Statistical Analysis Of Genetic Data Research Articles

Biogeographic origin and genetic characteristics of the peopling of Jeju Island based on lineage markers.

Jeju Island is the largest island of South Korea, located southwest far from the mainland of Korea, and has a unique history and its own cultures that are distinguished from those of the other regions of the Korean mainland. However, the Jeju population has not been deeply investigated to date to understand their genetic structure, which may reflect their historical and geographical background. To identify the genetic characteristics and biogeographic origin of people of Jeju Island based on the statistical analysis of genetic data using lineage markers. 17 Y-STRs data for 615 unrelated males and mitochondrial DNA haplogroup data for 799 unrelated individuals residing on Jeju Island were generated, and analyzed to investigate genetic diversity and genetic characteristics using statistical methods including pairwise Fst or Rst, Analysis of molecular variance (AMOVA) and Multidimensional scaling (MDS). For male individuals of Jeju Island, unique genetic characteristics were observed in the analysis of Y-STRs, including low haplotype diversity, strong association with surnames, genetic difference from other regions of Korea, and common genetic variation of the Y-STR loci known to be predominant in Northern populations, such as Mongolians. Statistical analysis of the mitochondrial DNA haplogroups also revealed similar results that showed low haplogroup diversity and high frequency of haplogroup Y prevalent mostly in ethnic populations around the Sea of Okhotsk in Northeastern Asia. All these results suggest that Jeju Island is genetically distinct from other regions of Korea, possibly being a subpopulation in Korea, and related closely to Northern Asian populations. The findings in the genetic approach could support understanding of the historical background of Jeju Island that is consistent with evidence from other multidisciplinary studies.

Statistical Analysis of Genetic Data for the Balkans plus Historical Evidence

Initial statistical analysis of genetic data on the Balkan nations showed the extent of their genetic links to each other. A careful review of the data highlighted an interesting feature: relatively large genetic differences between Greek regions. This is clearly expressed in the cases of Northern Greece, whose population is genetically much closer to the population of Bulgaria than to the population of Central and Southern Greece, whose population is genetically closer to the Albanian population than to the population of Northern Greece. These conclusions are based on numerical values from the results of statistical analysis of genetic data from EUPEDIA. The article also presents several historical testimonies that offer an explanation of the established regional genetic features of the population of today's Greece.

Single Nucleotide Polymorphisms Of Transforming Growth Factor-Β1 Gene As Potential Asthma Susceptible Variants In Punjabi Population Of Pakistan.

Candidate gene approach based on case-control model is a valuable strategy to determined disease related genetic variants. Two single nucleotide polymorphisms rs1800469 and rs2241715 in TGF β1gene have been reported to affect the asthmatic status in different populations. The main focus of this research was to find any relationship between these SNPs and asthma in Pakistani population. Using case-control model, a total of 108 individuals including 52 asthma patients and 56 healthy controls were screened to find asthma susceptibility of variants rs1800469 and rs2241715. These SNPs were genotyped using SNaPshot minisequencing assay followed by capillary electrophoresis using ABI 3130xl genetic analyzer platform. The statistical analysis of genetic data was performed by using SPSS 21, SHEsis online platform and SNPStats online web software. No association with asthma was seen in allelic model for both SNPs but genotypes analyzed under codominant, dominant, over dominant and recessive models of inheritance revealed that SNP rs2241715 is strongly associated with asthma under genotypic model. rs2241715 was found to be a genetic risk factor for asthma in Pakistani population.

Molecular Genetic Analysis of Stomach Contents Reveals Wild Atlantic Cod Feeding on Piscine Reovirus (PRV) Infected Atlantic Salmon Originating from a Commercial Fish Farm

In March 2012, fishermen operating in a fjord in Northern Norway reported catching Atlantic cod, a native fish forming an economically important marine fishery in this region, with unusual prey in their stomachs. It was speculated that these could be Atlantic salmon, which is not typical prey for cod at this time of the year in the coastal zone. These observations were therefore reported to the Norwegian Directorate of Fisheries as a suspected interaction between a local fish farm and this commercial fishery. Statistical analyses of genetic data from 17 microsatellite markers genotyped on 36 partially-degraded prey, samples of salmon from a local fish farm, and samples from the nearest wild population permitted the following conclusions: 1. The prey were Atlantic salmon, 2. These salmon did not originate from the local wild population, and 3. The local farm was the most probable source of these prey. Additional tests demonstrated that 21 of the 36 prey were infected with piscine reovirus. While the potential link between piscine reovirus and the disease heart and skeletal muscle inflammation is still under scientific debate, this disease had caused mortality of large numbers of salmon in the farm in the month prior to the fishermen's observations. These analyses provide new insights into interactions between domesticated and wild fish.

Utilizing Graph Theory to Select the Largest Set of Unrelated Individuals for Genetic Analysis

Many statistical analyses of genetic data rely on the assumption of independence among samples. Consequently, relatedness is either modeled in the analysis or samples are removed to "clean" the data of any pairwise relatedness above a tolerated threshold. Current methods do not maximize the number of unrelated individuals retained for further analysis, and this is a needless loss of resources. We report a novel application of graph theory that identifies the maximum set of unrelated samples in any dataset given a user-defined threshold of relatedness as well as all networks of related samples. We have implemented this method into an open source program called Pedigree Reconstruction and Identification of a Maximum Unrelated Set, PRIMUS. We show that PRIMUS outperforms the three existing methods, allowing researchers to retain up to 50% more unrelated samples. A unique strength of PRIMUS is its ability to weight the maximum clique selection using additional criteria (e.g. affected status and data missingness). PRIMUS is a permanent solution to identifying the maximum number of unrelated samples for a genetic analysis.

Guidelines for collecting and maintaining archives for genetic monitoring

Rapid advances in molecular genetic techniques and the statistical analysis of genetic data have revolutionized the way that populations of animals, plants and microorganisms can be monitored. Genetic monitoring is the practice of using molecular genetic markers to track changes in the abundance, diversity or distribution of populations, species or ecosystems over time, and to follow adaptive and non-adaptive genetic responses to changing external conditions. In recent years, genetic monitoring has become a valuable tool in conservation management of biological diversity and ecological analysis, helping to illuminate and define cryptic and poorly understood species and populations. Many of the detected biodiversity declines, changes in distribution and hybridization events have helped to drive changes in policy and management. Because a time series of samples is necessary to detect trends of change in genetic diversity and species composition, archiving is a critical component of genetic monitoring. Here we discuss the collection, development, maintenance, and use of archives for genetic monitoring. This includes an overview of the genetic markers that facilitate effective monitoring, describes how tissue and DNA can be stored, and provides guidelines for proper practice.

Is rs34861192 or rs1862513 a more promising variant for determining plasma resistin in an aged Japanese population?

Is rs34861192 or rs1862513 a more promising variant for determining plasma resistin in an aged Japanese population?

Genetic Perspectives on Marine Biological Invasions

The extent to which the geographic distributions of marine organisms have been reshaped by human activities remains underappreciated, and so does, consequently, the impact of invasive species on marine ecosystems. The application of molecular genetic data in fields such as population genetics, phylogeography, and evolutionary biology have improved our ability to make inferences regarding invasion histories. Genetic methods have helped to resolve longstanding questions regarding the cryptogenic status of marine species, facilitated recognition of cryptic marine biodiversity, and provided means to determine the sources of introduced marine populations and to begin to recover the patterns of anthropogenic reshuffling of the ocean's biota. These approaches stand to aid materially in the development of effective management strategies and sustainable science-based policies. Continued advancements in the statistical analysis of genetic data promise to overcome some existing limitations of current approaches. Still other limitations will be best addressed by concerted collaborative and multidisciplinary efforts that recognize the important synergy between understanding the extent of biological invasions and coming to a more complete picture of both modern-day and historical marine biogeography.

Genetic model for longitudinal studies of aging, health, and longevity and its potential application to incomplete data

Many longitudinal studies of aging collect genetic information only for a sub-sample of participants of the study. These data also do not include recent findings, new ideas and methodological concepts developed by distinct groups of researchers. The formal statistical analyses of genetic data ignore this additional information and therefore cannot utilize the entire research potential of the data. In this paper, we present a stochastic model for studying such longitudinal data in joint analyses of genetic and non-genetic sub-samples. The model incorporates several major concepts of aging known to date and usually studied independently. These include age-specific physiological norms, allostasis and allostatic load, stochasticity, and decline in stress resistance and adaptive capacity with age. The approach allows for studying all these concepts in their mutual connection, even if respective mechanisms are not directly measured in data (which is typical for longitudinal data available to date). The model takes into account dependence of longitudinal indices and hazard rates on genetic markers and permits evaluation of all these characteristics for carriers of different alleles (genotypes) to address questions concerning genetic influence on aging-related characteristics. The method is based on extracting genetic information from the entire sample of longitudinal data consisting of genetic and non-genetic sub-samples. Thus it results in a substantial increase in the accuracy of statistical estimates of genetic parameters compared to methods that use only information from a genetic sub-sample. Such an increase is achieved without collecting additional genetic data. Simulation studies illustrate the increase in the accuracy in different scenarios for datasets structurally similar to the Framingham Heart Study. Possible applications of the model and its further generalizations are discussed.

Integrated analysis of genetic data with R

Genetic data are now widely available. There is, however, an apparent lack of concerted effort to produce software systems for statistical analysis of genetic data compared with other fields of statistics. It is often a tremendous task for end-users to tailor them for particular data, especially when genetic data are analysed in conjunction with a large number of covariates. Here, R http://www.r-project.org, a free, flexible and platform-independent environment for statistical modelling and graphics is explored as an integrated system for genetic data analysis. An overview of some packages currently available for analysis of genetic data is given. This is followed by examples of package development and practical applications. With clear advantages in data management, graphics, statistical analysis, programming, internet capability and use of available codes, it is a feasible, although challenging, task to develop it into an integrated platform for genetic analysis; this will require the joint efforts of many researchers.

Genetic Effects of a Persistent Bottleneck on a Natural Population of Ornate Box Turtles (Terrapene ornata)

Human activities in the past few hundred years have caused enormous impacts on many ecosystems, greatly accelerating the rate of population decline and extinction. In addition to habitat alteration and destruction, the loss of genetic diversity due to reduced population size has become a major conservation issue for many imperiled species. However, the genetic effects of persistent population bottlenecks can be very different for long-lived and short-lived species when considering the time scale of centuries. To investigate the genetic effects of persistent population bottlenecks on long-lived species, we use microsatellite markers to assess the level of genetic diversity of a small ornate box turtle population that has experienced a persistent bottleneck in the past century, and compare it to a large relatively undisturbed population. The genetic signature of a recent bottleneck is detected by examining the deviation from mutation-drift equilibrium in the small population, but the bottleneck had little effect on its level of genetic diversity. Computer simulations combined with information on population structure suggest that an effective population size of 300, which results in a census population size of 700, would be required for the small population to maintain 90% of the average number of alleles per locus in the next 200 years. The life history of long-lived species could mask the accelerated rate of genetic drift, making population recovery a relatively slow process. Statistical analysis of genetic data and empirical-based computer simulations can be important tools to facilitate conservation planning.

Unbiased discordant sib-pair tests when parental genotypes are missing

Population admixture and stratification are potential sources of confounding in a variety of statistical analyses of genetic data. Many approaches to adjusting for such confounding have been developed in the context of association analyses. These approaches may generally be viewed as relying on nonparametric null hypotheses that specify certain, sometimes incompletely observed, conditional distributions; the conditional distributions are used to normalize test statistics to have expectation zero under the null hypothesis. Here, with a very simple example, it is shown that there is potentially information that is free of confounding, but that is not recovered by such normalized statistics. An approach presented here in the context of the simple example might be extended to methods for recovering information in more complex settings.