to clarify the interactions of genetic, epigenetic, and transcriptional regulatory mechanisms in normal human placentas. They also provide strong evidence for genotype-driven modifications of transcription and DNA methylation in normal placentas. In addition to these mechanistic implications, the data and analytical methods presented here will improve the interpretability of genome-wide and epigenome-wide association studies for human traits and diseases that involve placental functions.

Approaches to variant discovery for conifer transcriptome sequencing.

There is a wide diversity of bioinformatic tools available for the assembly of next generation sequence and subsequence variant calling to identify genetic markers at scale. Integration of genomics tools such as genomic selection, association studies, pedigree analysis and analysis of genetic diversity, into operational breeding is a goal for New Zealand’s most widely planted exotic tree species, Pinus radiata. In the absence of full reference genomes for large megagenomes such as in conifers, RNA sequencing in a range of genotypes and tissue types, offers a rich source of genetic markers for downstream application. We compared nine different assembler and variant calling software combinations in a single transcriptomic library and found that Single Nucleotide Polymorphism (SNPs) discovery could vary by as much as an order of magnitude (8,061 SNPs up to 86,815 SNPs). The assembler with the best realignment of the packages trialled, Trinity, in combination with several variant callers was then applied to a much larger multi-genotype, multi-tissue transcriptome and identified 683,135 in silico SNPs across a predicted 449,951 exons when mapped to the Pinus taeda ver 1.01e reference.

Opening the toolkit for genetic analysis and control of Anopheles mosquito vectors.

Anopheles is the only genus of mosquitoes that transmit human malaria and consequently the focus of large scale genome and transcriptome-wide association studies. Genetic tools to define the function of the candidate genes arising from these analyses are vital. Moreover, genome editing offers the potential to modify Anopheles population structure at local and global scale to provide complementary tools towards the ultimate goal of malaria elimination. Major breakthroughs in Anopheles genetic analysis came with the development of germline transformation and RNA interference technology. Yet, the field has been revolutionised again by precise genome editing now possible through site-specific nucleases. Here we review the components of the current genetic toolkit available to study Anopheles, focusing particularly on how these technical advances are used to gain insight into malaria transmission and the design of genetic methods to control Anopheles vectors.

SnpReady: a tool to assist breeders in genomic analysis

The snpReady R package is a new instrument developed to help breeders in genomic projects such as genomic prediction and association studies. This package offers three different methods to build the genomic relationship matrix, a new imputation method for missing markers based on Wright’s theory, and a population genetic overview. Therefore, we implemented three functions (raw.data, G.matrix, and popgen). Hence, this tool allows the raw data to be transformed from different genotyping platforms to numeric matrices and performs quality control (missing data and allele frequency). Moreover, the package generates and exports four different relationship matrices (proposed by Yang et al. (N 569:565–569, 2010), VanRaden (JDS 91:4414–23, 2008), and the Gaussian kernel) depending on the purpose and software to be used in further analysis. Finally, based on the genotypic matrix, the package estimates the genetic variability, effective population size, and endogamy, among other population genetic parameters. Empirical comparisons between the method of imputation proposed and other well-known approaches have shown a lower accuracy of imputation, however, with no significant impact on the genome prediction accuracies when a lower amount of missing data is allowed. The functions and arguments were designed to carry out the preparation of genomic datasets in a straightforward, fast, and more computationally efficient way. The package and its details are available at CRAN or http://www.github.com/italo-granato/snpReady .

Genomic variations and association study of agronomic traits in flax

BackgroundFlax (Linum usitatissimum. L) is an ancient oilseed and natural fiber crop. It could be divided into three categories by use, namely oil flax, fiber flax and oil-fiber dual purpose (OF). Cultivated flax is widely used in the food and textile industry. It is of great significance to elucidate the genetic characteristics of flax collections for accelerating the process of breeding improvement in this dual purpose crop. With the development of next-generation sequencing, we can use new methods, such as SLAF-seq (specific-locus amplified fragment sequencing), to decode unknown genomes of species. In this study, a high-through sequencing of flax collections using SLAF-seq was conducted. The evolutionary tendency was defined and candidate genes associated with agronomic traits of flax species were identified by Genome-Wide Association Studying (GWAS).ResultsA flax collection consisting of 224 varieties were sequenced by SLAF-seq. In total, 346,639 SLAF tags were developed from all accessions, with an average sequencing depth of 7.19 for each accession. A total of 584,987 SNPs (single nucleotide polymorphism) with an MAF > 0.05 were identified from these SLAFs. The population structure division and phylogenetic analysis indicated a strong divergence among three kinds of flax groups. The genome-wide variation uncovered that oil flax had the highest genetic diversity and was considered to be the ancestor of fiber flax and oil-fiber flax. Sixteen associated peak SNPs for six traits were obtained by GWAS of oil-related traits using EMMAX (efficient mixed-model association eXpedited). Candidate genes and their related pathway were evaluated. A new GWAS was developed for fiber properties using the GLM (General linear model) model and a number of loci were identified.ConclusionsTo our knowledge, this is the first study on discovery multiple loci for important agronomic traits of flax species using GWAS strategy. These results will provide the highest possibility of incorporating both high fiber and good oil traits in a single variety.

A Bayesian Framework for Generalized Linear Mixed Modeling Identifies New Candidate Loci for Late-Onset Alzheimer's Disease.

Recent technical and methodological advances have greatly enhanced genome-wide association studies (GWAS). The advent of low-cost, whole-genome sequencing facilitates high-resolution variant identification, and the development of linear mixed models (LMM) allows improved identification of putatively causal variants. While essential for correcting false positive associations due to sample relatedness and population stratification, LMMs have commonly been restricted to quantitative variables. However, phenotypic traits in association studies are often categorical, coded as binary case-control or ordered variables describing disease stages. To address these issues, we have devised a method for genomic association studies that implements a generalized LMM (GLMM) in a Bayesian framework, called Bayes-GLMM. Bayes-GLMM has four major features: (1) support of categorical, binary, and quantitative variables; (2) cohesive integration of previous GWAS results for related traits; (3) correction for sample relatedness by mixed modeling; and (4) model estimation by both Markov chain Monte Carlo sampling and maximal likelihood estimation. We applied Bayes-GLMM to the whole-genome sequencing cohort of the Alzheimer’s Disease Sequencing Project. This study contains 570 individuals from 111 families, each with Alzheimer’s disease diagnosed at one of four confidence levels. Using Bayes-GLMM we identified four variants in three loci significantly associated with Alzheimer’s disease. Two variants, rs140233081 and rs149372995, lie between PRKAR1B and PDGFA. The coded proteins are localized to the glial-vascular unit, and PDGFA transcript levels are associated with Alzheimer’s disease-related neuropathology. In summary, this work provides implementation of a flexible, generalized mixed-model approach in a Bayesian framework for association studies.

An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder

Genomic association studies of common or rare protein-coding variation have established robust statistical approaches to account for multiple testing. Here we present a comparable framework to evaluate rare and de novo noncoding single-nucleotide variants, insertion/deletions, and all classes of structural variation from whole-genome sequencing (WGS). Integrating genomic annotations at the level of nucleotides, genes, and regulatory regions, we define 51,801 annotation categories. Analyses of 519 autism spectrum disorder families did not identify association with any categories after correction for 4,123 effective tests. Without appropriate correction, biologically plausible associations are observed in both cases and controls. Despite excluding previously identified gene-disrupting mutations, coding regions still exhibited the strongest associations. Thus, in autism, the contribution of de novo noncoding variation is probably modest in comparison to that of de novo coding variants. Robust results from future WGS studies will require large cohorts and comprehensive analytical strategies that consider the substantial multiple-testing burden.

A genome-wide association study identifies candidate loci associated to syringomyelia secondary to Chiari-like malformation in Cavalier King Charles Spaniels

BackgroundSyringomyelia (SM) is a common condition affecting brachycephalic toy breed dogs and is characterized by the development of fluid-filled cavities within the spinal cord. It is often concurrent with a complex developmental malformation of the skull and craniocervical vertebrae called Chiari-like malformation (CM) characterized by a conformational change and overcrowding of the brain and cervical spinal cord particularly at the craniocervical junction. CM and SM have a polygenic mode of inheritance with variable penetrance.ResultsWe identified six cranial T1-weighted sagittal MRI measurements that were associated to maximum transverse diameter of the syrinx cavity. Increased syrinx transverse diameter has been correlated previously with increased likelihood of behavioral signs of pain. We next conducted a whole genome association study of these traits in 65 Cavalier King Charles Spaniel (CKCS) dogs (33 controls, 32 with extreme phenotypes). Two loci on CFA22 and CFA26 were found to be significantly associated to two traits associated with a reduced volume and altered orientation of the caudal cranial fossa. Their reconstructed haplotypes defined two associated regions that harbor only two genes: PCDH17 on CFA22 and ZWINT on CFA26. PCDH17 codes for a cell adhesion molecule expressed specifically in the brain and spinal cord. ZWINT plays a role in chromosome segregation and its expression is increased with the onset of neuropathic pain. Targeted genomic sequencing of these regions identified respectively 37 and 339 SNPs with significantly associated P values. Genotyping of tagSNPs selected from these 2 candidate loci in an extended cohort of 461 CKCS (187 unaffected, 274 SM affected) identified 2 SNPs on CFA22 that were significantly associated to SM strengthening the candidacy of this locus in SM development.ConclusionsWe identified 2 loci on CFA22 and CFA26 that contained only 2 genes, PCDH17 and ZWINT, significantly associated to two traits associated with syrinx transverse diameter. The locus on CFA22 was significantly associated to SM secondary to CM in the CKCS dog breed strengthening its candidacy for this disease. This study will provide an entry point for identification of the genetic factors predisposing to this condition and its underlying pathogenic mechanisms.

Mitochondrial function and autophagy: integrating proteotoxic, redox, and metabolic stress in Parkinson's disease.

Parkinson's disease (PD) is a movement disorder with widespread neurodegeneration in the brain. Significant oxidative, reductive, metabolic, and proteotoxic alterations have been observed in PD postmortem brains. The alterations of mitochondrial function resulting in decreased bioenergetic health is important and needs to be further examined to help develop biomarkers for PD severity and prognosis. It is now becoming clear that multiple hits on metabolic and signaling pathways are likely to exacerbate PD pathogenesis. Indeed, data obtained from genetic and genome association studies have implicated interactive contributions of genes controlling protein quality control and metabolism. For example, loss of key proteins that are responsible for clearance of dysfunctional mitochondria through a process called mitophagy has been found to cause PD, and a significant proportion of genes associated with PD encode proteins involved in the autophagy-lysosomal pathway. In this review, we highlight the evidence for the targeting of mitochondria by proteotoxic, redox and metabolic stress, and the role autophagic surveillance in maintenance of mitochondrial quality. Furthermore, we summarize the role of α-synuclein, leucine-rich repeat kinase 2, and tau in modulating mitochondrial function and autophagy. Among the stressors that can overwhelm the mitochondrial quality control mechanisms, we will discuss 4-hydroxynonenal and nitric oxide. The impact of autophagy is context depend and as such can have both beneficial and detrimental effects. Furthermore, we highlight the potential of targeting mitochondria and autophagic function as an integrated therapeutic strategy and the emerging contribution of the microbiome to PD susceptibility.

Genome-wide scan for commons SNPs affecting bovine leukemia virus infection level in dairy cattle.

BackgroundBovine leukemia virus (BLV) infection is omnipresent in dairy herds causing direct economic losses due to trade restrictions and lymphosarcoma-related deaths. Milk production drops and increase in the culling rate are also relevant and usually neglected. The BLV provirus persists throughout a lifetime and an inter-individual variation is observed in the level of infection (LI) in vivo. High LI is strongly correlated with disease progression and BLV transmission among herd mates. In a context of high prevalence, classical control strategies are economically prohibitive. Alternatively, host genomics studies aiming to dissect loci associated with LI are potentially useful tools for genetic selection programs tending to abrogate the viral spreading. The LI was measured through the proviral load (PVL) set–point and white blood cells (WBC) counts. The goals of this work were to gain insight into the contribution of SNPs (bovine 50KSNP panel) on LI variability and to identify genomics regions underlying this trait.ResultsWe quantified anti–p24 response and total leukocytes count in peripheral blood from 1800 cows and used these to select 800 individuals with extreme phenotypes in WBCs and PVL. Two case-control genomic association studies using linear mixed models (LMMs) considering population stratification were performed. The proportion of the variance captured by all QC-passed SNPs represented 0.63 (SE ± 0.14) of the phenotypic variance for PVL and 0.56 (SE ± 0.15) for WBCs. Overall, significant associations (Bonferroni’s corrected -log10p > 5.94) were shared for both phenotypes by 24 SNPs within the Bovine MHC. Founder haplotypes were used to measure the linkage disequilibrium (LD) extent (r2 = 0.22 ± 0.27 at inter-SNP distance of 25−50 kb). The SNPs and LD blocks indicated genes potentially associated with LI in infected cows: i.e. relevant immune response related genes (DQA1, DRB3, BOLA-A, LTA, LTB, TNF, IER3, GRP111, CRISP1), several genes involved in cell cytoskeletal reorganization (CD2AP, PKHD1, FLOT1, TUBB5) and modelling of the extracellular matrix (TRAM2, TNXB). Host transcription factors (TFs) were also highlighted (TFAP2D; ABT1, GCM1, PRRC2A).ConclusionsData obtained represent a step forward to understand the biology of BLV–bovine interaction, and provide genetic information potentially applicable to selective breeding programs.

Whole genome association study of brain-wide imaging phenotypes: A study of the ping cohort.

Neuropsychological disorders have a biological basis rooted in brain function, and neuroimaging data are expected to better illuminate the complex genetic basis of neuropsychological disorders. Because they are biological measures, neuroimaging data avoid biases arising from clinical diagnostic criteria that are subject to human understanding and interpretation. A challenge with analyzing neuroimaging data is their high dimensionality and complex spatial relationships. To tackle this challenge, we introduced a novel distance covariance tests that can assess the association between genetic markers and multivariate diffusion tensor imaging measurements, and analyzed a genome-wide association study (GWAS) dataset collected by the Pediatric Imaging, Neurocognition, and Genetics (PING) study. We also considered existing approaches as comparisons. Our results showed that, after correcting for multiplicity, distance covariance tests of the multivariate phenotype yield significantly greater power at detecting genetic markers affecting brain structure than standard mass univariate GWAS of individual neuroimaging biomarkers. Our results underscore the usefulness of utilizing the distance covariance to incorporate neuroimaging data in GWAS.

PEATH: single-individual haplotyping by a probabilistic evolutionary algorithm with toggling.

Single-individual haplotyping (SIH) is critical in genomic association studies and genetic diseases analysis. However, most genomic analysis studies do not perform haplotype-phasing analysis due to its complexity. Several computational methods have been developed to solve the SIH problem, but these approaches have not generated sufficiently reliable haplotypes. Here, we propose a novel SIH algorithm, called PEATH (Probabilistic Evolutionary Algorithm with Toggling for Haplotyping), to achieve more accurate and reliable haplotyping. The proposed PEATH method was compared to the most recent algorithms in terms of the phased length, N50 length, switch error rate and minimum error correction. The PEATH algorithm consistently provides the best phase and N50 lengths, as long as possible, given datasets. In addition, verification of the simulation data demonstrated that the PEATH method outperforms other methods on high noisy data. Additionally, the experimental results of a real dataset confirmed that the PEATH method achieved comparable or better accuracy. Source code of PEATH is available at https://github.com/jcna99/PEATH. jkrhee@catholic.ac.kr or sooyong.shin@gmail.com. Supplementary data are available at Bioinformatics online.

Genetics of kidney diseases in 2017: Unveiling the genetic architecture of kidney disease.

Technical advances in genome sequencing and association studies have yielded critical insights into the genetic architecture of kidney diseases. Here, I summarize four key studies from 2017 that deciphered the genetic basis of known and novel diseases and provided insights into the mechanisms of glomerular, developmental defects and manifestations of kidney disorders.