A simulation study of the genetic regulatory hierarchy for butterfly eyespot focus determination

The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. Two types of models have been used to study these patterns. The first type of model employs computational techniques and generalized mechanisms of pattern formation to make predictions about how color patterns will vary as parameters of the model are changed. These generalized mechanisms include diffusion gradient, reaction-diffusion, lateral inhibition, and threshold responses. The second type of model uses known genetic interactions from Drosophila melanogaster and patterns of candidate gene expression in one of several butterfly species (most often Junonia (Precis) coenia or Bicyclus anynana) to propose specific genetic regulatory hierarchies that appear to be involved in color pattern formation. This study combines these two approaches using computational techniques to test proposed genetic regulatory hierarchies for the determination of butterfly eyespot foci (also known as border ocelli foci). Two computer programs, STELLA 8.1 and Delphi 2.0, were used to simulate the determination of eyespot foci. Both programs revealed weaknesses in a genetic model previously proposed for eyespot focus determination. On the basis of these simulations, we propose two revised models for eyespot focus determination and identify components of the genetic regulatory hierarchy that are particularly sensitive to changes in model parameter values. These components may play a key role in the evolution of butterfly eyespots. Simulations like these may be useful tools for the study of other evolutionary developmental model systems and reveal similar sensitive components of the relevant genetic regulatory hierarchies.     

Linkage analysis for complex diseases: a new life for an old method

In this paper, I present the main approaches used in gene mapping of complex human diseases by linkage analysis based on molecular markers. The first section describes the traditional LOD-score analysis and gives a review of different improvements and refinements of this approach, which has been proposed in order to take into account complicating factors such as incomplete penetrance, genetic heterogeneity and unknown mode of inheritance. The second section describes the three main approaches of non-parametric linkage analysis, for which there is no need to specify a genetic model (mode of inheritance, allele frequencies, ..). A comparison between these three methods, which may seem to be more appropriate for complex diseases, is given based on the most recent published studies. In the third section and discussion, I compare the LOD-score and non-parametric methods by stressing the advantages and drawbacks of each approach as found in recent publications. I deduce that, in spite of its apparent and assumed inappropriateness to the analysis of complex diseases, the LOD-score method is still very useful and could provide, in some circumstances, more power and precision than model-free methods.     

A comparison of methods to estimate cross-environment genetic correlations

Advanced techniques for quantitative genetic parameter estimation may not always be necessary to answer broad genetic questions. However, simpler methods are often biased, and the extent of this determines their usefulness. In this study we compare family mean correlations to least squares and restricted error maximum likelihood (REML) variance component approaches to estimating cross-environment genetic correlations. We analysed empirical data from studies where both types of estimates were made, and from studies in our own laboratories. We found that the agreement between estimates was better when full-sib rather than half-sib estimates of cross-environment genetic correlations were used and when mean family size increased. We also note biases in REML estimation that may be especially important when testing to see if correlations differ from 0 or 1. We conclude that correlations calculated from family means can be used to test for the presence of genetic correlations across environments, which is sufficient for some research questions. Variance component approaches should be used when parameter estimation is the objective, or if the goal is anything other than determining broad patterns.     

Admixture as the basis for genetic mapping

Genetic mapping in natural populations is increasing rapidly in feasibility and accessibility. As with many areas in genetics, advances in molecular techniques and statistics are drastically altering how we can investigate inheritance in wild organisms. For ecology and evolution, this is particularly significant and promising, because many of the organisms of interest are not amenable to conventional genetic approaches. Admixture mapping falls within a family of statistical approaches that use natural recombination and linkage disequilibrium between genetic markers and phenotypes as the basis for mapping. Our aim in this review is to provide a snapshot of previous and ongoing research, existing methods and challenges, the nature of questions that can be investigated and prospects for the future of admixture mapping.     

Effect of genetic heterogeneity and assortative mating on linkage analysis: a simulation study.

Linkage studies of complex genetic traits raise questions about the effects of genetic heterogeneity and assortative mating on linkage analysis. To further understand these problems, I have simulated and analyzed family data for a complex genetic disease in which disease phenotype is determined by two unlinked disease loci. Two models were studied, a two-locus threshold model and a two-locus heterogeneity model. Information was generated for a marker locus linked to one of the disease-defining loci. Random-mating and assortative-mating samples were generated. Linkage analysis was then carried out by use of standard methods, under the assumptions of a single-locus disease trait and a random-mating population. Results were compared with those from analysis of a single-locus homogeneous trait in samples with the same levels of assortative mating as those considered for the two-locus traits. The results show that (1) introduction of assortative mating does not, in itself, markedly affect the estimate of the recombination fraction; (2) the power of the analysis, reflected in the LOD scores, is somewhat lower with assortative rather than random mating. Loss of power is greater with increasing levels of assortative mating; and (3) for a heterogeneous genetic disease, regardless of mating type, heterogeneity analysis permits more accurate estimate of the recombination fraction but may be of limited use in distinguishing which families belong to each homogeneous subset. These simulations also confirmed earlier observations that linkage to a disease "locus" can be detected even if the disease is incorrectly defined as a single-locus (homogeneous) trait, although the estimated recombination fraction will be significantly greater than the true recombination fraction between the linked disease-defining locus and the marker locus.     

GENEHUNTER: your 'one-stop shop' for statistical genetic analysis?

The past decade has brought a proliferation of statistical genetic (linkage) analysis techniques, incorporating new methodology and/or improvement of existing methodology in gene mapping, specifically targeted towards the localization of genes underlying complex disorders. Most of these techniques have been implemented in user-friendly programs and made freely available to the genetics community. Although certain packages may be more 'popular' than others, a common question asked by genetic researchers is 'which program is best for me?'. To help researchers answer this question, the following software review aims to summarize the main advantages and disadvantages of the popular GENEHUNTER package.     

The molecular genetic basis of plant adaptation

How natural selection on adaptive traits is filtered to the genetic level remains largely unknown. Theory and quantitative trait locus (QTL) mapping have provided insights into the number and effect of genes underlying adaptations, but these results have been hampered by questions of applicability to real biological systems and poor resolution, respectively. Advances in molecular technologies have expedited the cloning of adaptive genes through both forward and reverse genetic approaches. Forward approaches start with adaptive traits and attempt to characterize their underlying genetic architectures through linkage disequilibrium mapping, QTL mapping, and other methods. Reverse screens search large sequence data sets for genes that possess the signature of selection. Though both approaches have been successful in identifying adaptive genes in plants, very few, if any, of these adaptations' molecular bases have been fully resolved. The continued isolation of plant adaptive genes will lead to a more comprehensive understanding of natural selection's effect on genes and genomes.     

Individual-specific liability groups in genetic linkage, with applications to kindreds with Li-Fraumeni syndrome

In this report, we present a simple and powerful way to incorporate individual-specific liability classes into linkage analysis. The proposed method is applicable to both quantitative and qualitative traits. In linkage studies, we may have information about different covariates. Incorporation of these covariates along with the estimates of residual familial effects, age-at-onset effects, and susceptibility in the definition of liability classes can increase the power to detect genetic linkage. In this study, we show how one can form individual-specific liability classes and use these classes in standard linkage-analysis programs, such as the widely used LINKAGE package, to perform more powerful genetic linkage analysis. Our simulation study shows that this approach yields higher LOD scores and more-accurate estimates of the recombination fraction in the families showing linkage. The proposed method is also applied to kindreds collected, at the M. D. Anderson Cancer Center, through probands with childhood soft-tissue sarcoma. Confirmed germ-line mutations in the p53 tumor-suppressor gene have been identified in these families. Application of our method to these families yielded significantly higher LOD scores and more-accurate recombination fractions than did analysis that did not account for individual-specific covariate information.     

On the use of genetic divergence for identifying species

Degree of genetic divergence is frequently used to infer that two populations belong to separate species, or that several populations belong to a single species. I explore the logical framework of this approach, including the following assumptions: (i) speciation takes place over very long periods of time; (ii) reproductive isolation is based on the slow accumulation many genetic differences throughout the genome; (iii) genetic divergence automatically leads to reproductive isolation between species; and (iv) pre-mating and post-mating reproductive isolation have a similar genetic basis. I argue that so many exceptions to these assumptions have been demonstrated that they cannot be used with any reliability to distinguish different species. In addition, genetic distance as a species criterion is mostly used within the framework of Mayr's Biological Species Concept and is not free of assumptions about the nature of species or of speciation. The use of genetic distance to infer separate species (or the lack of these) is not parsimonious, its theoretical foundations are not well understood, and it cannot be applied over a wide range of plants and animals. I explore alternative approaches towards solving the species problems normally solved using genetic distance. © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 75 , 509–516.     

Testing for segregation distortion in genetic scoring data from backcross or doubled haploid populations

It is important that breeders have the means to assess genetic scoring data for segregation distortion because of its probable effect on the design of efficient breeding strategies. Scoring data is usually assessed for segregation distortion by separate nonindependent chi2 tests at each locus in a set of marker loci. This analysis gives the loci most affected by selection if it exists, but it cannot give a statistically correct test for the presence or absence of selection in a linkage group as a whole. I have used a combined test based on the statistic, which is the most significant P-value from the above tests, called the single locus test. I have also derived mathematically a new combined statistical test, the overall test, for segregation distortion that requires genetic scoring data for a single linkage group. This test also takes genetic linkage into account. Using a range of marker densities and population sizes, simulations were carried out, to compare the power of these two statistical tests to detect the effect of selection at one or two loci. The single locus test was always found to be more powerful than the overall test, but the single locus test required a more complicated P-value correction. For the single locus test, approximate correction factors for the P-values are given for a range of marker densities and genetic lengths.     

Comparative linkage meta-analysis reveals regionally-distinct, disparate genetic architectures: application to bipolar disorder and schizophrenia

New high-throughput, population-based methods and next-generation sequencing capabilities hold great promise in the quest for common and rare variant discovery and in the search for "missing heritability." However, the optimal analytic strategies for approaching such data are still actively debated, representing the latest rate-limiting step in genetic progress. Since it is likely a majority of common variants of modest effect have been identified through the application of tagSNP-based microarray platforms (i.e., GWAS), alternative approaches robust to detection of low-frequency (1-5% MAF) and rare (<1%) variants are of great importance. Of direct relevance, we have available an accumulated wealth of linkage data collected through traditional genetic methods over several decades, the full value of which has not been exhausted. To that end, we compare results from two different linkage meta-analysis methods--GSMA and MSP--applied to the same set of 13 bipolar disorder and 16 schizophrenia GWLS datasets. Interestingly, we find that the two methods implicate distinct, largely non-overlapping, genomic regions. Furthermore, based on the statistical methods themselves and our contextualization of these results within the larger genetic literatures, our findings suggest, for each disorder, distinct genetic architectures may reside within disparate genomic regions. Thus, comparative linkage meta-analysis (CLMA) may be used to optimize low-frequency and rare variant discovery in the modern genomic era.     

Identifying genetic risk factors for osteoporosis

Over the past decades epidemiological research of so-called "complex" diseases, i.e., common age-related disorders such as cancer, cardiovascular disease, diabetes, and osteoporosis, has identified anthropometric, behavioural, and serum parameters as risk factors. Recently, genetic polymorphisms have gained considerable interest, propelled by the Human Genome Project and its sequela that have identified most genes and uncovered a plethora of polymorphic variants, some of which embody the genetic risk factors. In all fields of complex disease genetics (including osteoporosis) progress in identifying these genetic factors has been hampered by often controversial results. Because of the small effect size for each individual risk polymorphism, this is mostly due to low statistical power and limitations of analytical methods. Genome-wide scanning approaches can be used to find the responsible genes. It is by now clear that linkage analysis is not suitable for this, but genome-wide association analysis has much better possibilities, as is illustrated by successful identification of risk alleles for several complex diseases. Candidate gene association analysis followed by replication and prospective multi-centred meta-analysis, is currently the best way forward to identify genetic markers for complex traits, such as osteoporosis. To accomplish this, we need large (global) collaborative studies using standardized methodology and definitions, to quantify by meta-analysis the subtle effects of the responsible gene variants.     

高通量测序技术结合正向遗传学手段在基因定位研究中的应用

传统的利用正向遗传学方法的基因定位一般是通过构建遗传连锁图谱进行的,该过程步骤繁琐、耗时耗力,很多情形下定位精确度低、区间大。随着高通量测序技术的快速发展以及测序成本的不断降低,多种简单快捷的利用测序手段定位基因的方法被开发出来,包括对突变体基因组直接测序定位、突变体材料构建混池测序定位和遗传分离群体测序构建图谱定位等,还可以对转录组和部分基因组进行测序定位。这些方法可以在核苷酸水平鉴定突变位点,并已推广到复杂的遗传背景中。近期报道的一些测序定位甚至是在不依赖于参考基因组序列、遗传杂交和连锁信息的情况下完成的,这使得很多非模式物种也能开展正向遗传学研究。本文就这些新技术及其在基因定位中的应用进行了综述。     

Human QTL linkage mapping

Human quantitative trait locus (QTL) linkage mapping, although based on classical statistical genetic methods that have been around for many years, has been employed for genome-wide screening for only the last 10–15 years. In this time, there have been many success stories, ranging from QTLs that have been replicated in independent studies to those for which one or more genes underlying the linkage peak have been identified to a few with specific functional variants that have been confirmed in in vitro laboratory assays. Despite these successes, there is a general perception that linkage approaches do not work for complex traits, possibly because many human QTL linkage studies have been limited in sample size and have not employed the family configurations that maximize the power to detect linkage. We predict that human QTL linkage studies will continue to be productive for the next several years, particularly in combination with RNA expression level traits that are showing evidence of regulatory QTLs of large effect sizes and in combination with high-density genome-wide SNP panels. These SNP panels are being used to identify QTLs previously localized by linkage and linkage results are being used to place informative priors on genome-wide association studies.     

Using parentage analysis to examine gene flow and spatial genetic structure

Numerous approaches have been developed to examine recent and historical gene flow between populations, but few studies have used empirical data sets to compare different approaches. Some methods are expected to perform better under particular scenarios, such as high or low gene flow, but this, too, has rarely been tested. In this issue of Molecular Ecology , Saenz-Agudelo et   al . (2009 ) apply assignment tests and parentage analysis to microsatellite data from five geographically proximal (2–6 km) and one much more distant (1500 km) panda clownfish populations, showing that parentage analysis performed better in situations of high gene flow, while their assignment tests did better with low gene flow. This unusually complete data set is comprised of multiple exhaustively sampled populations, including nearly all adults and large numbers of juveniles, enabling the authors to ask questions that in many systems would be impossible to answer. Their results emphasize the importance of selecting the right analysis to use, based on the underlying model and how well its assumptions are met by the populations to be analysed.     

Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations

Linkage analysis and allelic association (also referred to as linkage disequilibrium) studies are two major approaches for mapping genes that control simple or complex traits in plants, animals, and humans. But these two approaches have limited utility when used alone, because they use only part of the information that is available for a mapping population. More recently, a new mapping strategy has been designed to integrate the advantages of linkage analysis and linkage disequilibrium analysis for genome mapping in outcrossing populations. The new strategy makes use of a random sample from a panmictic population and the open-pollinated progeny of the sample. In this article, we extend the new strategy to map quantitative trait loci (QTL), using molecular markers within the EM-implemented maximum-likelihood framework. The most significant advantage of this extension is that both linkage and linkage disequilibrium between a marker and QTL can be estimated simultaneously, thus increasing the efficiency and effectiveness of genome mapping for recalcitrant outcrossing species. Simulation studies are performed to test the statistical properties of the MLEs of genetic and genomic parameters including QTL allele frequency, QTL effects, QTL position, and the linkage disequilibrium of the QTL and a marker. The potential utility of our mapping strategy is discussed.     

Molecular genetic approaches for studying the etiology of diabetic nephropathy

A critical challenge faced by clinical nephrologists today is the escalating number of patients developing end stage renal disease, a major proportion of which is attributed to diabetic nephropathy (DN). The need for new measures to prevent and treat this disease cannot be overemphasized. To this end, modern genetic approaches provide powerful tools to investigate the etiology of DN. Human studies have already established the importance of genetic susceptibility for DN. Several major susceptibility loci have been identified using linkage studies. In addition, linkage studies in rodents have pinpointed promising chromosomal segments that influence renal traits. Besides augmenting our understanding of disease pathogenesis, these animal studies may facilitate the cloning of disease susceptibility genes in man through the identification of homologous regions that contribute to renal disease. In human diabetes, various genes have been evaluated for their risk contribution to DN. This widespread strategy has been propelled by our knowledge of the glucose-activated pathways underlying DN. Evidence has emerged that a true association does indeed exist for some candidate genes. Furthermore, the in vivo manipulation of gene expression has shown that these genes can modify features of DN in transgenic and knockout rodent models, thus corroborating the findings from human association studies. Still, the exact molecular mechanisms involving these genes remain to be fully elucidated. This formidable task may be accomplished by continuing to harness the synergy between human and experimental genetic approaches. In this respect, our review provides a first synthesis of the current literature to facilitate this challenging effort.     

An AFLP-based genetic linkage map of channel catfish (Ictalurus punctatus) constructed by using an interspecific hybrid resource family

Catfish is the major aquaculture species in the United States. The hybrid catfish produced by crossing channel catfish females with blue catfish males exhibit a number of desirable production traits, but their mass production has been difficult. To introduce desirable genes from blue catfish into channel catfish through introgression, a genetic linkage map is helpful. In this project, a genetic linkage map was constructed using amplified fragment length polymorphism (AFLP). A total of 607 AFLP markers were analyzed using 65 primer combinations and an interspecific backcross resource family. A total of 418 AFLP markers were assigned to 44 linkage groups. Among the remaining 189 markers, 101 were not used because of significant segregation distortion, 29 were unlinked, and 59 were eliminated because they span very large distances. The 418 AFLP markers covered 1593 cM Kosambi. The AFLP markers showed a high level of clustering that appears to be related to certain primer combinations. This linkage map will serve as the basis for mapping a greater number of markers to provide a map with high enough resolution for it to be useful for selective breeding programs using introgression.     

Which strategy is better for linkage analysis: single-nucleotide polymorphisms or microsatellites? Evaluation by identity-by-state – identity-by-descent transformation affected sib-pair method on GAW14 data

The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state – identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.     

A brief history of genetic variation analysis

As the human genome sequence is determined, there is an emerging need for the analysis of human sequence variations as genetic markers in diagnosis, linkage and association studies, cancer research, and pharmacogenomics. There are several different techniques and approaches for detecting these genetic variations, and here we review some of these techniques and their application fields. However, all the techniques have advantages and disadvantages, andfactors such as laboratory instrumentation, personnel experience, required accuracy, required throughput, and cost often have to be taken into account before selecting a method.