Number Of Genetic Markers Research Articles

Ancient DNA in archaeological wheat grains: preservation conditions and the study of pre-Hispanic agriculture on the island of Gran Canaria (Spain)

The recovery of ancient DNA from archaeological wheat samples under different preservation conditions was assessed using a number of genetic markers. It was possible to amplify nuclear DNA from desiccated grains but not from charred. The desiccated grain was from a pre-Hispanic grain silo in Gran Canaria and showed excellent DNA preservation, enabling the amplification of the ribosomal DNA markers IGS and ITS, the upstream region of the HMW-glutenin locus and single-locus nuclear microsatellites. Our results demonstrated the presence of both durum and bread wheat in an assemblage of naked grain. We were also able to identify different genotypes in durum wheat and compare these with extant landraces, providing insights into the agrarian practices of the ancient Canarians and the origin of their crops.

Genetic diversity and population structure of the Korean field mouse(Apodumus peninsulae) in South Korea: from 17 previously and newly developed microsatellite markers

To provide more reliable genetic information on species and minimize experimental errors, biologists increase the number of genetic markers available and then carefully select optimal markers from a large candidate pool. We developed nine novel microsatellite markers from the Korean field mouse (Apodemus peninsulae), which is one of the most dominant forest animals in South Korea. The mean observed and expected heterozygosities across nine markers were 0.65 and 0.73, respectively, with an average polymorphic information content of 0.70. Using 17 microsatellite markers (nine polymorphic markers in this study, in combination with eight previously reported for the species), we conducted genetic analysis on the animals from six sampling locations. These locations are divided into the eastern (EAST) and the western (WEST) sides of the Taebaek mountain ranges in South Korea. Genetic diversity was high at both groups, with the mean expected heterozygosity of 0.77 in EAST and 0.78 in WEST. However, we did not observe strong evidence of genetic divergence between two groups. Future genetic research with more samples incorporating ecological study may clarify population structure in the species and the hypothesis of the mountains discontinuity of gene flow.

Species delimitation in fungal endophyte diversity studies and its implications in ecological and biogeographic inferences

The estimation of species diversity in fungal endophyte communities is based either on species counts or on the assignment of operational taxonomic units (OTUs). Consequently, the application of different species recognition criteria affects not only diversity estimates but also the ecological hypotheses that arise from those observations. The main objective of the study was to examine how the choice and number of genetic markers and species delimitation criteria influence biodiversity estimates. Here, we compare approaches to defining species boundaries in three dominant species complexes of tropical endophytes, specially Colletotrichum gloeosporioides agg., Pestalotiopsis microspora agg. and Trichoderma harzianum agg., from two Amazonian trees: Hevea brasiliensis and H. guianensis. Molecular tools were used to describe and compare the diversity of the different assemblages. Multilocus phylogenetic analyses [gpd, internal transcribed spacer (ITS) and tef1] and modern techniques for phylogenetic species delimitation were overlaid with ecological data to recognize putative species or OTUs. The results demonstrate that ITS alone generally underestimates the number of species predicted by other nuclear loci. These results question the use of ITS and arbitrary divergence thresholds for species delimitation.

Male Reproductive Traits and Their Relationship to Reproductive Traits in Their Female Progeny: A Systematic Review

The overall objective of one of the major research programs in the Co-operative Research Centre (CRC) for Beef Genetic Technologies is to 'Improve female reproductive performance' in tropical, northern Australian beef cattle herds. To address this overall objective, a quantitative genetics project focused on investigation of male reproductive traits was designed and linked to three female reproduction-focussed projects, (i) discovery of genes associated with post-partum re-conception and age at puberty; (ii) expression of genes associated with post-partum re-conception; and (iii) early predictors of lifetime female reproductive performance. During the initial planning of this male reproductive traits project, the CRC Scientific Review Committee recommended that the research team investigate and evaluate potentially new, early-life (i.e able to be measured before 2 years of age) predictors of both male and female reproductive performance. To address this recommendation, the following was carried out: (i) criteria for selection of traditional and candidate traits were established; (ii) methodology for tabulation of potential traits/phenotypes that define male and female reproductive function was developed; and (iii) a systematic scientific review of early-life predictors of male and female fertility was prepared. This review concluded that although factors that might be useful in predicting male reproductive performance have been studied for many years, there was relatively little useful information available to meet the objectives of this review. It was also concluded that the direction of future research should be guided not only by previous research which was scarce, but also by speculative hypotheses arising from an understanding of the physiological, endocrinological and genetic processes active in reproduction. A small number of new traits were recommended in addition to traditional sperm morphology, sexual behaviour, anatomical structure and growth traits. Potential additional traits include measurement of gonadotrophin-releasing hormone-stimulated luteinizing hormone (GnRH-stimulated LH); inhibin; several seminal plasma proteins (osteopontin, spermadhesin and seminal plasma proteins BSP30 and phospholipase A(2) could be used in an index); 11β-hydroxysteriod dehydrogenase; and leptin. In addition, the potential also exists to screen animals for a number of genetic markers associated with age of puberty, follicular recruitment and ovulation rate and genes associated with bovine seminal plasma protein and testosterone production. Insulin-like growth factor-1 (IGF-1) measurements are included because of their association with growth parameters, and an additional analysis demonstrated associations with male and female reproductive traits. Some of these factors have been previously evaluated in small numbers of animals of various species under intensive management conditions. Therefore, there is a need to evaluate these factors in much larger numbers of beef cattle grazing semi-extensive tropical production systems in northern Australia to determine their value in improving beef cattle enterprise profitability through improved herd fertility.

Genetic markers of antihypertensive drug efficacy

Objective. To assess the antihypertensive efficacy of monotherapy with Nifedipine Retard, Enalapril and Metoprolol in view of polymorphism of genes associated with lipid metabolism, renin-angiotensin system, β-adrenoreceptor and apoptosis cascade in patients with essential hypertension. Design and methods. 150 Tatar hypertensive patients were included, and blood pressure monitoring and gene polymorphism determination were performed. Antihypertensive therapy was prescribed for one month. Results. There are associations between antihypertensive efficacy and the number of genetic markers in hypertensive patients. We suppose that protein products encoded by GluR2, HIF-1А, CASP9, Ephrin-B3 genes play a role either in blood pressure regulation or in the mechanism of blood pressure decrease under therapy with Nifedipine Retard, Enalapril or Metoprolol. Conclusion. GluR2, HIF-1А, CASP9, Ephrin-B3 genes polymorphisms can be used to predict the efficacy of treatment with Nifedipine Retard, Enalapril and Metoprolol in hypertensive patients.

Genomewide Multiple-Loci Mapping in Experimental Crosses by Iterative Adaptive Penalized Regression

Genomewide multiple-loci mapping can be viewed as a challenging variable selection problem where the major objective is to select genetic markers related to a trait of interest. It is challenging because the number of genetic markers is large (often much larger than the sample size) and there is often strong linkage or linkage disequilibrium between markers. In this article, we developed two methods for genomewide multiple loci mapping: the Bayesian adaptive Lasso and the iterative adaptive Lasso. Compared with eight existing methods, the proposed methods have improved variable selection performance in both simulation and real data studies. The advantages of our methods come from the assignment of adaptive weights to different genetic makers and the iterative updating of these adaptive weights. The iterative adaptive Lasso is also computationally much more efficient than the commonly used marginal regression and stepwise regression methods. Although our methods are motivated by multiple-loci mapping, they are general enough to be applied to other variable selection problems.

Old and new ideas about genes and behaviour

Genetic factors have long been recognized as contributors to variantion in behaviour both within the normal span and as mental diseases. The first attempts to make behaviour the subject of scientific genetic studies used likeness between twins and other relatives to confirm heredity. Later heritability has been used as a quantitative estimate of the genetic part of the variance. Attempts to localize genetic factors became possible when associations between phenotypic aberrations and karyotype were observed. Inborn errors of metabolism further confirmed that specific metabolic deficits could influence behaviour. Many kinds of common mental deficiencies such as senile dementia have a heterogeneous background, and so have normal variations in talents and personality. The mapping of the human genome, the availability of an unlimited number of genetic markers and efficient statistical tools promised ample discoveries of genes behind the variation. The expectations have not been fulfilled and more subtle influences on gene expression have to be assumed. Topics that are taken up include genomic imprinting, brain activation patterns, the importance of neurotransmitter regulation and non-additive interactions between genes and environment.

ESTRUCTURA POBLACIONAL Y DIVERSIDAD GENÉTICA DE REBAÑOS BOVINOS DE CARNE DEL SUR DE CHILE

The genetic variability observed in domesticated animal species is progressively disappearing due to breed replacement and introgressions aimed at improving productivity and quality. These actions have eroded important adaptation traits, such as disease resistance and tolerance to harsh environments, usually found in local breeds. Currently, there is not sufficient information to understand the relationships among bovine breeds locally adapted and those of exotic origin and recently introduced. In the last decades, a number of genetic markers have become available to study the genetic diversity in a number of domestic animal species. In the present work, 10 microsatellite markers were used to study the genetic diversity and population structure of 212 bovine individuals (eight genotypes) distributed in several geographical Chilean zones. The microsatellites were amplified using PCR and the fragments were separated electrophoretically using the sequencer ABI PRISM 310. The results showed a clear evidence of population structure among eight bovine genotypes found in southern Chile. Estimate the genetic variability through the calculation of gene frequency allowed heterozigosity per locus to be determined, which varied between 0.61 SPS115 to 0.85 TGLA227. The average heterozigosity of the population was 0.73. According to the PIC, all loci studied were informative, with values that ranged from 0.58 SPS115 to 0.84 TGLA227, with an average population of 0.70. It was concluded that among the microsatellite markers analyzed there was a high degree of polymorphism; the number of alleles per locus in the population varied between nine for the SPS115 marker and 19 for the TGLA122 and INRA23 markers, respectively. In this way, the genetic analysis carried out revealed a high hybridization between beef breeds with subsequent probability of 0.99 and an average Fst (Index of genetic differentiation) in the population of 0.1371 (13.7%), which confirm the high genetic variation existing in the populations studied.

Impacts of using inbred animals in studies for detection of quantitative trait loci

Effects of utilizing inbred and noninbred family structures in experiments for detection of quantitative trait loci (QTL) were compared in this simulation study. Simulations were based on a general pedigree design originating from 2 unrelated sires. A variance component approach of mapping QTL was applied to simulated data that reflected common family structures from dairy populations. Five different family structures were considered: FS0 without inbreeding, FS1 with an inbred sire from an aunt-nephew mating, FS2 with an inbred sire originating from a half-sib mating, FS3 and FS4 based on FS2 but containing an increased number of offspring of the inbred sire (FS3), and another extremely inbred sire with its final offspring (FS4). Sixty replicates each of the 5 family structures in 2 simulation scenarios each were analyzed to provide a praxis-like situation of QTL analysis. The largest proportion of QTL position estimates within the correct interval of 3 cM, best test statistic profiles and the smallest average bias were obtained from the pedigrees described by FS4 and FS2. The approach does not depend on the kind and number of genetic markers. Inbreeding is not a recommended practice for commercial dairy production because of possible inbreeding depression, but inbred animals and their offspring that already exist could be advantageous for QTL mapping, because of reduced genetic variance in inbred parents.

Genetic characterization of Toxoplasma gondii isolates from China

Toxoplasma gondii infections are prevalent in humans and animals worldwide. In North America and Europe, T. gondii is highly clonal, consisting of three distinct lineages (Types I, II and III), whereas in South America, T. gondii is highly diverse with a few lineages expanded in the population. However, there is limited data on the diversity of T. gondii in Asia. Here we report the genetic characterization of T. gondii isolates from different hosts and geographical locations in China using the multilocus PCR–RFLP. A total of 17 T. gondii isolates from humans (3 strains), sheep (1 strain), pigs (5 strains) and cats (8 strains) were typed at 10 genetic markers including 9 nuclear loci SAG1, SAG2, SAG3, BTUB, GRA6, L358, PK1, c22-8, c29-2 and an apicoplast locus Apico. Four genotypes were revealed, including three previously reported and one new genotype. Three isolates belong to the clonal Type I lineage, one isolate belongs to the clonal Type II lineage, and the rest 13 isolates are grouped into two genotypes. This is the first report of genetic typing of T. gondii isolates from different hosts and geographical locations in China using a number of genetic markers, which has implications for the studies of population genetic structures of T. gondii, as well as for the prevention and control of T. gondii infections in humans and animals in China.

Incorporation of genotype effects into animal model evaluations when only a small fraction of the population has been genotyped

The method of Israel and Weller (Estimation of candidate gene effects in dairy cattle populations. Journal of Dairy Science 1998, 81, 1653–1662) to estimate quantitative trait locus (QTL) effects when only a small fraction of the population was genotyped was investigated by simulation. The QTL effect was underestimated in all cases, but bias was greater for extreme allelic frequencies, and increased with the number of generations included in the simulations. Apparently, as the fraction of animals with inferred genotypes increases, the genotype probabilities tend to ‘mimic’ the effect of relationships. Unbiased estimates of QTL effects were derived by a modified ‘cow model’ without the inclusion of the relationship matrix on simulated data, even though only a small fraction of the population was genotyped. This method yielded empirically unbiased estimates for the effects of the genes DGAT1 and ABCG2 on milk production traits in the Israeli Holstein population. Based on these results, an efficient algorithm for marker-assisted selection in dairy cattle was proposed. Quantitative trait loci effects are estimated and subtracted from the cows’ records. Genetic evaluations are then computed for the adjusted records. Animals are then selected based on the sum of their polygenic genetic evaluations and QTL effects. This scheme differs from a traditional dairy cattle breeding scheme in that all bull calves were considered candidates for selection. At year 10, total genetic gain was 20% greater by the proposed algorithm as compared to the selection based on a standard animal model for a locus with a substitution effect of 0.5 phenotypic standard deviations. The proposed method is easy to apply, and all required software are ‘on the shelf.’ It is only necessary to genotype breeding males, which are a very small fraction of the entire population. The method is flexible with respect to the model used for routine genetic evaluation. Any number of genetic markers can be easily incorporated into the algorithm, and the reduction in genetic gain due to incorrect QTL determination is minimal.

Frequency of mutations in “thrombophilic state” genes in Uzbekistan

The frequency of mutations in a number of genetic markers, specifically factor V gene (G1691A), blood coagulation factor II gene (G20210A), and the methylenetetrahydrofolate reductase (MTHFR) gene (C677T), is studied in ethnic Uzbek patients with deep vein thrombosis of the lower extremities and in healthy donors. It is established that the incidence of mutant alleles among patients in Uzbekistan for FV Leiden is 12.9%; for prothrombin, 4%; and for MTHFR, 47.8%. The mutant allele C677T of the MTHFR gene has the highest expressivity in the appearance of MTHFR (47.8%). It is noted that this mutation in the MTHFR gene is encountered significantly more frequently in females with deep vein thrombosis than in males with deep vein thrombosis. The G20210A mutation in the prothrombin gene is encountered more rarely in the Uzbek population. The penetrance is studied and the role of these mutations in the appearance of deep vein thrombosis is estimated.

Discussion and summary

Discussion and summary

Sequentially testing for a gene–drug interaction in a genomewide analysis

Assaying a large number of genetic markers from patients in clinical trials is now possible in order to tailor drugs with respect to efficacy. The statistical methodology for analysing such massive data sets is challenging. The most popular type of statistical analysis is to use a univariate test for each genetic marker, once all the data from a clinical study have been collected. This paper presents a sequential method for conducting an omnibus test for detecting gene-drug interactions across the genome, thus allowing informed decisions at the earliest opportunity and overcoming the multiple testing problems from conducting many univariate tests. We first propose an omnibus test for a fixed sample size. This test is based on combining F-statistics that test for an interaction between treatment and the individual single nucleotide polymorphism (SNP). As SNPs tend to be correlated, we use permutations to calculate a global p-value. We extend our omnibus test to the sequential case. In order to control the type I error rate, we propose a sequential method that uses permutations to obtain the stopping boundaries. The results of a simulation study show that the sequential permutation method is more powerful than alternative sequential methods that control the type I error rate, such as the inverse-normal method. The proposed method is flexible as we do not need to assume a mode of inheritance and can also adjust for confounding factors. An application to real clinical data illustrates that the method is computationally feasible for a large number of SNPs.

D2 dopamine receptor gene haplotypes and their influence on alcohol and tobacco consumption magnitude in alcohol-dependent individuals

Alcohol dependence and habitual smoking frequently co-occur and possibly mutually influence each other. Both have been related to alterations of dopaminergic neurotransmission. The aim of this analysis of the Munich Gene Data Bank for Alcoholism(MGBA) was to re-evaluate the potential relation between D2 receptor and dopamine transporter gene haplotypes and quantity-related phenotypes of alcohol consumption (average daily alcohol intake before admission for treatment) and smoking (average units smoked per day). A total of 333 inpatients (265 males) were enrolled in the study, all of who met the ICD10 diagnosis of alcohol dependence. Mild and strong quantity drinkers and smokers were separated into groups by median split. A number of genetic markers were chosen across D2 dopamine receptor gene (-141 Ins/Del, Taq1B, Taq1D, Ser311Cys; rs1079594 (intron 7); Taq1A) and dopamine transporter (40bp variable number of tandem repeat; rs2617605 (intron 2); rs37022 (intron 7); rs40184 (intron 14)). Genotyping was performed using PCR. Strong drinkers reported significantly higher amounts of smoking and vice versa. While no association was detected for dopamine transporter genetic variants, a number of D2 receptor gene single nucleotide polymorphisms were related to both smoking- and drinking-related behaviours. Subsequent analysis of D2 receptor gene haplotypes revealed that two common haplotypes had a significant association with quantitative phenotypes of regular drinking (Ins-C-G-C-A1) and smoking (Ins-T-G-A-A2). The finding of an association between common D2 dopamine receptor gene haplotypes with the quantity of drinking and smoking corroborates with results from previous studies suggesting a relationship between the dopamine system and alcohol and substance use disorders. Furthermore, it makes D2 dopamine receptor a candidate gene significantly influencing both alcohol and nicotine dependence.

DESIGN AND ANALYSIS OF AN EFFICIENT RECURSIVE LINKING ALGORITHM FOR CONSTRUCTING LIKELIHOOD BASED GENETIC MAPS FOR A LARGE NUMBER OF MARKERS

A multi-locus likelihood of a genetic map is computed based on a mathematical model of chromatid exchange in meiosis that accounts for any type of bivalent configuration in a genetic interval in any specified order of genetic markers. The computational problem is to calculate the likelihood (L) and maximize L by choosing an ordering of genetic markers on the map and the recombination distances between markers. This maximum likelihood estimate (MLE) could be found either with a straightforward algorithm or with the proposed recursive linking algorithm that implements the likelihood computation process involving an iterative procedure is called Expectation Maximization (EM). The time complexity of the straightforward algorithm is exponential without bound in the number of genetic markers, and implementation of the model with a straightforward algorithm for more than seven genetic markers is not feasible, thus motivating the critical importance of the proposed recursive linking algorithm. The recursive linking algorithm decomposes the pool of genetic markers into segments and renders the model implementable for hundreds of genetic markers. The recursive algorithm is shown to reduce the order of time complexity from exponential to linear in the number of markers. The improvement in time complexity is shown theoretically by a worst-case analysis of the algorithm and supported by run time results using data on linkage group-II of the fungal genome Neurospora crassa.

Advances in statistical human genetics over the last 25 years

The past 25 years has seen an explosion in the number of genetic markers that can be measured on DNA samples at an ever decreasing cost. Although basic statistical methods for analysing such data gathered on samples of either independent individuals or family members, one or two markers at a time, were already well developed before this explosion occurred, there has been a corresponding burst in activity to develop multiple marker models to find disease-causing gene variants, capitalizing on the data that have become available, to increase the power of such methods. This has required the concomitant development of faster algorithms to speed up the computation of various likelihoods. For linkage analysis, to obtain the approximate locations for genes of interest, Mendelian segregation models have been extended to be more realistic and statistical models that do not assume specific modes of inheritance have been extended to allow for the analysis of larger pedigree structures. For association analysis, to obtain more precise locations for genes of interest, the recent completion of the first stage of the HapMap project has spurred the development, still underway, of novel experimental designs and analytical methods to combat the curse of dimensionality and the resulting multiple testing problem. Perhaps the greatest current challenge concerns how best to gather and synthesize the many lines of evidence possible in order to discover the genetic determinants underlying complex diseases.

DNA Registers of Legally Obtained Wildlife and Derived Products as Means to Identify Illegal Takes

The exploitation and sale of wildlife species that are endangered in only part of their range present regulators with the critical challenge of separating legal from illegal takes. Wildlife DNA registers created from tissue samples of legally obtained individual wildlife specimens can address this problem by allowing managers to identify unregistered (presumably illegally obtained) specimens. We tested the effectiveness of the only current, fully operational wildlife DNA register of individual genetic profiles collected from legally caught minke whales (Balaenoptera acutorostrata). Twenty minke whale tissue samples collected at markets in Norway and 2 additional samples collected from beached minke whales in Denmark were genotyped at 12 loci used by the Norwegian minke whale DNA register Genetic profiles of these samples then were compared against the 2676 individual profiles deposited in the Norwegian register The high number of genetic markers used to identify individuals in our study allowed consistent matching of sample and reference profiles despite an overall error rate (due to experimental and interlaboratory data standardization) estimated at 0.015 per locus. Of the 22 test samples only the 2 Danish samples failed to match an existing profile in the Norwegian minke whale DNA register Our results show that the basic principle of wildlife DNA registers can work in a real-life situation. The strength of wildlife DNA registers lies in their ability to unambiguously identify unregistered specimens with the aid of sensitive genetic methods that enable analysis of highly processed or degraded tissue samples. Our study also highlights a number of methodological problems such as laboratory errors and interlaboratory data standardization, which need be addressed to ensure a successful implementation of wildlife DNA registers.

Prediction of the risk of type 1 diabetes from polymorphisms in candidate genes

Type 1 diabetes (T1D) is a T-cell-mediated autoimmune disease, in which pancreatic β cells are selectively destroyed. Innate immune response is also important in the development of T1D. Several studies have demonstrated that HLA class II alleles, DQ and DR influence T1D susceptibility. Specific class I and II alleles are non-randomly associated with each other on an extended haplotypes, the typing of which provide the best risk determinants of T1D. Studying the haplotype in different ethnic populations will enable us to identify the exact polymorphisms that can trigger T1D and to develop biological tools for protection. With the advent of numerous candidate markers, additional loci that influence susceptibility to T1D have been reported. Although different studies have suggested that various genetic variants increase the risk, large-scale association studies that examine many polymorphisms simultaneously are required to allow reliable prediction of the genetic risk. Not only the numbers of genetic markers we are applying, but also the accurate phenotyping is critical in the success of setting-up a best-fitting model for prediction. For a better phenotyping, multiple autoantibodies to islet cell antigens, which may arise even before the clinical onset have been known as the best surrogate markers of T1D. However, differences in genotypes between antibody-positive individuals and T1D patients may indicate genetic factors, which determine progression to clinical disease. The ability to detect those antibody-positive individuals who will progress to T1D is central to prevention program.

Regression models for allele sharing: analysis of accumulating data in affected sib pair studies.

Advances in human genome mapping have led to the identification of large numbers of genetic markers that allow systematic searches for multiple disease susceptibility genes for complex traits. A common design involves the recruitment of families with at least two children affected with the disease of interest. The objective is to find chromosomal regions that harbour susceptibility genes for the disease. The affected children, their parents if available, and sometimes other, unaffected, siblings are genotyped using sets of microsatellite DNA markers representing chromosomal sites distributed across the genome. Each marker can occur in several different variants known as alleles, and a pair of alleles constitutes the marker genotype. Each child randomly inherits one of their mother's two alleles and one of their father's two alleles. If a marker is close to a disease susceptibility gene, then affected siblings are expected to have more sharing of the same maternal and/or paternal marker alleles. Statistical methods are used to estimate the distribution of allele sharing in each affected sib pair (ASP) using the set of markers typed across each chromosome, and to test for the presence of excess sharing in the families as a group at each point across the genome. Regression models that allow the allele sharing proportions to depend on characteristics of the family such as diagnostic subtype or ethnic background have been developed to address the heterogeneity that is characteristic of complex disease, but these have not yet been widely applied. In this paper, we apply regression modelling to investigate variation associated with family-level covariates and with the order in which families are recruited and genotyped. We also discuss how some of the concepts of group sequential analysis apply to accumulating data from genome scans of complex disease.