Genetic Association Studies: An Information Content Perspective

The availability of high-density single nucleotide polymorphisms (SNPs) data has made the human genetic association studies possible to identify common and rare variants underlying complex diseases in a genome-wide scale. A handful of novel genetic variants have been identified, which gives much hope and prospects for the future of genetic association studies. In this process, statistical and computational methods play key roles, among which information-based association tests have gained large popularity. This paper is intended to give a comprehensive review of the current literature in genetic association analysis casted in the framework of information theory. We focus our review on the following topics: (1) information theoretic approaches in genetic linkage and association studies; (2) entropy-based strategies for optimal SNP subset selection; and (3) the usage of theoretic information criteria in gene clustering and gene regulatory network construction.     

Molecular genetics of myocardial infarction

Myocardial infarction (MI) is an important clinical problem because of its large contribution to mortality. The main causal and treatable risk factors for MI include hypertension, hypercholesterolemia or dyslipidemia, diabetes mellitus, and smoking. In addition to these risk factors, recent studies have shown the importance of genetic factors and interactions between multiple genes and environmental factors. Disease prevention is an important strategy for reducing the overall burden of MI, with the identification of markers for disease risk being key both for risk prediction and for potential intervention to lower the chance of future events. Although genetic linkage analyses of families and sib-pairs as well as candidate gene and genome-wide association studies have implicated several loci and candidate genes in predisposition to coronary heart disease (CHD) or MI, the genes that contribute to genetic susceptibility to these conditions remain to be identified definitively. In this review, we summarize both candidate loci for CHD or MI identified by linkage analyses and candidate genes examined by association studies. We also review in more detail studies that have revealed the association with MI or CHD of polymorphisms in MTHFR, LPL, and APOE by the candidate gene approach and those in LTA and at chromosomal region 9p21.3 by genome-wide scans. Such studies may provide insight into the function of implicated genes as well as into the role of genetic factors in the development of CHD and MI.     

Probabilistic graphical models for genetic association studies

Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.     

Molecular genetic studies of gene identification for sarcopenia

Sarcopenia, which is characterized by a progressive decrease of skeletal muscle mass and function with aging, is closely related to several common diseases (such as cardiovascular and airway diseases) and functional impairment/disability. Strong genetic determination has been reported for muscle mass and muscle strength, two most commonly recognized and studied risk phenotypes for sarcopenia, with heritability ranging from 30 to 85% for muscle strength and 45–90% for muscle mass. Sarcopenia has been the subject of increasing genetic research over the past decade. This review is designed to comprehensively summarize the most important and representative molecular genetic studies designed to identify genetic factors associated with sarcopenia. We have methodically reviewed whole-genome linkage studies in humans, quantitative trait loci mapping in animal models, candidate gene association studies, newly reported genome-wide association studies, DNA microarrays and microRNA studies of sarcopenia or related skeletal muscle phenotypes. The major results of each study are tabulated for easy comparison and reference. The findings of representative studies are discussed with respect to their influence on our present understanding of the genetics of sarcopenia. This is a comprehensive review of molecular genetic studies of gene identification for sarcopenia, and an overarching theme for this review is that the currently accumulating results are tentative and occasionally inconsistent and should be interpreted with caution pending further investigation. Consequently, this overview should enhance recognition of the need to validate/replicate the genetic variants underlying sarcopenia in large human cohorts and animal. We believe that further progress in understanding the genetic etiology of sarcopenia will provide valuable insights into important fundamental biological mechanisms underlying muscle physiology that will ultimately lead to improved ability to recognize individuals at risk for developing sarcopenia and our ability to treat this debilitating condition.     

Genetics of LDL particle heterogeneity: from genetic epidemiology to DNA-based variations

Substantial evidence exists suggesting that small, dense LDL particles are associated with an increased risk of coronary heart disease. This disease-related risk factor is recognized to be under both genetic and environmental influences. Several studies have been conducted to elucidate the genetic architecture underlying this trait, and a review of this literature seems timely. The methods and strategies used to determine its genetic component and to identify the genes have greatly changed throughout the years owing to the progress made in genetic epidemiology and the influence of the Human Genome Project. Heritability studies, complex segregation analyses, candidate gene linkage and association studies, genome-wide linkage scans, and animal models are all part of the arsenal to determine the susceptibility genes. The compilation of these studies clearly revealed the complex genetic nature of LDL particles. This work is an attempt to summarize the growing evidence of genetic control on LDL particle heterogeneity with the aim of providing a concise overview in one read.     

The genetic basis of female mate preferences

We review the evidence for genetic variation in female and male mate preferences. Genetic differences between species and partially isolated races show that preferences can evolve and were genetically variable in the past. Within populations there is good evidence of genetic variation, both of discrete genetic effects (8 cases) and quantitative genetic effects (17 cases), from a diverse range of taxa. We also review evidence for the presence of genetic covariance between mate preferences and sexual traits in the other sex. The 11 studies go a long way to validating the theoretical prediction of positive genetic covariance. The few negative results are best explained by a lack of appropriate experimental design. One unresolved question is whether genetic covariance is due to linkage disequilibrium between unlinked genes or physical linkage. Some evidence points to linkage disequilibrium but this is not yet conclusive.     

Sequential Designs for Genetic Epidemiological Linkage or Association Studies A Review of the Literature

Objectives. The cost of a genetic linkage or association study is largely determined by the number of individuals to be recruited, phenotyped, and genotyped. The efficiency can be increased by using a sequential procedure that reduces time and cost on average. Two strategies for sequential designs in genetic epidemiological studies can be distinguished: One approach is to increase the sample size sequentially and to conduct multiple significance tests on accumulating data. If significance or futility can be assumed with a certain probability, the study is stopped. Otherwise, it is carried on to the next stage. The second approach is to conduct early linkage analyses on a coarse marker grid, and to increase marker density in later stages. Interim analyses are performed to select interesting genomic areas for follow up. The aim of this article is to give a review on sequential procedures in the context of genetic linkage and association studies. Methods. A systematic literature search was performed in the Medline and the Linkage Bibliography databases. Articles were defined as relevant if a sequential design was proposed or applied in genetic linkage or association studies. Results. The majority of proposed study designs is developed to meet the demands of specific studies and lacks a theoretical foundation. A second group of procedures is based on simulation results and principally restricted to the specific simulated situations. Finally, some theoretically founded procedures have been proposed that are discussed in detail. Conclusions. Although interesting and promising procedures have been suggested, they still lack realizations for practical purposes. In addition, further developments are required to adapt sequential strategies for optimal use in genetic epidemiological studies.     

The genetics of human personality

Personality traits are the relatively enduring patterns of thoughts, feelings and behaviors that reflect the tendency to respond in certain ways under certain circumstances. Twin and family studies have showed that personality traits are moderately heritable, and can predict various lifetime outcomes, including psychopathology. The Research Domain Criteria characterizes psychiatric diseases as extremes of normal tendencies, including specific personality traits. This implies that heritable variation in personality traits, such as neuroticism, would share a common genetic basis with psychiatric diseases, such as major depressive disorder. Despite considerable efforts over the past several decades, the genetic variants that influence personality are only beginning to be identified. We review these recent and increasingly rapid developments, which focus on the assessment of personality via several commonly used personality questionnaires in healthy human subjects. Study designs covered include twin, linkage, candidate gene association studies, genome‐wide association studies and polygenic analyses. Findings from genetic studies of personality have furthered our understanding about the genetic etiology of personality, which, like neuropsychiatric diseases themselves, is highly polygenic. Polygenic analyses have showed genetic correlations between personality and psychopathology, confirming that genetic studies of personality can help to elucidate the etiology of several neuropsychiatric diseases.     

Genomic and genetic aspects of autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a strong genetic component. The past decade has witnessed tremendous progress in the genetic studies of ASD. In this article, we review the accumulating literatures on the monogenic forms of ASD and chromosomal abnormalities associated with ASD, the genome-wide linkage and association studies, the copy number variation (CNV) and the next generation sequencing (NGS) studies. With more than hundreds of mutations being implicated, the convergent biological pathways are emerging and the genetic landscape of ASD becomes clearer. The genetic studies provide a solid basis for future translational study for better diagnoses, intervention and treatment of ASD.     

Progress in the genetics of common obesity: size matters

PURPOSE OF REVIEW: Over the past two decades serious efforts has been invested in the search for genes that predispose to common obesity, but progress has been slow and success limited. Genome-wide association, however, has revived optimism. Here we review recent advances in the field of obesity genetics and discuss the most important findings of candidate gene, genome-wide linkage studies and genome-wide association studies. We conclude by speculating about the way forward in the near future. RECENT FINDINGS: Although large-scale candidate gene studies have placed MC4R more firmly on the human obesity map, the major breakthrough in obesity genetics was the discovery of FTO through genome-wide association. Variants located in the first intron of FTO were unequivocally associated with a 1.67-fold increased risk for obesity and a 0.40-0.66 kg/m2 increase in body mass index. SUMMARY: Genome-wide association promises to enhance greatly our understanding of the genetic basis of common obesity, although candidate gene studies will remain a valuable approach because they allow more detailed analyses of biologically relevant candidates. A key factor contributing to continued success lies in large-scale data integration through international collaboration, which will provide the sample sizes required to identify genetic association with conclusive evidence.     

Allelic Association: Linkage Disequilibrium Structure and Gene Mapping

The linkage disequilibrium (LD) structure of the human genome is now well understood and characterised for a number of human populations. The LD structure underpins the design and execution of candidate gene and genome-wide association mapping studies. Successful association mapping studies completed to date provide vital new insights into the genetic influences on common diseases, such as diabetes, some cancers and heart disease. The LD structure also presents new avenues of research into the genetic history of human populations, the effects of natural selection and the impact of recombination on the genomic landscape. This review introduces this exciting and complex field by encompassing this range of topics.     

Residual linkage: why do linkage peaks not disappear after an association study?

Family-based candidate gene and genome-wide association studies are a logical progression from linkage studies for the identification of gene and polymorphisms underlying complex traits. An efficient way to analyse phenotypic and genotypic data is to model linkage and association simultaneously. An important result from such an analysis is whether any evidence for linkage remains after fitting polymorphisms at candidate genes (residual linkage), because this may indicate locus and allelic heterogeneity in the population and will influence subsequent molecular strategies. Here we report that substantial residual linkage is to be expected, even under genetic homogeneity and when the underlying causal polymorphisms are genotyped and fitted in the model. We simulated a powerful design to detect linkage to quantitative trait loci, with 5, 10 or 20 causal SNPs spread throughout the genome. These SNPs were responsible for all genetic variation, and hence for both linkage and association. Residual linkage at the largest linkage peak from a genome-wide scan was substantial, with mean LOD scores of 0.4, 0.7, and 1.4 for the case of 5, 10 and 20 underlying causal SNPs, respectively. For less powerful designs, the proportion of the original LOD scores that remains after association will be even larger. All cases of ‘significant’ residual linkage are false positives. The reason for the apparent paradox of detecting residual linkage after fitting causal polymorphisms is that the linkage signals at the largest peaks in a genome-scan are severely inflated, even if all peaks correspond to true linkage. Our findings are general and apply to linkage mapping of any phenotype and to any pedigree structure.     

Genetic susceptibility to ankylosing spondylitis

Ankylosing spondylitis is a highly heritable, common rheumatic condition, primarily affecting the axial skeleton. The association with HLA-B27 has been demonstrated worldwide, and evidence for a role of HLA-B27 in disease comes from linkage and association studies in humans, and transgenic animal models. However, twin studies indicate that HLA-B27 contributes only 16% of the total genetic risk for disease. Furthermore, there is compelling evidence that non-B27 genes, both within and outwith the major histocompatability complex, are involved in disease aetiology. In this post-genomic era we have the tools to help elicit the genetic basis of disease. This review describes methods for genetic investigation of ankylosing spondylitis, and summarises the status of current research in this exciting area.     

SNP frequency, haplotype structure and linkage disequilibrium in elite maize inbred lines

Background  

Recent studies of ancestral maize populations indicate that linkage disequilibrium tends to dissipate rapidly, sometimes within 100 bp. We set out to examine the linkage disequilibrium and diversity in maize elite inbred lines, which have been subject to population bottlenecks and intense selection by breeders. Such population events are expected to increase the amount of linkage disequilibrium, but reduce diversity. The results of this study will inform the design of genetic association studies.     

A common genetic variant in the neurexin superfamily member CNTNAP2 increases familial risk of autism

Autism is a childhood neuropsychiatric disorder that, despite exhibiting high heritability, has largely eluded efforts to identify specific genetic variants underlying its etiology. We performed a two-stage genetic study in which genome-wide linkage and family-based association mapping was followed up by association and replication studies in an independent sample. We identified a common polymorphism in contactin-associated protein-like 2 (CNTNAP2), a member of the neurexin superfamily, that is significantly associated with autism susceptibility. Importantly, the genetic variant displays a parent-of-origin and gender effect recapitulating the inheritance of autism.     

Dissecting the genetics of complex inheritance: linkage disequilibrium mapping provides insight into Crohn disease

Family studies for Crohn disease (CD) report extensive linkage on chromosome 16q and pinpoint NOD2 as a possible causative locus. However, linkage is also observed in families that do not bear the most frequent NOD2 causative mutations, but no other signals on 16q have been found so far in published genome-wide association studies. Our aim is to identify this missing genetic contribution. We apply a powerful genetic mapping approach to the Wellcome Trust Case-Control Consortium and the National Institute of Diabetes and Digestive and Kidney Diseases genome-wide association data on CD. This method takes into account the underlying structure of linkage disequilibrium (LD) by using genetic distances from LD maps and provides a location for the causal agent. We find genetic heterogeneity within the NOD2 locus and also show an independent and unsuspected involvement of the neighboring gene, CYLD. We find associations with the IRF8 region and the region containing CDH1 and CDH3, as well as substantial phenotypic and genetic heterogeneity for CD itself. The genes are known to be involved in inflammation and immune dysregulation. These findings provide insight into the genetics of CD and suggest promising directions for understanding disease heterogeneity. The application of this method thus paves the way for understanding complex inheritance in general, leading to the dissection of different pathways and ultimately, personalized treatment.     

基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。     

Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM).

A population association has consistently been observed between insulin-dependent diabetes mellitus (IDDM) and the "class 1" alleles of the region of tandem-repeat DNA (5'' flanking polymorphism [5''FP]) adjacent to the insulin gene on chromosome 11p. This finding suggests that the insulin gene region contains a gene or genes contributing to IDDM susceptibility. However, several studies that have sought to show linkage with IDDM by testing for cosegregation in affected sib pairs have failed to find evidence for linkage. As means for identifying genes for complex diseases, both the association and the affected-sib-pairs approaches have limitations. It is well known that population association between a disease and a genetic marker can arise as an artifact of population structure, even in the absence of linkage. On the other hand, linkage studies with modest numbers of affected sib pairs may fail to detect linkage, especially if there is linkage heterogeneity. We consider an alternative method to test for linkage with a genetic marker when population association has been found. Using data from families with at least one affected child, we evaluate the transmission of the associated marker allele from a heterozygous parent to an affected offspring. This approach has been used by several investigators, but the statistical properties of the method as a test for linkage have not been investigated. In the present paper we describe the statistical basis for this "transmission test for linkage disequilibrium" (transmission/disequilibrium test [TDT]). We then show the relationship of this test to tests of cosegregation that are based on the proportion of haplotypes or genes identical by descent in affected sibs. The TDT provides strong evidence for linkage between the 5''FP and susceptibility to IDDM. The conclusions from this analysis apply in general to the study of disease associations, where genetic markers are usually closely linked to candidate genes. When a disease is found to be associated with such a marker, the TDT may detect linkage even when haplotype-sharing tests do not.     

Family study designs in the age of genome-wide association studies: experience from the Framingham Heart Study

PURPOSE OF REVIEW: The past year has seen the publication of many genome-wide association studies, most of which are case-control studies. These publications are at the forefront of current research into the examination of genetic effects for numerous diseases, including diabetes, heart disease and cancer. Over the past 25 years the tour de force of genetics research has been in family studies, using segregation, linkage and association analyses. Are these approaches now passé? Here we discuss the role of family studies in modern genetics research, using results from the Framingham Heart Study as examples. RECENT FINDINGS: Family studies permit both linkage and association analyses. Importantly, family-based association tests that consider transmission of genetic variants within a family provide important information on the genetic etiology of disease traits and avoid the potential of false-positive findings due to population substructure. SUMMARY: Family-based study designs continue to contribute much to the modern era of genome-wide association studies.     

The search for loci under selection: trends,biases and progress

Detecting genetic variants under selection using F_ST outlier analysis (OA) and environmental association analyses (EAAs) are popular approaches that provide insight into the genetic basis of local adaptation. Despite the frequent use of OA and EAA approaches and their increasing attractiveness for detecting signatures of selection, their application to field‐based empirical data have not been synthesized. Here, we review 66 empirical studies that use Single Nucleotide Polymorphisms (SNPs) in OA and EAA. We report trends and biases across biological systems, sequencing methods, approaches, parameters, environmental variables and their influence on detecting signatures of selection. We found striking variability in both the use and reporting of environmental data and statistical parameters. For example, linkage disequilibrium among SNPs and numbers of unique SNP associations identified with EAA were rarely reported. The proportion of putatively adaptive SNPs detected varied widely among studies, and decreased with the number of SNPs analysed. We found that genomic sampling effort had a greater impact than biological sampling effort on the proportion of identified SNPs under selection. OA identified a higher proportion of outliers when more individuals were sampled, but this was not the case for EAA. To facilitate repeatability, interpretation and synthesis of studies detecting selection, we recommend that future studies consistently report geographical coordinates, environmental data, model parameters, linkage disequilibrium, and measures of genetic structure. Identifying standards for how OA and EAA studies are designed and reported will aid future transparency and comparability of SNP‐based selection studies and help to progress landscape and evolutionary genomics.