Microsatellite DNA markers for rice chromosomes

We found 369 complete microsatellites, of which (CGG/GCC)_n was the most frequent, in 11 798 rice sequences in the database. Of these microsatellites, 35 out of 45 could be successfully converted into microsatellite DNA markers using sequence information in their flanking regions. Thus, the time and labor used to develop new microsatellite DNA markers could be saved by using these published sequences. Twenty eight polymorphic markers between Asominori (japonica) and IR24 (indica) have been correctly mapped on the rice genome and microsatellites appear to be randomly distributed in the rice chromosomes. Integration of these markers with the published microsatellite DNA markers showed that about 35% of the rice chromosomes were covered by the 56 microsatellite DNA markers. These microsatellites were hypervariable and were easily to assay by PCR; they were distributed to all chromosomes and therefore, one can easily select plants carrying desired chromosome regions using these microsatellite DNA markers. Thus, microsatellite maps should aid the development of new breeds of rice saving time, labor, and money.     

Blood genetic systems in four Amazonian tribes.

Data on 31 genetic systems were obtained for 421 individuals belonging to the Arara, Araweté, Mundurucu, and Jamamadi tribes of northern Brazil. The Jamamadi depart farthest, and the Mundurucu least, from South American Indian averages. These data are analyzed together with those of 24 other Amazonian groups. Genetic distances and corresponding dendrograms indicate a cluster of 14 related tribes living north of the Amazon river. These genetic results show only a modest correlation with linguistic and geographic relationships among these groups.     

Effect of Peroxisome Proliferator-Activated Receptor-γ Coactivator-1 Alpha Variants on Spontaneous Clearance and Fibrosis Progression during Hepatitis C Virus Infection in Moroccan Patients

Virologica Sinica - Hepatitis C virus (HCV) is still one of the main causes of liver disease worldwide. Metabolic disorders, including non-alcoholic fatty liver disease (NAFLD), induced by HCV have...     

Characterization of Endogenous Human FcγRIII by Mass Spectrometry Reveals Site,Allele and Sequence Specific Glycosylation*

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Highlights

• •Glycosylation of endogenous FcγRIII from neutrophils and matched plasma from more than 40 donors characterized at two sites involved in IgG binding.
• •Glycosylation of soluble FcγRIII glycosylation at N45 can be used to assign FcγRIIIb alleles.
• •FcγRIIIb allele specific differences in glycosylation at N162 may influence differential activity observed for primary cells.

     

Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function

Over 520 different amino acid substitution variants have been previously identified in the systematic screening of 91 human DNA repair genes for sequence variation. Two algorithms were employed to predict the impact of these amino acid substitutions on protein activity. Sorting Intolerant from Tolerant (SIFT) classified 226 of 508 variants (44%) as "Intolerant." Polymorphism Phenotyping (PolyPhen) classed 165 of 489 amino acid substitutions (34%) as "Probably or possibly damaging." Another 9-15% of the variants were classed as "Potentially intolerant or damaging." The results from the two algorithms are highly associated, with concordance in predicted impact observed for approximately 62% of the variants. Twenty-one to thirty-one percent of the variant proteins are predicted to exhibit reduced activity by both algorithms. These variants occur at slightly lower individual allele frequency than do the variants classified as "Tolerant" or "Benign." Both algorithms correctly predicted the impact of 26 functionally characterized amino acid substitutions in the APE1 protein on biochemical activity, with one exception. It is concluded that a substantial fraction of the missense variants observed in the general human population are functionally relevant. These variants are expected to be the molecular genetic and biochemical basis for the associations of reduced DNA repair capacity phenotypes with elevated cancer risk.     

Mapping genes for resistance to<Emphasis Type="Italic"> Verticillium albo-atrum</Emphasis> in tetraploid and diploid potato populations using haplotype association tests and genetic linkage analysis

Verticillium wilt disease of potato is caused predominantly by Verticillium albo-atrum and V. dahliae. StVe1 —a putative QTL for resistance against V. dahliae —was previously mapped to potato chromosome 9. To develop allele-specific, SNP-based markers within the locus, the StVe1 fragment from a set of 30 North American potato cultivars was analyzed. Three distinct and highly diverse haplotypes can be distinguished at the StVe1 locus. These were detected in 97%, 33%, and 10% of the cultivars analyzed. We tested for haplotype association and for genetic linkage between the StVe1 haplotypes and resistance of tetraploid potato to V. albo-atrum. Moreover, field resistance was assessed in diploid populations with known molecular linkage maps in order to identify novel QTLs. Resistance QTLs against V. albo-atrum were detected on four chromosomes (2, 6, 9, and 12) at the diploid level, with one QTL on chromosome 2 contributing over 40% to the total phenotypic variation of the trait. At the tetraploid level, a significant association between the StVe1-839-C haplotype and susceptibility to the disease was detected, suggesting that resistance-related genes directed against V. albo-atrum and V. dahliae are located in the same genomic region of chromosome 9. However, on the basis of the present analysis, we cannot determine whether these genes are closely linked or if a single gene provides resistance against both Verticillium species. To assess the usefulness of the StVe1-839-C haplotype for marker-assisted selection, we subjected the resistance data to Bayesian analysis, and calculated positive (0.65) and negative (0.75) predictive values, and overall predictive accuracy (0.72). Our results indicate that tagging of additional genes for resistance to Verticillium with molecular markers will be required for efficient marker-assisted selection.Communicated by M.-A. Grandbastien     

Interactions between meat intake and genetic variation in relation to colorectal cancer

Meat intake is associated with the risk of colorectal cancer. The objective of this systematic review was to evaluate interactions between meat intake and genetic variation in order to identify biological pathways involved in meat carcinogenesis. We performed a literature search of PubMed and Embase using “interaction”, “meat”, “polymorphisms”, and “colorectal cancer”, and data on meat–gene interactions were extracted. The studies were divided according to whether information on meat intake was collected prospectively or retrospectively. In prospective studies, interactions between meat intake and polymorphisms in PTGS2 (encoding COX-2), ABCB1, IL10, NFKB1, MSH3, XPC (P_int = 0.006, 0.01, 0.04, 0.03, 0.002, 0.01, respectively), but not IL1B, HMOX1, ABCC2, ABCG2, NR1I2 (encoding PXR), NR1H2 (encoding LXR), NAT1, NAT2, MSH6, or MLH1 in relation to CRC were found. Interaction between a polymorphism in XPC and meat was found in one prospective and one case–control study; however, the directions of the risk estimates were opposite. Thus, none of the findings were replicated. The results from this systematic review suggest that genetic variation in the inflammatory response and DNA repair pathway is involved in meat-related colorectal carcinogenesis, whereas no support for the involvement of heme and iron from meat or cooking mutagens was found. Further studies assessing interactions between meat intake and genetic variation in relation to CRC in large well-characterised prospective cohorts with relevant meat exposure are warranted.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-014-0448-9) contains supplementary material, which is available to authorized users.     

Population genetic models can be used to study the evolution of the interacting behaviours of parents and their progeny

An example of the evolution of the interacting behaviours of parents and progeny is studied using iterative equations linking the frequencies of the gametes produced by the progeny to the frequencies of the gametes in the parental generation. This population genetics approach shows that a model in which both behaviours are determined by a single locus can lead to a stable equilibrium in which the two behaviours continue to segregate. A model in which the behaviours are determined by genes at two separate loci leads eventually to fixation of the alleles at both loci but this can take many generations of selection. Models of the type described in this paper will be needed to understand the evolution of complex behaviour when genomic or experimental information is available about the genetic determinants of behaviour and the selective values of different genomes.     

A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.