Robust analysis with related samples under the presence of population substructure and its application to body mass index

Recent investigations such as a more powerful quasi-likelihoods score test (MQLS) statistic have enabled the efficient association analysis with related samples. Although those approaches are robust against the mis-specified phenotypic distribution and covariance structure, it has been shown that MQLS statistic becomes violated under the presence of the population substructure if the level of population substructure depends on the genomic location. In this report, we propose a new statistical method which combines EIGENSTRAT approach and MQLS-statistic. The proposed method was evaluated with simulation data under various scenarios and we found that proposed method performs better than the traditional methods such as transmission disequilibrium test. The proposed method was applied to genetic association analysis for body mass index with Framingham heart study, and we found that rs1121980 and rs9940128 in the linkage block in FTO gene are associated with the body mass index.     

Genetic analysis of host-parasite coevolution in human malaria.

Recent twin studies of clinical malaria and immune responses to malaria antigens have underscored the importance of both major histocompatability complex (MHC) and non-MHC genes in determining variable susceptibility and immune responsiveness. By using a combination of whole genome genetic linkage studies of families and candidate genes analysis, non-MHC genes are being mapped and identified. Human leucocyte antigen (HLA) genotype was found to affect susceptibility to severe malaria in a large study of West African children. T lymphocytes that may mediate such resistance have been identified and their target antigens and epitopes characterized. Some of these epitopes show substantial polymorphism, which appears to result from immune selection pressure. Natural variant epitopes have been found to escape T-cell recognition in cytolytic and other T-cell assays. More recently a novel immune escape mechanism has been described in viral infections, altered peptide ligand antagonism, whereby variants of a T-cell epitope can downregulate or ablate a T cell response to the index peptide. The likely implications of such immune escape mechanisms for the population structure of malaria parasites, for HLA associations with malaria infection and disease, and for the design of new malaria vaccines, are discussed. The evolutionary consequences of such molecular interactions can be assessed by using mathematical models that capture the dynamic of variable host and parasite molecules. Combined genetic, immunological and mathematical analysis of host and parasite variants in natural populations can identify some mechanisms driving host-parasite coevolution.     

Genetic variation, its assessment and implications to the conservation of seagrasses

In a study of the widespread Australian endemic seagrass Posidonia australis , allozyme analysis identified a wide range in population genetic structure assessed using the multilocus genotype diversity statistic ( D _G). Values of D _G between zero and one were obtained; however, RAPD analysis generally detected higher levels of diversity, where D _G values were all greater than 0.5 ( D _G = 0.67 – 1). Some populations were allozymically monomorphic using allozyme analysis yet were highly polymorphic using RAPD analysis. The differences observed between methods, particularly among allozymically uniform populations, demonstrate the importance of choosing an appropriate method when assessing genotypic diversity. Different methods may reflect different historical aspects of population processes where allozymes reflect broader-scale gene flow and population establishment and DNA fingerprinting methods such as RAPDs may reflect fine-scale local recruitment events and shorter-term population processes. Using either method alone, particularly in genotypically depauperate organisms such as seagrasses and other clonal organisms, will be problematic in assessing their population genetic potential, a parameter being used by conservation managers to decide upon management strategies in rare and endangered organisms. It is recommended that the impact of disturbance assessed using genotypic diversity measures requires more than one technique to provide the most appropriate information for designing subsequent conservation strategies.     

群体遗传学研究中的数据处理方法I．RAPD数据的AMOVA分析

近年来,RAPD数据和AMOVA分析广泛地应用于群体遗传学和保护遗传学研究。然而,由于RAPD标记具显性特点。加上目前进行AMOVA分析所依赖的RAPDistance软件不完善,使得对RAPD数据进行AMOVA分析时存在许多不足。本文介绍了AMOVA分析的基本过程,同时引入一个新的程序DCFA用以替代RADistance并详述了将DCFA与WINAMOVA联用,对RAPD数据进行AMOVA分析的具体步骤与注意事项,最后,以产自中国和巴西8个普通野生稻（Oryza furipogon)天然群体为例,演示了对RAPD表型数据进行AMOVA分析的过程,讨论了AMOVA分析结果在群体遗传结构上的意义。通过对AMOVA算法的分析,同时比较4种距离系数所得AMOVA结果,我们认为在进行AMOVA分析时选择NEI－LI距离和欧氏距离平方较为合适,而目前国内使用较多的JACCARD系数不适合AMOVA分析。     

Mutagenic DNA repair in escherichia coli. V. Mutation frequency decline and error-free post-replication repair in an excision-proficient strain.

Mutation frequency decline (MFD) is an irreversible loss of newly-induced suppressor mutations occurring in excision-proficient Escherichia coli during a short period of incubation in minimal medium before plating on broth- or Casamino acids-enriched selective agar. It is known that MFD of UV-induced mutations may occur before DNA containing pre-mutagenic lesions is replicated, but we conclude that MFD can also occur after the damaged DNA has been replicated on the basis of the following evidence. (1) Mutation fixation in rich medium (i.e., loss of susceptibility to mutation frequency decline) with ethyl methanesulphonate mutagenesis begins immediately, whereas with UV it is delayed for 20--30 min. (2) The delay in mutation fixation after UV can be explained neither by inhibition of DNA replication nor by a delay in the appearance of error-prone repair activity in the irradiated population. (3) MFD at later times after UV irradiation is more rapid and is less strongly inhibited by caffeine than is MFD immediately after irradiation. (4) Excision is virtually complete 20 min after 3 J m-2 UV but at that time virtually all mutations are still susceptible to MFD. We have presented evidence elsewhere that in bacteria there is an alternative error-free excision-dependent type of post-replication repair of potentially mutagenic daughter strand gaps. We suggest that this process is inhibited at tRNA loci in the presence of nutrient broth or Casamino acids, possibly because of a broth-dependent change in the structure of the single-stranded region including the tRNA locus.     

黄颡鱼母本的肠系膜脂肪沉积对繁育性能的影响

为探索肠系膜脂肪堆积对黄颡鱼母本繁育性能的影响,选择了4个母本群体来评价肠系膜脂肪沉积与黄颡鱼繁育性能的关联:group 1,野生黄颡鱼群体[肠系膜脂肪指数(0.56±0.17)%]; group 2,肠系膜脂肪较少的黄颡鱼养殖群体[肠系膜脂肪指数(1.97±0.40)%]; group 3和4,肠系膜脂肪较多的2个黄颡鱼养殖群体[肠系膜脂肪指数(5.92±1.85)%和(9.62±1.01)%]。研究结果显示肠系膜脂肪体重比与性腺指数总体上呈负相关。人工繁殖结果表明, group 1和group 2的产卵率、受精率和单尾母鱼出苗量无显著性差异,但显著高于group 3和group4; group 1和2的苗种畸形率显著低于group 3和4。黄颡鱼雌鱼血清中促黄体生成素(LH)和卵黄蛋白原(VTG)的水平随着肠系膜脂肪沉积量的增大而降低;相反,肝脏和卵子中的油脂和糖原含量随着肠系膜脂肪沉积量的增大而升高。研究发现,肠系膜脂肪过度沉积的黄颡鱼母本的生理指标和繁殖性能明显下降,生产出来的苗种质量欠佳;建立完善的亲本培育模式并减少肠系膜脂肪沉积能够显著改善繁殖性能,文章为提高鱼类苗种质量提...     

等位基因多态性群体遗传结构的多元非线性分析方法

长期以来,对于多维基因多态性数据的多元统计分析,如计算遗传距离时昕用的聚类分析、分析群体遗传结构时所用的主成分分析、因子分析和典型相关分析等,一直应用为无约束条件数据而设计的经典多元线性分析方法,并没有注意基因多态性数据的“闭合效应”所带来的问题。从分析基因多态性数据的分布和结构特征入手,文中指出了基因多态性分布具有“闭合数据”的特点,分析了由于“闭合效应”的影响,经典多元线性方法用于群体遗传结构分析昕面临的困难。根据成分数据统计分析的理论和方法,提出了基因多态性群体遗传结构的多元非线性分析基本方法。并以主成分分析为例,通过实例比较和分析了经典线性主成分分析和“对数比”非线性主成分分析的结果,证明“对数比”非线性主成分分析方法是研究基因多态性群体遗传结构的良好方法,具有特异、灵敏等优点,其结果符合群体遗传学规律。     

SGS--Spatial Genetic Software: a computer program for analysis of spatial genetic and phenotypic structures of individuals and populations.

We have developed a program called Spatial Genetic Software (SGS), which provides a user-friendly Windows tool to analyze both local and broad scale genetic and phenotypic structure. It can deal with nearly any type of genetic data, codominant (allozyme, PCR-RFLP, microsatellite) or dominant (RAPD, AFLP) markers, or biparentally (nuclear) or uniparentally (cpDNA and mtDNA) inherited markers. Data based on any of these markers can be analyzed, either as individual genotypes within a single population (local scale) or as allele or haplotype frequencies from different populations (broad scale). We also include a simple approach to analysis of spatial structure for continuous quantitative traits. The program implements various parameters to analyze spatial genetic and phenotypic structure: Moran's index, Geary's index, number of alleles in common, and approaches using genetic distances and F(ST) values. The statistical significance of all measures is verified by the use of a permutation test. The results are assessed by graphics that can be integrated, via the clipboard, to other Windows programs. The details of the computations are given in a table and can be stored as ASCII files.     

The use of quantitative traits in the study of human population structure

Studies of human population structure and history have tended to use demographic and/or serological data for analysis. This paper reviews the methods and studies that incorporate quantitative traits (usually polygenic traits) in such analyses. Methods of assessing the degree and pattern of among-group variation are discussed, and are characterized as being model-free or model-bound. Model-free methods deal with the measure of overall populational differentiation and with comparative methods for describing the pattern of differentiation. Model-bound methods are used for direct incorporation into theoretical models of population structure in order to estimate genetic parameters, such as those in admixture and isolation by distance models. To date, studies have indicated that quantitative traits may often be used successfully in studies of human population structure, and show effects of microevolutionary forces on quantitative variation among populations.     

Genetic analysis of the predisposition to uterine myoma. Prevalence and morbidity

Prevalence of uterine myoma (MU) was estimated in several Moscow districts. The overall average estimate of the MU prevalence is 2.45% among women of all groups. The prevalence MU estimates increase with the age, its maximum value reaching 8.31% at the age of 50 years. The morbidity risk estimates increased with the age as well, the maximum value being 2.98% at the age of 40-44 years. The value of "cumulative" morbidity risks, i. e. the probability to be affected, is 9.74% for a population living long enough, this value being based on the age-specific estimates of morbidity risks. Taking into consideration the autopsy data, indicating that frequency of MU, including small myomatous nodes, is 20%, the conclusion is made that MU is manifested by clinically expressed disturbances (urging a woman to address to a doctor) in 50% of cases only. Epidemiological data obtained are to be used later for genetic analysis of familial data on MU.     

Genetic parameters for medullated fiber and its relationship with other productive traits in alpacas

The alpaca fiber diameter (FD) varies from 18 to 36 μm, being the finer fiber categories highly appreciated. However, the alpaca fiber presents some limitations in the textile industry due to the high incidence of fiber medullation and diameter variability, both reduces the comfort feeling of the garments. Decreasing or even removing medullation could be a possible selection objective in alpaca breeding programs for increasing economic value of the alpaca fiber. Therefore, the present work aimed to estimate genetic parameters regarding medullation traits, as well as the genetic correlations with other economical important traits, to be able to select the appropriate criteria to reduce or remove medullation on alpaca fiber and help to reduce the prickle factor in the garments. The data was collected from 2000 to 2017 and belonged to the Pacomarca experimental farm. There were 3698 medullation records corresponding to 1869 Huacaya and 414 Suri genetic types. The fiber samples were taken from the mid side, and were analyzed in an OFDA 100® device. The traits analyzed were percentage of medullation (PM), medullated fiber diameter (MFD), FD, standard deviation of FD, greasy fleece weight as fiber traits; density, crimp in Huacaya and lock structure in Suri, head conformation, leg coverage as morphological traits; weaning weight and age at first calving as secondary and functional traits. Genetic parameters were estimated via a multitrait restricted maximum likelihood. The heritabilities for PM and MFD were 0.225 and 0.237 in Huacaya genetic type and 0.664 and 0.237 in Suri genetic type, respectively; heritabilities for other traits were moderate for productive and morphological traits, and low to moderate for secondary and functional traits. The genetic correlations PM–FD and MFD–FD were high and favorable in both genetic types, between 0.531 and 0.975; the genetic correlation PM–MFD was 0.121 in Huacaya and 0.427 in Suri. The rest of genetic correlations with other traits were in general moderate and favorable. The repeatabilities were 0.556 and 0.668 for PM, and 0.322 and 0.293 for MFD in Huacaya and Suri genetic types, respectively. As a conclusion, PM was identified to be a good selection criterion, probably combined in an index with FD to reduce prickling factor.     

居群遗传结构研究中显性标记数据方法初探

为对比显性标记应用于居群遗传结构研究时不同统计参数的适用性,利用RAPD技术对中国5个居群的100个疣粒野生稻个体进行了遗传结构分析。在衡量居群遗传多样性水平时,多态位点比率（PPB）会低估遗传变异的量,其价值不如Shannon多样性指数和Nei基因多样性指数,而采用Nei指数时不必进行Lynch-Milligan矫正。对个体间遗传关系进行分析时,17种遗传相似性指数矩阵两两之间的Mantel检测都表现出极显著的相关性（r＞0.95,t＞t     

Genetic mark-recapture analysis of winter faecal pellets allows estimation of population size in Sage Grouse Centrocercus urophasianus

The Sage Grouse Centrocercus urophasianus is a species of conservation concern throughout its range in western North America. Since the 1950s, the high count of males at leks has been used as an index for monitoring populations. However, the relationship between this lek-count index and population size is unclear, and its reliability for assessing population trends has been questioned. We used non-invasive genetic mark-recapture analysis of faecal and feather samples to estimate pre-breeding population size for the Parachute-Piceance-Roan, a small, geographically isolated population of Sage Grouse in western Colorado, during two consecutive winters from 2012 to 2014. We estimated total pre-breeding population size as 335 (95% confidence interval (CI): 287–382) in the first winter and 745 (95% CI: 627–864) in the second, an approximate doubling in abundance between years. Although we also observed a large increase in the spring lek-count index between those years, high male count data poorly represented mark-recapture estimates of male abundance in each year. Our data suggest that lek counts are useful for detecting the direction and magnitude of large changes in Sage Grouse abundance over time but they may not reliably reflect small changes in abundance that may be relevant to small populations of conservation concern.     

Genetic classification of populations using supervised learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.     

A review on metabolic pathway analysis with emphasis on isotope labeling approach

The recent progress on metabolic systems engineering was reviewed based on our recent research results in terms of (1) metabolic signal flow diagram approach, (2) metabolic flux analysis (MFA) in particular with intracellular isotopomer distribution using NMR and/or GC-MS, (3) synthesis and optimization of metabolic flux distribution (MFD), (4) modification of MFD by gene manipulation and by controlling culture environment, (5) metabolic control analysis (MCA), (6) design of metabolic regulation structure, and (7) identification of unknown pathways with isotope tracing by NMR. The main characteristics of metabolic engineering is to treat metabolism as a network or entirety instead of individual reactions. The applications were made for poly-3-hydroxybutyrate (PHB) production usingRalstonia eutropha and recombinantEscherichia coli, lactate production by recombinantSaccharomyces cerevisiae, pyruvate production by vitamin auxotrophic yeastToluropsis glabrata, lysine production usingCorynebacterium glutamicum, and energetic analysis of photosynthesic microorganisms such as Cyanobateria. The characteristics of each approach were reviewed with their applications. The approach based on isotope labeling experiments gives reliable and quantitative results for metabolic flux analysis. It should be recognized that the next stage should be toward the investigation of metabolic flux analysis with gene and protein expressions to uncover the metabolic regulation in relation to genetic modification and/or the change in the culture condition.     

Data registration and selective single-molecule analysis using multi-parameter fluorescence detection

A general strategy to identify and quantify sample molecules in dilute solution employing a new spectroscopic method for data registration and specific burst analysis denoted as multi-parameter fluorescence detection (MFD) was recently developed. While keeping the experimental advantage of monitoring single molecules diffusing through the microscopic open volume element of a confocal epi-illuminated set-up as in experiments of fluorescence correlation spectroscopy, MFD uses pulsed excitation and time-correlated single-photon counting to simultaneously monitor the evolution of the four-dimensional fluorescence information (intensity, F; lifetime, tau; anisotropy, r; and spectral range, lambda(r)) in real time and allows for exclusion of extraneous events for subsequent analysis. In this review, the versatility of this technique in confocal fluorescence spectroscopy will be presented by identifying freely diffusing single dyes via their characteristic fluorescence properties in homogenous assays, resulting in significantly reduced misclassification probabilities. Major improvements in background suppression are demonstrated by time-gated autocorrelation analysis of fluorescence intensity traces extracted from MFD data. Finally, applications of MFD to real-time conformational dynamics studies of fluorescence labeled oligonucleotides will be presented.     

Genetic analysis reveals population structure and recent migration within the highly fragmented range of the Cross River gorilla (Gorilla gorilla diehli)

Recently developed methods of individual-based analysis of genetic data allow an unprecedented opportunity to understand the relationships among fragmented populations. By defining population structure and identifying migrant individuals, such analyses can provide a framework to aid in evaluating the threats posed by inbreeding and reduced genetic variability as a consequence of limited gene flow among fragments. Here we investigate population structure in the critically endangered Cross River gorilla (Gorilla gorilla diehli) by applying a suite of individual-based analyses to data obtained from between one-quarter and one-third of the estimated total population through the use of noninvasively collected DNA samples. The population structure inferred using data from 11 autosomal microsatellite loci was broadly consistent with geography and habitat fragmentation, but showed no simple isolation-by-distance effects. In contrast to previous field surveys, which suggested that all gorilla localities were isolated from one another, we infer low levels of gene flow and identify migrants between habitat fragments as well as individuals of admixed ancestry, suggesting persistent recent reproductive contact between many of the localities. These results are encouraging for the conservation of the Cross River gorilla population. Conservation efforts should strive to maintain connectivity between subpopulations that are still in migratory contact and attempt to restore connectivity where it has been lost.     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.