QTL for the heritable inverted teat defect in pigs

The mothering ability of a sow largely depends on the shape and function of the mammary gland. The aim of this study was to identify QTL for the heritable inverted teat defect, a condition characterized by disturbed development of functional teats. A QTL analysis was conducted in a porcine experimental population based on Duroc and Berlin Miniature pigs (DUMI). The significant QTL were confirmed by linkage analysis in commercial pigs according to the affected sib pair design and refined by family-based association test (FBAT). Nonparametric linkage (NPL) analysis revealed five significant and seven suggestive QTL for the inverted teat defect in the porcine experimental population. In commercial dam lines five significant NPL values were detected. QTL regions in overlapping marker intervals or close proximity in both populations were found on SSC3, SSC4, SSC6, and SSC11. SSC6 revealed QTL in both populations at different positions, indicating the segregation of at least two QTL. The results confirm the previously proposed polygenic inheritance of the inverted teat defect and, for the first time, point to genomic regions harboring relevant genes. The investigation revealed variation of the importance of QTL in the various populations due to either differences in allele frequencies and statistical power or differences in the genetic background that modulates the impact of the liability loci on the expression of the disease. The QTL study enabled us to name a number of plausible positional candidate genes. The correspondence of QTL regions for the inverted teat defect and previously mapped QTL for teat number are in line with the etiologic relationship of these traits.     

QTL and candidate genes for fecundity in sows

Fecundity in pigs is a trait of major economic interest but low heritability. For the improvement of fecundity, genetic markers for selection are desirable and therefore, several searches for genetic variation influencing fecundity have been performed. The aim of this review is to compare and to evaluate all published QTL analyses and candidate gene approaches concerning reproductive traits in sows. For this purpose, we present a comprehensive cytogenetic map comprising 54 QTL and 11 candidate genes with influence on reproductive traits. The evaluation and comparison of the results showed similarities, but also marked differences among studies. Reasons for different results are multicausal and are due to differences between resource populations, number of evaluated animals, mating systems, measured phenotypical traits and environmental influences. We could show that chromosome 8 and to a lower extend chromosome 7 are the most important chromosomes with regard to reproductive traits in pigs. For further research, fine mapping of the identified QTL regions is necessary in order to confirm and to narrow the most likely chromosomal intervals. Although difficult to perform, an advance would be a standardization of the experimental setup in particular, in respect to the collection of phenotypic data. Furthermore, we suggest to publish the information on further identified QTL and candidate genes as comprehensive and accurate as possible in order to allow a more transparent comparison and collation of the results.     

Identification of neural outgrowth genes using genome-wide RNAi

While genetic screens have identified many genes essential for neurite outgrowth, they have been limited in their ability to identify neural genes that also have earlier critical roles in the gastrula, or neural genes for which maternally contributed RNA compensates for gene mutations in the zygote. To address this, we developed methods to screen the Drosophila genome using RNA-interference (RNAi) on primary neural cells and present the results of the first full-genome RNAi screen in neurons. We used live-cell imaging and quantitative image analysis to characterize the morphological phenotypes of fluorescently labelled primary neurons and glia in response to RNAi-mediated gene knockdown. From the full genome screen, we focused our analysis on 104 evolutionarily conserved genes that when downregulated by RNAi, have morphological defects such as reduced axon extension, excessive branching, loss of fasciculation, and blebbing. To assist in the phenotypic analysis of the large data sets, we generated image analysis algorithms that could assess the statistical significance of the mutant phenotypes. The algorithms were essential for the analysis of the thousands of images generated by the screening process and will become a valuable tool for future genome-wide screens in primary neurons. Our analysis revealed unexpected, essential roles in neurite outgrowth for genes representing a wide range of functional categories including signalling molecules, enzymes, channels, receptors, and cytoskeletal proteins. We also found that genes known to be involved in protein and vesicle trafficking showed similar RNAi phenotypes. We confirmed phenotypes of the protein trafficking genes Sec61alpha and Ran GTPase using Drosophila embryo and mouse embryonic cerebral cortical neurons, respectively. Collectively, our results showed that RNAi phenotypes in primary neural culture can parallel in vivo phenotypes, and the screening technique can be used to identify many new genes that have important functions in the nervous system.     

Identification of marker genes in Alzheimer's disease using a machine-learning model

Alzheimer''s Disease (AD) is one of the most common causes of dementia, mostly affecting the elderly population. Currently, there is no proper diagnostic tool or method available for the detection of AD. The present study used two distinct data sets of AD genes, which could be potential biomarkers in the diagnosis. The differentially expressed genes (DEGs) curated from both datasets were used for machine learning classification, tissue expression annotation and co-expression analysis. Further, CNPY3, GPR84, HIST1H2AB, HIST1H2AE, IFNAR1, LMO3, MYO18A, N4BP2L1, PML, SLC4A4, ST8SIA4, TLE1 and N4BP2L1 were identified as highly significant DEGs and exhibited co-expression with other query genes. Moreover, a tissue expression study found that these genes are also expressed in the brain tissue. In addition to the earlier studies for marker gene identification, we have considered a different set of machine learning classifiers to improve the accuracy rate from the analysis. Amongst all the six classification algorithms, J48 emerged as the best classifier, which could be used for differentiating healthy and diseased samples. SMO/SVM and Logit Boost further followed J48 to achieve the classification accuracy.     

Identification of susceptibility genes for complex diseases using pooling-based genome-wide association scans

The success of genome-wide association studies (GWAS) to identify risk loci of complex diseases is now well-established. One persistent major hurdle is the cost of those studies, which make them beyond the reach of most research groups. Performing GWAS on pools of DNA samples may be an effective strategy to reduce the costs of these studies. In this study, we performed pooling-based GWAS with more than 550,000 SNPs in two case-control cohorts consisting of patients with Type II diabetes (T2DM) and with chronic rhinosinusitis (CRS). In the T2DM study, the results of the pooling experiment were compared to individual genotypes obtained from a previously published GWAS. TCF7L2 and HHEX SNPs associated with T2DM by the traditional GWAS were among the top ranked SNPs in the pooling experiment. This dataset was also used to refine the best strategy to correctly identify SNPs that will remain significant based on individual genotyping. In the CRS study, the top hits from the pooling-based GWAS located within ten kilobases of known genes were validated by individual genotyping of 1,536 SNPs. Forty-one percent (598 out of the 1,457 SNPs that passed quality control) were associated with CRS at a nominal P value of 0.05, confirming the potential of pooling-based GWAS to identify SNPs that differ in allele frequencies between two groups of subjects. Overall, our results demonstrate that a pooling experiment on high-density genotyping arrays can accurately determine the minor allelic frequency as compared to individual genotyping and produce a list of top ranked SNPs that captures genuine allelic differences between a group of cases and controls. The low cost associated with a pooling-based GWAS clearly justifies its use in screening for genetic determinants of complex diseases. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identification of macrophage-related candidate genes in lupus nephritis using bioinformatics analysis

Lupus nephritis (LN) is a chronic autoimmune disorder. Here we try to identify the candidate genes in macrophages related to LN. We performed a systematic search in the Gene Expression Omnibus (GEO) database for microarray in human mononuclear cells and mouse macrophages of LN. The results of clustering and venn analysis of different GEO datasets showed that 8 genes were up-regulated and 2 genes down-regulated in samples from both human and mouse LN. The data from gene network and GO analysis revealed that CD38 and CCL2 were localized in the core of the network. Immunofluorescence staining showed that CD38 expression was markedly increased in macrophages from kidneys with LN. Our study identifies the gene expression profile for macrophages and demonstrated the induction of CCL2 and CD38 in macrophages from patients with LN. However, regarding the limited patient number included in this study, the results are preliminary and more studies are still needed to further decipher the macrophage-related candidate genes for the pathogenesis of LN.     

A genome-wide association study uncovers novel genomic regions and candidate genes of yield-related traits in upland cotton

Key message

A total of 62 SNPs associated with yield-related traits were identified by a GWAS. Based on significant SNPs, two candidate genes pleiotropically increase lint yield.

Abstract

Improved fibre yield is considered a constant goal of upland cotton (Gossypium hirsutum) breeding worldwide, but the understanding of the genetic basis controlling yield-related traits remains limited. To better decipher the molecular mechanism underlying these traits, we conducted a genome-wide association study to determine candidate loci associated with six yield-related traits in a population of 719 upland cotton germplasm accessions; to accomplish this, we used 10,511 single-nucleotide polymorphisms (SNPs) genotyped by an Illumina CottonSNP63K array. Six traits, including the boll number, boll weight, lint percentage, fruit branch number, seed index and lint index, were assessed in multiple environments; large variation in all phenotypes was detected across accessions. We identified 62 SNP loci that were significantly associated with different traits on chromosomes A07, D03, D05, D09, D10 and D12. A total of 689 candidate genes were screened, and 27 of them contained at least one significant SNP. Furthermore, two genes (Gh_D03G1064 and Gh_D12G2354) that pleiotropically increase lint yield were identified. These identified SNPs and candidate genes provide important insights into the genetic control underlying high yields in G. hirsutum, ultimately facilitating breeding programmes of high-yielding cotton.

     

Identification of a novel candidate gene for rolled leaf in rice

A semi-narrow and adaxially rolled leaf mutant, rl15(t), was induced from Korean japonica rice cultivar Ilpum by chemical mutagenesis using ethyl methanesulfonate. We characterized the mutant and identified the novel gene causing the mutant phenotype. Cytological analysis of mutant leaves indicated that the adaxial leaf-rolling phenotype is due to the reduced size and number of bulliform cells in the mutant. Genetic analysis showed that the rolled leaf trait is controlled by a single recessive gene, designated rl15(t). Using an F₂ mapping population generated from a cross between Milyang23 and the mutant, we mapped the candidate region to a 174 kb interval on the long arm of chromosome 1 near the centromeric region. Through whole genome sequencing in bulk and MutMap analysis, we identified the causal SNP within the candidate region. The results of RT-PCR analysis indicated that a splicing error occurred due to a base change from G to A at the beginning of the fifth intron of LOC_Os01g37837, which encodes a putative seryl-tRNA synthetase, resulting in the mutant phenotype. Further study of the rl15(t) gene will facilitate analysis of leaf architecture and morphogenesis in rice plants.     

Commonality of functional annotation: a method for prioritization of candidate genes from genome-wide linkage studies

Linkage studies of complex traits frequently yield multiple linkage regions covering hundreds of genes. Testing each candidate gene from every region is prohibitively expensive and computational methods that simplify this process would benefit genetic research. We present a new method based on commonality of functional annotation (CFA) that aids dissection of complex traits for which multiple causal genes act in a single pathway or process. CFA works by testing individual Gene Ontology (GO) terms for enrichment among candidate gene pools, performs multiple hypothesis testing adjustment using an estimate of independent tests based on correlation of GO terms, and then scores and ranks genes annotated with significantly-enriched terms based on the number of quantitative trait loci regions in which genes bearing those annotations appear. We evaluate CFA using simulated linkage data and show that CFA has good power despite being conservative. We apply CFA to published linkage studies investigating age-of-onset of Alzheimer's disease and body mass index and obtain previously known and new candidate genes. CFA provides a new tool for studies in which causal genes are expected to participate in a common pathway or process and can easily be extended to utilize annotation schemes in addition to the GO.     

Potential novel candidate polymorphisms identified in genome-wide association study for breast cancer susceptibility

Previous genome-wide association studies (GWAS) have shown several risk alleles to be associated with breast cancer. However, the variants identified so far contribute to only a small proportion of disease risk. The objective of our GWAS was to identify additional novel breast cancer susceptibility variants and to replicate these findings in an independent cohort. We performed a two-stage association study in a cohort of 3,064 women from Alberta, Canada. In Stage I, we interrogated 906,600 single nucleotide polymorphisms (SNPs) on Affymetrix SNP 6.0 arrays using 348 breast cancer cases and 348 controls. We used single-locus association tests to determine statistical significance for the observed differences in allele frequencies between cases and controls. In Stage II, we attempted to replicate 35 significant markers identified in Stage I in an independent study of 1,153 cases and 1,215 controls. Genotyping of Stage II samples was done using Sequenom Mass-ARRAY iPlex platform. Six loci from four different gene regions (chromosomes 4, 5, 16 and 19) showed statistically significant differences between cases and controls in both Stage I and Stage II testing, and also in joint analysis. The identified variants were from EDNRA, ROPN1L, C16orf61 and ZNF577 gene regions. The presented joint analyses from the two-stage study design were not significant after genome-wide correction. The SNPs identified in this study may serve as potential candidate loci for breast cancer risk in a further replication study in Stage III from Alberta population or independent validation in Caucasian cohorts elsewhere.     

Identification of candidate genes in the type 2 diabetes modifier locus using expression QTL

To identify new genetic determinants relevant to type 2 diabetes (T2D), diabetic F2 progeny were generated by intercrossing F1 mice obtained from a cross of BKS.Cg-Lepr(db)+/+m and DBA/2, and T2D-related phenotypes were measured. In the F2 population, increased susceptibility to diabetes and obesity was observed. We also detected the major quantitative trait loci (QTL) modifying the severity of diabetes on chromosome 9, where peaks of logarithm of odds (LOD) overlapped for three traits. To identify candidate genes in the QTL intervals, we combined "expression QTL" (eQTL), taking mRNA levels as quantitative traits, and "interstrain sequence variations, including cSNPs." As a result, four genes were identified from cosegregation of clinical QTL with eQTL and 13 genes were found from interstrain cSNPs as candidates in the T2D modifier QTL. Our combined approach shows the acceleration of the discovery of candidate genes in the QTL of interest, spanning several megabases.     

Identification of functional candidate genes for drought tolerance in rice

Drought tolerance (DT) in rice is known to be controlled by many quantitative trait loci (QTLs) and involved differential expression of large numbers of genes, but linking QTLs with their underlying genes remains the most challenging issue in plant molecular biology. To shed some light on this issue, differential gene expression in response to PEG simulated drought in 3 unique genetic materials (a lowland rice, IR64 and its derived line, PD86 which has 11 introgressed DT QTLs, and a upland rice IRAT109) was investigated using a PCR-based subtractive hybridization strategy. More than 300 unique subtracted cDNA sequences, covering genes of diverse cellular activities and functions, were identified and confirmed by semi-quantitative and quantitative RT-PCR. Detailed bioinformatics analyses of the data revealed two interesting results. First, the levels and mechanisms of DT of the three rice lines were associated with the number and types of differentially expressed genes, suggesting different DT mechanisms in rice are controlled by different sets of genes and different metabolic pathways, and most differentially expressed genes under drought were able to contribute to DT. Second, there appeared a high correspondence in genomic location between DT QTLs and clusters of differentially expressed genes in rice, suggesting some DT QTLs may represent clusters of co-regulated and functionally related genes. Thus, differential gene expression analyses using genetically characterized materials can provide additional insights into the molecular basis of QTLs and convergent evidence to shortlist the candidate genes for target QTLs. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Bin-Ying Fu and Jian-Hua Xiong are contributed to this work equally.     

The identification of candidate radio marker genes using a coexpression network analysis in gamma‐irradiated rice

Plant physiological and biochemical processes are significantly affected by gamma irradiation stress. In addition, gamma‐ray (GA) differentially affects gene expression across the whole genome. In this study, we identified radio marker genes (RMGs) responding only to GA stress compared with six abiotic stresses (chilling, cold, anoxia, heat, drought and salt) in rice. To analyze the expression patterns of differentially expressed genes (DEGs) in gamma‐irradiated rice plants against six abiotic stresses, we conducted a hierarchical clustering analysis by using a complete linkage algorithm. The up‐ and downregulated DEGs were observed against six abiotic stresses in three and four clusters among a total of 31 clusters, respectively. The common gene ontology functions of upregulated DEGs in clusters 9 and 19 are associated with oxidative stress. In a Pearson's correlation coefficient analysis, GA stress showed highly negative correlation with salt stress. On the basis of specific data about the upregulated DEGs, we identified the 40 candidate RMGs that are induced by gamma irradiation. These candidate RMGs, except two genes, were more highly induced in rice roots than in other tissues. In addition, we obtained other 38 root‐induced genes by using a coexpression network analysis of the specific upregulated candidate RMGs in an ARACNE algorithm. Among these genes, we selected 16 RMGs and 11 genes coexpressed with three RMGs to validate coexpression network results. RT‐PCR assay confirmed that these genes were highly upregulated in GA treatment. All 76 genes (38 root‐induced genes and 38 candidate RMGs) might be useful for the detection of GA sensitivity in rice roots.