We showed that ENO1 expression was reduced by 50% in the patient cells mimicking the haploinsufficiency caused by a monosomy. Expression analysis in mouse development revealed a strong expression of both transcripts in the central nervous system. Interestingly, 1p36 monosomy has been associated with bilateral polymicrogyria [20,21]. Although 1p36 deletions are common, early descriptions did not provide cerebral imaging results [5, 28-30]. More recently however, correlations between 1p36 rearrangements and polymicrogyria were reported. In a large study of 64 patients presenting a deletion of 1p36 , 20% of the cases had polymicrogyria. Combined with three additional reports [3,21,31], these studies allowed to define a putative minimal critical region of 3.8 Mb in 1p36 for polymicrogyria localized between 1Mb and 4.8Mb from the 1p telomere (see Figure 3B). The 1p36 rearrangement described here is the smallest identified to date in association with polymicrogyria. Although it lies outside of the minimal critical region defined previously , it contains the RERE gene, proposed to be a candidategene for polymicrogyria . RERE encodes a member of the arginine/glutamic acid repeat-containing protein family. This protein was shown to be a nuclear receptor co-regulator interacting with NR2F2 and NR2E1 [32,33] and to interact with histone deacetylases in the mouse embryo [31,34], but its function is not completely elucidated . Different mouse models with null or hypomorphic RERE alleles have been produced [35,36,37]. These models revealed that a deficiency of RERE leads to a wide range of developmental defects including the brain, heart and kidney. One of these was described with abnormal cerebellum development  but without neuronal migration defects in the cerebral cortex. Moreover, in human, a new genetic syndrome was recently described in RERE variant cases [38,39]. It is characterized by neurodevelopmental disorder that may be accompanied by brain, eye and/or heart anomalies, but none of the 19 individuals reported in the Jordan et al and Fregeau et al. studies had any kind of polymicrogyria.
The gene GAD2 encoding the glutamic acid decarboxylase enzyme (GAD65) is a positional candidategene for obesity on Chromosome 10p11–12, a susceptibility locus for morbid obesity in four independent ethnic populations. GAD65 catalyzes the formation of c-aminobutyric acid (GABA), which interacts with neuropeptide Y in the paraventricular nucleus to contribute to stimulate food intake. A case-control study (575 morbidly obese and 646 control subjects) analyzing GAD2 variants identified both a protective haplotype, including the most frequent alleles of single nucleotide polymorphisms (SNPs) þ61450 C.A and þ83897 T.A (OR ¼ 0.81, 95% CI [0.681–0.972], p ¼ 0.0049) and an at-risk SNP (243 A.G) for morbid obesity (OR ¼ 1.3, 95% CI [1.053–1.585], p ¼ 0.014). Furthermore, familial-based analyses confirmed the association with the obesity of SNP þ61450 C.A and þ83897 T.A haplotype (v 2 ¼ 7.637, p ¼ 0.02). In the murine insulinoma cell line bTC3, the G at-risk allele of SNP 243 A.G increased six times GAD2 promoter activity (p , 0.0001) and induced a 6-fold higher affinity for nuclear extracts. The 243 A.G SNP was associated with higher hunger scores (p ¼ 0.007) and disinhibition scores (p ¼ 0.028), as assessed by the Stunkard Three-Factor Eating Questionnaire. As GAD2 is highly expressed in pancreatic b cells, we analyzed GAD65 antibody level as a marker of b- cell activity and of insulin secretion. In the control group, 243 A.G, þ61450 C.A, and þ83897 T.A SNPs were associated with lower GAD65 autoantibody levels (p values of 0.003, 0.047, and 0.006, respectively). SNP þ83897 T.A was associated with lower fasting insulin and insulin secretion, as assessed by the HOMA-B% homeostasis model of b- cell function (p ¼ 0.009 and 0.01, respectively). These data support the hypothesis of the orexigenic effect of GABA in humans and of a contribution of genes involved in GABA metabolism in the modulation of food intake and in the development of morbid obesity.
Received March 26, 2007. Accepted January 1, 2008.
1 Corresponding author: email@example.com
tion. Today there are large data sets for phenotypic records and sparse data sets for genotypes at candidategene loci. To allow simple and better use of the available data, the missing genotypes can be replaced with pre- dicted values. Israel and Weller (1998, 2002) showed that useful estimates of candidategene effects can be calculated under an animal model with regression of milk, fat, and protein yields and SCS on genotype proba- bilities or predicted gene content, defined as the number of copies of a particular allele in the genotype of an animal (Lynch and Walsh, 1997). In large pedigrees with sparse molecular data, the currently available methods for calculation of genotype probabilities may be impractical. In large animal populations, the conver- gence of Markov chain Monte Carlo (MCMC) methods cannot be easily monitored, whereas the estimates from iterative peeling and sparse data can strongly depend on the assumed gene frequency, which remains un- known (Gengler et al., 2007). Recently, a more practical method was proposed to approximate gene content in large pedigrees (Gengler et al., 2007). The new method compared very positively to the iterative peeling ap- proach (van Arendonk et al., 1989) applied to a popula- tion under selection and with known gene frequency.
In light of the potential role of PPAR-γ in pathogenesis of SSc, we hypothesized that genetic variants in the PPARG may influence disease susceptibility. Two coding, non- synonymous PPARG polymorphisms (rs1801282 (P12A) and rs3856806 (C141T)) have been extensively studied in diabetes, coronary artery disease, the metabolic syndrome, and non-alcoholic fatty liver disease [32-35]. The P12A variant has been associated with increased insulin sensitiv- ity, lower body mass and protection from type 2 diabetes , while the C161T variant has been associated with in- creased body weight . In the present studies we sought to conduct a candidategene association approach to inves- tigate common variants in the PPARG gene with SSc.
Candidategene variation in gilthead sea bream reveals complex spatiotemporal selection patterns between
marine and lagoon habitats
B Guinand, C Chauvel, M Lechene, J Tournois, Costas S Tsigenopoulos, A. M. Darnaude, David J. Mckenzie, P.-A Gagnaire
Plum pox virus (PPV), the causing agent of the sharka disease, belongs to the genus Potyvirus that contains the largest number of virus species infecting plants. The virus genome has been extensively characterised and sequenced. How- ever, few data are available on its interactions with the host plant, Prunus. In this study, we are focusing on the clon- ing and characterisation of any candidate genes involved in the expression of the resistance/susceptibility trait and any polymorphic genes putatively involved in the trait variation. In order to clone candidate genes, two main approaches are currently developed: the homology cloning of genes presumed to affect the resistance/susceptibility trait and the differential screening of cDNA pools corresponding to infected and non-infected plant material. The second approach is based on the transcript imaging of the host plant response to PPV infection. Previously, it has been shown that infection by a potyvirus is associated with specific changes in host gene expression, mainly down-regulation, while the expression of some genes remained unchanged. Thereby, in the differential display approach combined to further characterisation of candidategene expression, we aim to monitor host gene expression in response to the virus and to describe a highly regulated interaction between the Prunus host plant and the infecting Plum pox virus.
5 Institute of Marine Biology and Genetics, Hellenic Center for Marine Research, PO Box 2214, Gournes Pediados,
71500 Heraklion, Crete, Greece
ABSTRACT: In marine fishes, the extent to which spatial patterns induced by selection remain stable across generations remains largely unknown. In the gilthead sea bream Sparus aurata, polymorphisms in the growth hormone (GH) and prolactin (Prl) genes can display high levels of differentiation between marine and lagoon habitats. These genotype−environment associations have been attributed to differential selection following larval settlement, but it remains unclear whether selective mortality during later juvenile stages further shapes genetic differences among habitats. We addressed this question by analysing differentiation patterns at GH and Prl markers together with a set of 21 putatively neutral microsatellite loci. We compared genetic variation of spring juveniles that had just settled in 3 ecologically different lagoons against older juveniles sampled from the same sites in autumn, at the onset of winter outmigration. In spring, genetic dif- ferentiation among lagoons was greater than expected from neutrality for both candidategene markers. Surprisingly, this signal disappeared completely in the older juveniles, with no signifi- cant differentiation for either locus a few months later in autumn. We searched for signals of hap- lotype structure within GH and Prl genes using next-generation amplicon deep sequencing. Both genes contained 2 groups of haplotypes, but high similarities among groups indicated that signa- tures of selection, if any, had largely been erased by recombination. Our results are consistent with the view that differential selection operates during early juvenile life in sea bream and high- light the importance of temporal replication in studies of post-settlement selection in marine fish. KEY WORDS: Candidategene · Growth hormone · Prolactin · Genetic differentiation · Amplicon sequencing · Local selection
This candidategene mapping approach was based on an in silico identification and a genetic mapping of genes potentially involved in the polyphenol pathway and its regulation. Focusing on a limited number of genes, some other possible functions remain to be investigated, like additional transcription factors, small regulatory RNA (miRNA) and other genes which could affect the catalytic activity of enzymes or phenolic compounds transport or stability. However, this study contributed to highlight a large number of candidate genes for most of the major QTLs. It also highlights the complexity of the biosynthesis of these compounds by showing the absence of major expected gene of the biosynthesis under major QTLs like the ANR on LG16 and the HCT/HQT on the LG17. Following a fine mapping approach of the QTL of interest, a functional validation can now be undertaken, using sequencing methods, QRT-PCR, and transgen- esis. QTL detection based on the level of expression of these genes (eQTL) would also permit further in depth understanding of the phenolic compounds biosynthesis.
zulhermana.sembiring@SampoernaAgro.com (Z.S.); dwi.asmono@SampoernaAgro.com (D.A.)
* Correspondence: firstname.lastname@example.org
Received: 19 June 2019; Accepted: 14 September 2019; Published: 26 September 2019
Abstract: Oil palm production is gaining importance in Central and South America. However, the main species Elaeis guineensis (Eg) is suffering severely from bud rod disease, restricting the potential cultivation areas. Therefore, breeding companies have started to work with interspecific Elaeis oleifera × Eg (Eo × Eg) hybrids which are tolerant to this disease. We performed association studies between candidategene (CG) single nucleotide polymorphisms (SNP) and six production and 19 oil quality traits in 198 accessions of interspecific oil palm hybrids from five different origins. For this purpose, barcoded amplicons of initially 167 CG were produced from each genotype and sequenced with Ion Torrent. After sequence cleaning 115 SNP remained targeting 62 CG. The influence of the origins on the different traits was analyzed and a genetic diversity study was performed. Two generalized linear models (GLM) with principle component analysis (PCA) or structure (Q) matrixes as covariates and two mixed linear models (MLM) which included in addition a Kinship (K) matrix were applied for association mapping using GAPIT. False discovery rate (FDR) multiple testing corrections were applied in order to avoid Type I errors. However, with FDR adjusted p values no significant associations between SNP and traits were detected. If using unadjusted p values below 0.05, seven of the studied CG showed potential associations with production traits, while 23 CG may influence different quality traits. Under these conditions the current approach and the detected candidate genes could be exploited for selecting genotypes with superior CG alleles in Marker Assisted Selection systems.
2/6 10/03/10 One of the limitations of interdisciplinarity is the misunderstanding regarding specific words and concepts from one discipline to the other (1). One way to circumvent this aspect is to incorporate the vocabulary of other disciplines, when it corresponds to the appropriate concept. Gene environment interaction is a popular topic, for which there has been to date,more reviews than established findings. There has been numerous attempts to represent what types of interactions could occur (2). Geneticists have proposed the term candidate genes to infer there was a specific hypothesis, usually regarding the function of the gene, justifying its study for a given disease, whereas genomewide comparisons have been called "agnostic" (3), which etymologically means without knowledge. In that context, testing a candidategene-environment interaction is to test a hypothesis, based on knowledge (4).
Key Words: Linkage Maps, Segregation Families, QTL
1412 PIT-1, a candidategene for mass assisted selection in dairy bulls. I. Parmentier* 1 , N. Gengler 2 , S. Fontaine 1 , B. Auvray 2 , T. Burnside 3 , D. Portetelle 1 , and R. Renaville 1 , 1 Gembloux Agricultural University, Animal and microbial biology unit,
associations with CD (omnibus test p-value=0.035). Two specific haplotypes (GAGTTCGTAA, p=0.05; GGCCTCGTCG, p=0.001) showed evidence for association with CD. No parent-of-origin effects were observed. The second phase of the study retested the three CYP4F2 SNPs that showed association in the first stage and was based on 223 CD cases and 330 controls. Some indications of association with one SNP i.e. rs3093158 were present (genotypic uncorrected 1- sided p-value=0.03); however this genotype association did not withstand correction. Combining cases from the two phases of the study revealed significant interactions between the CYP4F2, ALOX and NOD2 genes. No gene-gender interactions were obvious nor were the study genes associated with specific clinical phenotypes of CD. Conclusions - Our study suggests that the CYP4F2, a key member of the LTB 4 metabolic pathway is a potential candidategene for CD. Furthermore there was evidence that interactions between adaptive immunity genes (CYP4F2 and ALOX5) and innate immunity genes (NOD2) genes modify risk for CD in children. Further studies on larger cohorts are required to confirm these findings.
Description and nucleotide diversity of the candidategene VvDXS
VvDXS gene structure consists of ten exons and nine introns spread for a total of 4790 bp corresponding to the gene prediction LOC 100249323 on V. vinifera PN40024. A coding region of 2151 bp is predicted to encode for a DXS protein of 716 amino acids. The over- all level of sequence polymorphism of VvDXS in grape- vine is high and the overall SNP frequency is higher than the average frequency of polymorphisms (1 every 64 bp) described by Lijavetsky et al.  for 230 gene fragments in 10 grape genotypes. On the other hand, the overall SNP frequency observed here is slightly lower than the frequency described by Le Cunff et al.  (1 every 49 bp) for three genes in the G-92 core collection. Moreover, the ratio of synonymous to non- synonymous changes in VvDXS (1.5:1) is higher than the 1:1 reported by Ljiavetsky et al. . Forty percent of the missense mutations were predicted to affect pro- tein function, which is again higher than the 16% observed by Lijavetsky et al. . When considering the subsets of muscat, neutral and aromatic accessions sepa- rately, the polymorphic site frequency and the mean nucleotide variability were higher in the neutral group than in the muscat and aromatic groups. This is not surprising, as 45 out of 48 neutral genotypes belong to the G-48 core collection, which was designed to repre- sent a huge percentage of the genetic variability in a grapevine collection , while muscat types share com- mon ancestry to a certain extend.
There were a total of 342 intact genes and 164 pseudogenes (Table S2) distributed among 49 clusters (Table S3) on 15 different chromosomes, and spanning a total of nearly 48 Mb.
Search of microsatellite loci and design of PCR primers
We used an automated pipeline (adapted from the msfinder Perl pipeline originally developed by Dr. Till Bayer) to find in the reference mouse genome all microsatellite loci with characteristics suitable for this study (purity of the array of repeats, number of repeats). We then defined PCR primers flanking these arrays and predicted to generate unique PCR products based on the reference mouse genome sequence. Whenever possible, primers lying outside repeated elements were chosen but otherwise, one of the two primers was allowed to overlap with such an element. From the resulting list, we selected 1,248 loci for the experiment. By choosing such a scale we were able to include all possible loci lying between the first and last gene of each of the clusters we defined, as well as some flanking loci. The average distance to the nearest candidategene was 20 kb and the average distance between adjacent microsatellites was 33 kb.
Although a number of strategies can be envisaged to identify candidategene markers, essentially two approaches are presently developed to estimate the favorable QTL implicated in a specific production trait. The first method, the positional cloning, consists of the localization of the genes of interest using marker–QTL associations covering the whole genome. This approach can be divided in the following steps: 1) identification of chromo- somal regions containing QTL of interest (10 –20 cM), 2) specification of QTL location within these regions (5 cM), 3) identification of markers in tight linkage to the QTL (1–2 cM), and 4) identification of potential candidate genes in this region. The positional genetics approach is based on mapping QTL to progressively narrower chromosomal regions, using a battery of microsatellite markers, until suitable candidate genes are identified. Based on the maximum likelihood multilocus linkage analysis that accounts for variance heterogeneity of the phenotypes, different authors reported the presence of QTL affecting milk yield and composition on numerous chromosome regions including chromosomes 1, 2, 5, 6, 9, 10, 14, 16, 18, 20, and 23 (P ⬍ 0.05 to P ⬍ 0.00001) [4–6]. On the other hand, markers with effects on somatic cell score were located on chromosomes 5, 22, and 23 (P ⬍ 0.005) . In this strategy, the next step of research is the chromosomal microdissection of target-specific areas of the involved chromosome and then identification of the actual underlying genes. However, the informativeness of the family material may likely become a limiting factor for finemap- ping efforts .
Despite these new insights, the results presented here have some limitations. First of all, it is difficult to draw general conclusions based on an isolated case of MDE patient. Replications are warranted but finding similar cases would require a very large prospective cohort within healthy subjects or individuals at risk for an MDE. More- over, the inherent physiological or stochastic variations in gene expression could contribute to the observed varia- tions and little is known about variation of gene expres- sion of candidate genes such as SLC6A4/5HTT . Finally, we favored a candidategene approach and re- stricted our analysis to a few genes of interest. Of note, other candidate biomarker genes such as FKBP5 could also be informative in such a case and deserve further in- vestigations [30,31].
Figure. QTL meta-analysis for phenolic compounds quantified in fruit and juice prepared for two harvest years and candidategene related to the biosynthethic pathway. Meta-QTLs are represented as full bars on the right side of the LG. Details on QTL included in meta-QTL analysis
Hanoi, Vietnam email@example.com
Abstract—Candidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.