Common vs. rare allele hypotheses for complex diseases

Analysis of multiple related phenotypes in genome-wide association studies

Most genome-wide association studies (GWAS) have been conducted by focusing on one phenotype of interest for identifying genetic variants associated with common complex phenotypes. However, despite many successful results from GWAS, only a small number of genetic variants tend to be identified and replicated given a very stringent genome-wide significance criterion, and explain only a small fraction of phenotype heritability. In order to improve power by using more information from data, we propose an alternative multivariate approach, which considers multiple related phenotypes simultaneously. We demonstrate through computer simulation that the multivariate approach can improve power for detecting disease-predisposing genetic variants and pleiotropic variants that have simultaneous effects on multiple related phenotypes. We apply the multivariate approach to a GWA dataset of 8,842 Korean individuals genotyped for 327,872 SNPs, and detect novel genetic variants associated with metabolic syndrome related phenotypes. Considering several related phenotype simultaneously, the multivariate approach provides not only more powerful results than the conventional univariate approach but also clue to identify pleiotropic genes that are important to the pathogenesis of many related complex phenotypes.

A potential role for genome structure in the translation of mechanical force during immune cell development

Immune cells react to a wide range of environments, both chemical and physical. While the former has been extensively studied, there is growing evidence that physical and in particular mechanical forces also affect immune cell behavior and development. In order to elicit a response that affects immune cell behavior or development, environmental signals must often reach the nucleus. Chemical and mechanical signals can initiate signal transduction pathways, but mechanical forces may also have a more direct route to the nucleus, altering nuclear shape via mechanotransduction. The three-dimensional organization of DNA allows for the possibility that altering nuclear shape directly remodels chromatin, redistributing critical regulatory elements and proteins, and resulting in wide-scale gene expression changes. As such, integrating mechanotransduction and genome architecture into the immunology toolkit will improve our understanding of immune development and disease.

The genetic architecture of autism spectrum disorders (ASDs) and the potential importance of common regulatory genetic variants

Currently, there is great interest in identifying genetic variants that contribute to the risk of developing autism spectrum disorders (ASDs), due in part to recent increases in the frequency of diagnosis of these disorders worldwide. While there is nearly universal agreement that ASDs are complex diseases, with multiple genetic and environmental contributing factors, there is less agreement concerning the relative importance of common vs rare genetic variants in ASD liability. Recent observations that rare mutations and copy number variants (CNVs) are frequently associated with ASDs, combined with reduced fecundity of individuals with these disorders, has led to the hypothesis that ASDs are caused primarily by de novo or rare genetic mutations. Based on this model, large-scale whole-genome DNA sequencing has been proposed as the most appropriate method for discovering ASD liability genes. While this approach will undoubtedly identify many novel candidate genes and produce important new insights concerning the genetic causes of these disorders, a full accounting of the genetics of ASDs will be incomplete absent an understanding of the contributions of common regulatory variants, which are likely to influence ASD liability by modifying the effects of rare variants or, by assuming unfavorable combinations, directly produce these disorders. Because it is not yet possible to identify regulatory genetic variants by examination of DNA sequences alone, their identification will require experimentation. In this essay, I discuss these issues and describe the advantages of measurements of allelic expression imbalance (AEI) of mRNA expression for identifying cis-acting regulatory variants that contribute to ASDs.

Analysis with the exome array identifies multiple new independent variants in lipid loci

It has been hypothesized that low frequency (1-5% minor allele frequency (MAF)) and rare (<1% MAF) variants with large effect sizes may contribute to the missing heritability in complex traits. Here, we report an association analysis of lipid traits (total cholesterol, LDL-cholesterol, HDL-cholesterol triglycerides) in up to 27 312 individuals with a comprehensive set of low frequency coding variants (ExomeChip), combined with conditional analysis in the known lipid loci. No new locus reached genome-wide significance. However, we found a new lead variant in 26 known lipid association regions of which 16 were >1000-fold more significant than the previous sentinel variant and not in close LD (six had MAF <5%). Furthermore, conditional analysis revealed multiple independent signals (ranging from 1 to 5) in a third of the 98 lipid loci tested, including rare variants. Addition of our novel associations resulted in between 1.5- and 2.5-fold increase in the proportion of heritability explained for the different lipid traits. Our findings suggest that rare coding variants contribute to the genetic architecture of lipid traits.

An enhanced version of Cochran-Armitage trend test for genome-wide association studies

Genome-wide association studies the evaluation of association between candidate gene and disease status is widely carried out using Cochran-Armitage trend test. However, only a small number of research papers have evaluated the distribution of p-values for the Cochran-Armitage trend test. In this paper, an enhanced version of Cochran-Armitage trend test based on bootstrap approach is introduced. The achieved results confirm that the distribution of p-values of the proposed approach fits better to the uniform distribution, and it is thus concluded that the proposed method, which needs less assumptions in comparison with the conventional method, can be successfully used to test the genetic association.

AMD and the alternative complement pathway: genetics and functional implications

Age-related macular degeneration (AMD) is an ocular neurodegenerative disorder and is the leading cause of legal blindness in Western societies, with a prevalence of up to 8 % over the age of 60, which continues to increase with age. AMD is characterized by the progressive breakdown of the macula (the central region of the retina), resulting in the loss of central vision including visual acuity. While its molecular etiology remains unclear, advances in genetics and genomics have illuminated the genetic architecture of the disease and have generated attractive pathomechanistic hypotheses. Here, we review the genetic architecture of AMD, considering the contribution of both common and rare alleles to susceptibility, and we explore the possible mechanistic links between photoreceptor degeneration and the alternative complement pathway, a cascade that has emerged as the most potent genetic driver of this disorder.

Sequence capture by hybridization to explore modern and ancient genomic diversity in model and nonmodel organisms

The recent expansion of next-generation sequencing has significantly improved biological research. Nevertheless, deep exploration of genomes or metagenomic samples remains difficult because of the sequencing depth and the associated costs required. Therefore, different partitioning strategies have been developed to sequence informative subsets of studied genomes. Among these strategies, hybridization capture has proven to be an innovative and efficient tool for targeting and enriching specific biomarkers in complex DNA mixtures. It has been successfully applied in numerous areas of biology, such as exome resequencing for the identification of mutations underlying Mendelian or complex diseases and cancers, and its usefulness has been demonstrated in the agronomic field through the linking of genetic variants to agricultural phenotypic traits of interest. Moreover, hybridization capture has provided access to underexplored, but relevant fractions of genomes through its ability to enrich defined targets and their flanking regions. Finally, on the basis of restricted genomic information, this method has also allowed the expansion of knowledge of nonreference species and ancient genomes and provided a better understanding of metagenomic samples. In this review, we present the major advances and discoveries permitted by hybridization capture and highlight the potency of this approach in all areas of biology.

Selection and explosive growth alter genetic architecture and hamper the detection of causal rare variants

The role of rare alleles in complex phenotypes has been hotly debated, but most rare variant association tests (RVATs) do not account for the evolutionary forces that affect genetic architecture. Here, we use simulation and numerical algorithms to show that explosive population growth, as experienced by human populations, can dramatically increase the impact of very rare alleles on trait variance. We then assess the ability of RVATs to detect causal loci using simulations and human RNA-seq data. Surprisingly, we find that statistical performance is worst for phenotypes in which genetic variance is due mainly to rare alleles, and explosive population growth decreases power. Although many studies have attempted to identify causal rare variants, few have reported novel associations. This has sometimes been interpreted to mean that rare variants make negligible contributions to complex trait heritability. Our work shows that RVATs are not robust to realistic human evolutionary forces, so general conclusions about the impact of rare variants on complex traits may be premature.

Genome-wide dissection of the maize ear genetic architecture using multiple populations

Improvement of grain yield is an essential long-term goal of maize (Zea mays) breeding to meet continual and increasing food demands worldwide, but the genetic basis remains unclear. We used 10 different recombination inbred line (RIL) populations genotyped with high-density markers and phenotyped in multiple environments to dissect the genetic architecture of maize ear traits. Three methods were used to map the quantitative trait loci (QTLs) affecting ear traits. We found 17-34 minor- or moderate-effect loci that influence ear traits, with little epistasis and environmental interactions, totally accounting for 55.4-82% of the phenotypic variation. Four novel QTLs were validated and fine mapped using candidate gene association analysis, expression QTL analysis and heterogeneous inbred family validation. The combination of multiple different populations is a flexible and manageable way to collaboratively integrate widely available genetic resources, thereby boosting the statistical power of QTL discovery for important traits in agricultural crops, ultimately facilitating breeding programs.

Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples

OBJECTIVE

The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.

METHODS

This approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM).

RESULTS

In our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18).

CONCLUSION

We propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.

Epidemiology of cardiovascular disease: recent novel outlooks on risk factors and clinical approaches

INTRODUCTION

Cardiovascular (CVD) risk assessment with traditional risk factors (age, sex, blood pressure, lipids, smoking and diabetes) has remained relatively invariant over the past decades despite some inaccuracies associated with this approach. However, the search for novel, robust and cost-effective risk markers of CVD risk is ongoing.

AREAS COVERED

A large share of the major developments in CVD risk prediction during the past five years has been made in large-scale biomarker discovery and the so called 'omics' - the rapidly growing fields of genomics, transcriptomics, epigenetics and metabolomics. This review focuses on how these new technologies are helping drive primary CVD risk estimation forward in recent years, and speculates on how they could be utilized more effectively for discovering novel risk factors in the future. Expert commentary: The search for new CVD risk factors is currently undergoing a significant revolution as the simple relationship between single risk factors and disease will have to be replaced by models that strive to integrate the whole field of omics into medicine.

Recurrent Rare Genomic Copy Number Variants and Bicuspid Aortic Valve Are Enriched in Early Onset Thoracic Aortic Aneurysms and Dissections

Thoracic Aortic Aneurysms and Dissections (TAAD) are a major cause of death in the United States. The spectrum of TAAD ranges from genetic disorders, such as Marfan syndrome, to sporadic isolated disease of unknown cause. We hypothesized that genomic copy number variants (CNVs) contribute causally to early onset TAAD (ETAAD). We conducted a genome-wide SNP array analysis of ETAAD patients of European descent who were enrolled in the National Registry of Genetically Triggered Thoracic Aortic Aneurysms and Cardiovascular Conditions (GenTAC). Genotyping was performed on the Illumina Omni-Express platform, using PennCNV, Nexus and CNVPartition for CNV detection. ETAAD patients (n = 108, 100% European American, 28% female, average age 20 years, 55% with bicuspid aortic valves) were compared to 7013 dbGAP controls without a history of vascular disease using downsampled Omni 2.5 data. For comparison, 805 sporadic TAAD patients with late onset aortic disease (STAAD cohort) and 192 affected probands from families with at least two affected relatives (FTAAD cohort) from our institution were screened for additional CNVs at these loci with SNP arrays. We identified 47 recurrent CNV regions in the ETAAD, FTAAD and STAAD groups that were absent or extremely rare in controls. Nine rare CNVs that were either very large (>1 Mb) or shared by ETAAD and STAAD or FTAAD patients were also identified. Four rare CNVs involved genes that cause arterial aneurysms when mutated. The largest and most prevalent of the recurrent CNVs were at Xq28 (two duplications and two deletions) and 17q25.1 (three duplications). The percentage of individuals harboring rare CNVs was significantly greater in the ETAAD cohort (32%) than in the FTAAD (23%) or STAAD (17%) cohorts. We identified multiple loci affected by rare CNVs in one-third of ETAAD patients, confirming the genetic heterogeneity of TAAD. Alterations of candidate genes at these loci may contribute to the pathogenesis of TAAD.

Gene-based aggregate SNP associations between candidate AD genes and cognitive decline

Single nucleotide polymorphisms (SNPs) in and near ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, complement receptor 1 (CR1), EPHA1, EXOC3L2, FERMT2, HLA cluster (DRB5-DQA), INPP5D, MEF2C, MS4A cluster (MS4A3-MS4A6E), NME8, PICALM, PTK2B, SLC24A4, SORL1, and ZCWPW1 have been associated with Alzheimer's disease (AD) in large meta-analyses. We aimed to determine whether established AD-associated genes are associated with longitudinal cognitive decline by examining aggregate variation across these gene regions. In two single-sex cohorts of older, community-dwelling adults, we examined the association between SNPs in previously implicated gene regions and cognitive decline (age-adjusted person-specific cognitive slopes) using a Sequence Kernel Association Test (SKAT). In regions which showed aggregate significance, we examined the univariate association between individual SNPs in the region and cognitive decline. Only two of the original AD-associated SNPs were significantly associated with cognitive decline in our cohorts. We identified significant aggregate-level associations between cognitive decline and the gene regions BIN1, CD33, CELF1, CR1, HLA cluster, and MEF2C in the all-female cohort and significant associations with ABCA7, HLA cluster, MS4A6E, PICALM, PTK2B, SLC24A4, and SORL1 in the all-male cohort. We also identified a block of eight correlated SNPs in CD33 and several blocks of correlated SNPs in CELF1 that were significantly associated with cognitive decline in univariate analysis in the all-female cohort.

Interaction Between CYP4F2 rs2108622 and CPY4A11 rs9333025 Variants Is Significantly Correlated with Susceptibility to Ischemic Stroke and 20-Hydroxyeicosatetraenoic Acid Level

AIMS

To investigate the association of four variants of two CYP ω-hydroxylase genes and 20-hydroxyeicosatetraenoic acid (HETE) levels with ischemic stroke (IS) and whether gene-gene interactions between these genes increase the risk of IS.

METHODS

Three hundred ninety-six patients with IS and 378 controls were genotyped for rs2269231, rs9333025, rs2108622, and rs3093135. Gene-gene interactions were analyzed using generalized multifactor dimensionality reduction (GMDR) methods. The 20-HETE levels was measured in 218 IS patients and 126 controls.

RESULTS

The frequency of the GG genotype of rs9333025 was significantly higher in IS patients than in controls (p < 0.001). The GMDR analysis showed a significant gene-gene interaction between rs9333025 and rs2108622 (p = 0.0116). This gene-gene interaction predicted a significantly higher risk of IS in individuals carrying the genotypes of rs9333025 GG and rs2108622 GG (odds ratio = 1.92, 95% confidence interval = 1.12-4.26, p = 0.007). The plasma levels of 20-HETE were significantly higher in IS patients than in controls, and IS patients carrying the genotype combination of rs9333025 GG and rs2108622 GG had higher 20-HETE levels than IS patients with other combinations of the two variants.

CONCLUSION

CYP4A1l rs9333025 GG and CYP4F2 rs2108622 GG two-loci interaction significantly increases the risk for IS and an elevated 20-HETE level.

Genetic data: The new challenge of personalized medicine, insights for rheumatoid arthritis patients

Rapid advances in genotyping technology, analytical methods, and the establishment of large cohorts for population genetic studies have resulted in a large new body of information about the genetic basis of human rheumatoid arthritis (RA). Improved understanding of the root pathogenesis of the disease holds the promise of improved diagnostic and prognostic tools based upon this information. In this review, we summarize the nature of new genetic findings in human RA, including susceptibility loci and gene-gene and gene-environment interactions, as well as genetic loci associated with sub-groups of patients and those associated with response to therapy. Possible uses of these data are discussed, such as prediction of disease risk as well as personalized therapy and prediction of therapeutic response and risk of adverse events. While these applications are largely not refined to the point of clinical utility in RA, it seems likely that multi-parameter datasets including genetic, clinical, and biomarker data will be employed in the future care of RA patients.

Progress in methods for rare variant association

Empirical studies and evolutionary theory support a role for rare variants in the etiology of complex traits. Given this motivation and increasing affordability of whole-exome and whole-genome sequencing, methods for rare variant association have been an active area of research for the past decade. Here, we provide a survey of the current literature and developments from the Genetics Analysis Workshop 19 (GAW19) Collapsing Rare Variants working group. In particular, we present the generalized linear regression framework and associated score statistic for the 2 major types of methods: burden and variance components methods. We further show that by simply modifying weights within these frameworks we arrive at many of the popular existing methods, for example, the cohort allelic sums test and sequence kernel association test. Meta-analysis techniques are also described. Next, we describe the 6 contributions from the GAW19 Collapsing Rare Variants working group. These included development of new methods, such as a retrospective likelihood for family data, a method using genomic structure to compare cases and controls, a haplotype-based meta-analysis, and a permutation-based method for combining different statistical tests. In addition, one contribution compared a mega-analysis of family-based and population-based data to meta-analysis. Finally, the power of existing family-based methods for binary traits was compared. We conclude with suggestions for open research questions.

Complex genetics of pulmonary diseases: lessons from genome-wide association studies and next-generation sequencing

The advent of high-throughput technologies has provided exceptional assistance for lung scientists to discover novel genetic variants underlying the development and progression of complex lung diseases. However, the discovered variants thus far do not explain much of the estimated heritability of complex lung diseases. Here, we review the literature of successfully used genome-wide association studies (GWASs) and identified the polymorphisms that reproducibly underpin the susceptibility to various noncancerous complex lung diseases or affect therapeutic responses. We also discuss the inherent limitations of GWAS approaches and how the use of next-generation sequencing technologies has furthered our understanding about the genetic determinants of these diseases. Next, we describe the contribution of the metagenomics to understand the interactions of the airways microbiome with lung diseases. We then highlight the urgent need for new integrative genomics-phenomics methods to more effectively interrogate and understand multiple downstream "omics" (eg, chromatin modification patterns). Finally, we address the scarcity of genetic studies addressing under-represented populations such as African Americans and Hispanics.

From Single Variants to Protein Cascades: MULTISCALE MODELING OF SINGLE NUCLEOTIDE VARIANT SETS IN GENETIC DISORDERS

Understanding the role of genetics in disease has become a central part of medical research. Non-synonymous single nucleotide variants (nsSNVs) in coding regions of human genes frequently lead to pathological phenotypes. Beyond single variations, the individual combination of nsSNVs may add to pathogenic processes. We developed a multiscale pipeline to systematically analyze the existence of quantitative effects of multiple nsSNVs and gene combinations in single individuals on pathogenicity. Based on this pipeline, we detected in a data set of 842 nsSNVs discovered in 76 genes related to cardiomyopathies, associated nsSNV combinations in seven genes present in at least 70% of all 639 patient samples, but not in a control cohort of healthy humans. Structural analyses of these revealed primarily an influence on the protein stability. For amino acid substitutions located at the protein surface, we generally observed a proximity to putative binding pockets. To computationally analyze cumulative effects and their impact, pathogenicity methods are currently being developed. Our approach supports this process, as shown on the example of a cardiac phenotype but can be likewise applied to other diseases such as cancer.

Detecting the Common and Individual Effects of Rare Variants on Quantitative Traits by Using Extreme Phenotype Sampling

Next-generation sequencing technology has made it possible to detect rare genetic variants associated with complex human traits. In recent literature, various methods specifically designed for rare variants are proposed. These tests can be broadly classified into burden and nonburden tests. In this paper, we take advantage of the burden and nonburden tests, and consider the common effect and the individual deviations from the common effect. To achieve robustness, we use two methods of combining p-values, Fisher's method and the minimum-p method. In rare variant association studies, to improve the power of the tests, we explore the advantage of the extreme phenotype sampling. At first, we dichotomize the continuous phenotypes before analysis, and the two extremes are treated as two different groups representing a dichotomous phenotype. We next compare the powers of several methods based on extreme phenotype sampling and random sampling. Extensive simulation studies show that our proposed methods by using extreme phenotype sampling are the most powerful or very close to the most powerful one in various settings of true models when the same sample size is used.

Whole exome sequencing identifies novel candidate genes that modify chronic obstructive pulmonary disease susceptibility

Background

Chronic obstructive pulmonary disease (COPD) is characterized by an irreversible airflow limitation in response to inhalation of noxious stimuli, such as cigarette smoke. However, only 15–20 % smokers manifest COPD, suggesting a role for genetic predisposition. Although genome-wide association studies have identified common genetic variants that are associated with susceptibility to COPD, effect sizes of the identified variants are modest, as is the total heritability accounted for by these variants. In this study, an extreme phenotype exome sequencing study was combined with in vitro modeling to identify COPD candidate genes.

Results

We performed whole exome sequencing of 62 highly susceptible smokers and 30 exceptionally resistant smokers to identify rare variants that may contribute to disease risk or resistance to COPD. This was a cross-sectional case-control study without therapeutic intervention or longitudinal follow-up information. We identified candidate genes based on rare variant analyses and evaluated exonic variants to pinpoint individual genes whose function was computationally established to be significantly different between susceptible and resistant smokers. Top scoring candidate genes from these analyses were further filtered by requiring that each gene be expressed in human bronchial epithelial cells (HBECs). A total of 81 candidate genes were thus selected for in vitro functional testing in cigarette smoke extract (CSE)-exposed HBECs. Using small interfering RNA (siRNA)-mediated gene silencing experiments, we showed that silencing of several candidate genes augmented CSE-induced cytotoxicity in vitro.

Conclusions

Our integrative analysis through both genetic and functional approaches identified two candidate genes (TACC2 and MYO1E) that augment cigarette smoke (CS)-induced cytotoxicity and, potentially, COPD susceptibility.

Electronic supplementary material

The online version of this article (doi:10.1186/s40246-015-0058-7) contains supplementary material, which is available to authorized users.

Pooled-DNA Sequencing for Elucidating New Genomic Risk Factors, Rare Variants Underlying Alzheimer's Disease

Analyses of genome-wide association studies (GWAS) for complex disorders usually identify common variants with a relatively small effect size that only explain a small proportion of phenotypic heritability. Several studies have suggested that a significant fraction of heritability may be explained by low-frequency (minor allele frequency (MAF) of 1-5 %) and rare-variants that are not contained in the commercial GWAS genotyping arrays (Schork et al., Curr Opin Genet Dev 19:212, 2009). Rare variants can also have relatively large effects on risk for developing human diseases or disease phenotype (Cruchaga et al., PLoS One 7:e31039, 2012). However, it is necessary to perform next-generation sequencing (NGS) studies in a large population (>4,000 samples) to detect a significant rare-variant association. Several NGS methods, such as custom capture sequencing and amplicon-based sequencing, are designed to screen a small proportion of the genome, but most of these methods are limited in the number of samples that can be multiplexed (i.e. most sequencing kits only provide 96 distinct index). Additionally, the sequencing library preparation for 4,000 samples remains expensive and thus conducting NGS studies with the aforementioned methods are not feasible for most research laboratories.The need for low-cost large scale rare-variant detection makes pooled-DNA sequencing an ideally efficient and cost-effective technique to identify rare variants in target regions by sequencing hundreds to thousands of samples. Our recent work has demonstrated that pooled-DNA sequencing can accurately detect rare variants in targeted regions in multiple DNA samples with high sensitivity and specificity (Jin et al., Alzheimers Res Ther 4:34, 2012). In these studies we used a well-established pooled-DNA sequencing approach and a computational package, SPLINTER (short indel prediction by large deviation inference and nonlinear true frequency estimation by recursion) (Vallania et al., Genome Res 20:1711, 2010), for accurate identification of rare variants in large DNA pools. Given an average sequencing coverage of 30× per haploid genome, SPLINTER can detect rare variants and short indels up to 4 base pairs (bp) with high sensitivity and specificity (up to 1 haploid allele in a pool as large as 500 individuals). Step-by-step instructions on how to conduct pooled-DNA sequencing experiments and data analyses are described in this chapter.

NGS for Sequence Variants

Recent technological advances in next-generation sequencing (NGS) provide unprecedented power to sequence personal genomes, characterize genomic landscapes, and detect a large number of sequence variants. The discovery of disease-causing variants in patients' genomes has dramatically changed our perspective on precision medicine. This chapter provides an overview of sequence variant detection and analysis in NGS study. We outline the general methods for identifying different types of sequence variants from NGS data. We summarize the common approaches for analyzing and visualizing casual variants associated with complex diseases on precision medicine informatics.

Genetics and psychotic disorders: A fresh look at consanguinity

Consanguineous unions refer to marriages between related individuals who share a common ancestor. These unions are still commonplace in certain regions of the world such as the southern coast of the Mediterranean, throughout the Middle East and South-East Asia. According to available data, couples of second cousins or closer and their offspring currently represent 10.4% of the world's population, thus resulting in increased frequencies of autosomal recessive disorders. Furthermore, consanguinity may be implicated in the increased frequency of multifactorial pathologies such as mental disorders. The few existing epidemiological studies in consanguineous and/or geographically isolated populations confirm that there is a significant association between consanguinity and mental disorders and a higher risk of schizophrenia or bipolar disorders among offspring from consanguineous couples. There exists a strong and complex genetic component in the predisposition to psychotic disorders that has been confirmed in numerous studies. However, the genetic basis of these disorders remains poorly understood. GWAS studies (Genome Wide Association Studies) over the past 10 years have identified a few weak associations, thus refuting the "common diseases-common variants" hypothesis. A model implicating numerous rare variants has been supported by the recent discovery of CNVs (Copy Number Variants) and their statistically significant association with psychiatric disorders such as schizophrenia, bipolar disorders and autism. The study of consanguineous families may contribute to identifying rare variants in homogenous populations who have conserved certain alleles. Major developments in molecular biology techniques would facilitate these studies as well as contributing to identifying major genes. These results emphasize the need for genetic counseling in high-risk communities and the importance of implementing preventive actions and raising awareness concerning the risk of consanguineous unions.

GABRB2 Haplotype Association with Heroin Dependence in Chinese Population

Substance dependence is a frequently observed comorbid disorder in schizophrenia, but little is known about genetic factors possibly shared between the two psychotic disorders. GABRB2, a schizophrenia candidate gene coding for GABAA receptor β2 subunit, is examined for possible association with heroin dependence in Han Chinese population. Four single nucleotide polymorphisms (SNPs) in GABRB2, namely rs6556547 (S1), rs1816071 (S3), rs18016072 (S5), and rs187269 (S29), previously associated with schizophrenia, were examined for their association with heroin dependence. Two additional SNPs, rs10051667 (S31) and rs967771 (S32), previously associated with alcohol dependence and bipolar disorder respectively, were also analyzed. The six SNPs were genotyped by direct sequencing of PCR amplicons of target regions for 564 heroin dependent individuals and 498 controls of Han Chinese origin. Interestingly, it was found that recombination between the haplotypes of all-derived-allele (H1; OR = 1.00) and all-ancestral-allele (H2; OR = 0.74) at S5-S29 junction generated two recombinants H3 (OR = 8.51) and H4 (OR = 5.58), both conferring high susceptibility to heroin dependence. Additional recombination between H2 and H3 haplotypes at S1-S3 junction resulted in a risk-conferring haplotype H5 (OR = 1.94x109). In contrast, recombination between H1 and H2 haplotypes at S3-S5 junction rescued the risk-conferring effect of recombination at S5-S29 junction, giving rise to the protective haplotype H6 (OR = 0.68). Risk-conferring effects of S1-S3 and S5-S29 crossovers and protective effects of S3-S5 crossover were seen in both pure heroin dependent and multiple substance dependence subgroups. In conclusion, significant association was found with haplotypes of the S1-S29 segment in GABRB2 for heroin dependence in Han Chinese population. Local recombination was an important determining factor for switching haplotypes between risk-conferring and protective statuses. The present study provide evidence for the schizophrenia candidate gene GABRB2 to play a role in heroin dependence, but replication of these findings is required.

A frequent hypofunctional IRAK2 variant is associated with reduced spontaneous hepatitis C virus clearance

Patients carrying very rare loss-of-function mutations in interleukin-1 receptor-associated kinase 4 (IRAK4), a critical signaling mediator in Toll-like receptor signaling, are severely immunodeficient, highlighting the paramount role of IRAK kinases in innate immunity. We discovered a comparatively frequent coding variant of the enigmatic human IRAK2, L392V (rs3844283), which is found homozygously in ∼15% of Caucasians, to be associated with a reduced ability to induce interferon-alpha in primary human plasmacytoid dendritic cells in response to hepatitis C virus (HCV). Cytokine production in response to purified Toll-like receptor agonists was also impaired. Additionally, rs3844283 was epidemiologically associated with a chronic course of HCV infection in two independent HCV cohorts and emerged as an independent predictor of chronic HCV disease. Mechanistically, IRAK2 L392V showed intact binding to, but impaired ubiquitination of, tumor necrosis factor receptor-associated factor 6, a vital step in signal transduction.

CONCLUSION

Our study highlights IRAK2 and its genetic variants as critical factors and potentially novel biomarkers for human antiviral innate immunity.

Genetics of leprosy: Expected-and unexpected-developments and perspectives

A solid body of evidence produced over decades of intense research supports the hypothesis that leprosy phenotypes are largely dependent on the genetic characteristics of the host. The early evidence of a major gene effect controlling susceptibility to leprosy came from studies of familial aggregation, twins, and complex segregation analysis. Later, linkage and association analysis, first applied to the investigation of candidate genes and chromosomal regions and more recently, to genome-wide scans, have revealed several HLA and non-HLA gene variants as risk factors for leprosy phenotypes such as disease per se, its clinical forms, and leprosy reactions. In addition, powerful, hypothesis-free strategies such as genome-wide association studies have led to an exciting, unexpected development: Leprosy susceptibility genes seem to be shared with Crohn's and Parkinson's disease. Today, a major challenge is to find the exact variants causing the biological effect underlying the genetic associations. New technologies, such as Next Generation Sequencing-that allows, for the first time, the cost- and time-effective sequencing of a complete human genome-hold the promise to reveal such variants; thus, strategies can be developed to study the functional impact of these variants in the context of infection, hopefully leading to the development of new targets for leprosy treatment and prevention.

Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method

The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.

Genetic analysis of the isolated Faroe Islands reveals SORCS3 as a potential multiple sclerosis risk gene

Background:

In search of the missing heritability in multiple sclerosis (MS), additional approaches adding to the genetic discoveries of large genome-wide association studies are warranted.

Objective:

The objective of this research paper is to search for rare genetic MS risk variants in the genetically homogenous population of the isolated Faroe Islands.

Methods:

Twenty-nine Faroese MS cases and 28 controls were genotyped with the HumanOmniExpressExome-chip. The individuals make up 1596 pair-combinations in which we searched for identical-by-descent shared segments using the PLINK-program.

Results:

A segment spanning 63 SNPs with excess case-case-pair sharing was identified (0.00173 < p > 0.00212). A haplotype consisting of 42 of the 63 identified SNPs which spanned the entire the Sortilin-related vacuolar protein sorting 10 domain containing receptor 3 ( SORCS3) gene had a carrier frequency of 0.34 in cases but was not present in any controls ( p = 0.0008).

Conclusion:

This study revealed an oversharing in case-case-pairs of a segment spanning 63 SNPs and the entire SORCS3. While not previously associated with MS, SORCS3 appears to be important in neuronal plasticity through its binding of neurotrophin factors and involvement in glutamate homeostasis. Although additional work is needed to scrutinise the genetic effect of the SORCS3-covering haplotype, this study suggests that SORCS3 may also be important in MS pathogenesis.

A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005<MAF<0.05) variants. In addition to confirmation of most known CAD loci, we identified 10 novel loci, eight additive and two recessive, that contain candidate genes that newly implicate biological processes in vessel walls. We observed intra-locus allelic heterogeneity but little evidence of low frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size.

An introductory review of parallel independent component analysis (p-ICA) and a guide to applying p-ICA to genetic data and imaging phenotypes to identify disease-associated biological pathways and systems in common complex disorders

Complex inherited phenotypes, including those for many common medical and psychiatric diseases, are most likely underpinned by multiple genes contributing to interlocking molecular biological processes, along with environmental factors (Owen et al., 2010). Despite this, genotyping strategies for complex, inherited, disease-related phenotypes mostly employ univariate analyses, e.g., genome wide association. Such procedures most often identify isolated risk-related SNPs or loci, not the underlying biological pathways necessary to help guide the development of novel treatment approaches. This article focuses on the multivariate analysis strategy of parallel (i.e., simultaneous combination of SNP and neuroimage information) independent component analysis (p-ICA), which typically yields large clusters of functionally related SNPs statistically correlated with phenotype components, whose overall molecular biologic relevance is inferred subsequently using annotation software suites. Because this is a novel approach, whose details are relatively new to the field we summarize its underlying principles and address conceptual questions regarding interpretation of resulting data and provide practical illustrations of the method.

Genome-wide Association Study of Autism Spectrum Disorder in the East Asian Populations

Autism spectrum disorder is a heterogeneous neurodevelopmental disorder with strong genetic basis. To identify common genetic variations conferring the risk of ASD, we performed a two-stage genome-wide association study using ASD family and healthy control samples obtained from East Asian populations. A total of 166 ASD families (n = 500) and 642 healthy controls from the Japanese population were used as the discovery cohort. Approximately 900,000 single nucleotide polymorphisms (SNPs) were genotyped using Affymetrix Genome-Wide Human SNP array 6.0 chips. In the replication stage, 205 Japanese ASD cases and 184 healthy controls, as well as 418 Chinese Han trios (n = 1,254), were genotyped by TaqMan platform. Case-control analysis, family based association test, and transmission/disequilibrium test (TDT) were then conducted to test the association. In the discovery stage, significant associations were suggested for 14 loci, including 5 known ASD candidate genes: GPC6, JARID2, YTHDC2, CNTN4, and CSMD1. In addition, significant associations were identified for several novel genes with intriguing functions, such as JPH3, PTPRD, CUX1, and RIT2. After a meta-analysis combining the Japanese replication samples, the strongest signal was found at rs16976358 (P = 6.04 × 10(-7)), which is located near the RIT2 gene. In summary, our results provide independent support to known ASD candidate genes and highlight a number of novel genes warranted to be further investigated in a larger sample set in an effort to improve our understanding of the genetic basis of ASD.

Genetics of leprosy: expected and unexpected developments and perspectives

A solid body of evidence produced over decades of intense research supports the hypothesis that leprosy phenotypes are largely dependent on the genetic characteristics of the host. The early evidence of a major gene effect controlling susceptibility to leprosy came from studies of familial aggregation, twins, and Complex Segregation Analysis. Later, linkage and association analysis, first applied to the investigation of candidate genes and chromosomal regions and more recently, to genome-wide scans, have revealed several leukocyte antigen complex and nonleukocyte antigen complex gene variants as risk factors for leprosy phenotypes such as disease per se, its clinical forms and leprosy reactions. In addition, powerful, hypothesis-free strategies such as Genome-Wide Association Studies have led to an exciting, unexpected development: Leprosy susceptibility genes seem to be shared with Crohn's and Parkinson's diseases. Today, a major challenge is to find the exact variants causing the biological effect underlying the genetic associations. New technologies, such as Next Generation Sequencing that allows, for the first time, the cost and time-effective sequencing of a complete human genome, hold the promise to reveal such variants. Strategies can be developed to study the functional effect of these variants in the context of infection, hopefully leading to the development of new targets for leprosy treatment and prevention.

Enrichment of Minor Alleles of Common SNPs and Improved Risk Prediction for Parkinson's Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder in the aged population and thought to involve many genetic loci. While a number of individual single nucleotide polymorphisms (SNPs) have been linked with PD, many remain to be found and no known markers or combinations of them have a useful predictive value for sporadic PD cases. The collective effects of genome wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have recently been shown to be linked with quantitative variations of numerous complex traits in model organisms with higher MAC more likely linked with lower fitness. Here we found that PD cases had higher MAC than matched controls. A set of 37564 SNPs with MA (MAF < 0.4) more common in cases (P < 0.05) was found to have the best predictive accuracy. A weighted risk score calculated by using this set can predict 2% of PD cases (100% specificity), which is comparable to using familial PD genes to identify familial PD cases. These results suggest a novel genetic component in PD and provide a useful genetic method to identify a small fraction of PD cases.

BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide polymorphisms

BACKGROUND

High-throughput genetic testing is increasingly applied in clinics. Next-Generation Sequencing (NGS) data analysis however still remains a great challenge. The interpretation of pathogenicity of single variants or combinations of variants is crucial to provide accurate diagnostic information or guide therapies.

METHODS

To facilitate the interpretation of variants and the selection of candidate non-synonymous polymorphisms (nsSNPs) for further clinical studies, we developed BALL-SNP. Starting from genetic variants in variant call format (VCF) files or tabular input, our tool, first, visualizes the three-dimensional (3D) structure of the respective proteins from the Protein Data Bank (PDB) and highlights mutated residues, automatically. Second, a hierarchical bottom up clustering on the nsSNPs within the 3D structure is performed to identify nsSNPs, which are close to each other. The modular and flexible implementation allows for straightforward integration of different databases for pathogenic and benign variants, but also enables the integration of pathogenicity prediction tools. The collected background information of all variants is presented below the 3D structure in an easily interpretable table format.

RESULTS

First, we integrated different data resources into BALL-SNP, including databases containing information on genetic variants such as ClinVar or HUMSAVAR; third party tools that predict stability or pathogenicity in silico such as I-Mutant2.0; and additional information derived from the 3D structure such as a prediction of binding pockets. We then explored the applicability of BALL-SNP on the example of patients suffering from cardiomyopathies. Here, the analysis highlighted accumulation of variations in the genes JUP, VCL, and SMYD2.

CONCLUSION

Software solutions for analyzing high-throughput genomics data are important to support diagnosis and therapy selection. Our tool BALL-SNP, which is freely available at http://www.ccb.uni-saarland.de/BALL-SNP, combines genetic information with an easily interpretable and interactive, graphical representation of amino acid changes in proteins. Thereby relevant information from databases and computational tools is presented. Beyond this, proximity to functional sites or accumulations of mutations with a potential collective effect can be discovered.

Age-related macular degeneration: genome-wide association studies to translation

In recent years, genome-wide association studies (GWAS), which are able to analyze the contribution to disease of genetic variations that are common within a population, have attracted considerable investment. Despite identifying genetic variants for many conditions, they have been criticized for yielding data with minimal clinical utility. However, in this regard, age-related macular degeneration (AMD), the most common form of blindness in the Western world, is a striking exception. Through GWAS, common genetic variants at a number of loci have been discovered. Two loci in particular, including genes of the complement cascade on chromosome 1 and the ARMS2/HTRA1 genes on chromosome 10, have been shown to convey significantly increased susceptibility to developing AMD. Today, although it is possible to screen individuals for a genetic predisposition to the disease, effective interventional strategies for those at risk of developing AMD are scarce. Ongoing research in this area is nonetheless promising. After providing brief overviews of AMD and common disease genetics, we outline the main recent advances in the understanding of AMD, particularly those made through GWAS. Finally, the true merit of these findings and their current and potential translational value is examined.Genet Med 18 4, 283-289.

Likelihood ratio tests in rare variant detection for continuous phenotypes

It is believed that rare variants play an important role in human phenotypes; however, the detection of rare variants is extremely challenging due to their very low minor allele frequency. In this paper, the likelihood ratio test (LRT) and restricted likelihood ratio test (ReLRT) are proposed to test the association of rare variants based on the linear mixed effects model, where a group of rare variants are treated as random effects. Like the sequence kernel association test (SKAT), a state-of-the-art method for rare variant detection, LRT and ReLRT can effectively overcome the problem of directionality of effect inherent in the burden test in practice. By taking full advantage of the spectral decomposition, exact finite sample null distributions for LRT and ReLRT are obtained by simulation. We perform extensive numerical studies to evaluate the performance of LRT and ReLRT, and compare to the burden test, SKAT and SKAT-O. The simulations have shown that LRT and ReLRT can correctly control the type I error, and the controls are robust to the weights chosen and the number of rare variants under study. LRT and ReLRT behave similarly to the burden test when all the causal rare variants share the same direction of effect, and outperform SKAT across various situations. When both positive and negative effects exist, LRT and ReLRT suffer from few power reductions compared to the other two competing methods; under this case, an additional finding from our simulations is that SKAT-O is no longer the optimal test, and its power is even lower than that of SKAT. The exome sequencing SNP data from Genetic Analysis Workshop 17 were employed to illustrate the proposed methods, and interesting results are described.

Genetics and Epigenetics of Eating Disorders

Eating disorders (EDs) are serious psychiatric conditions influenced by biological, psychological, and sociocultural factors. A better understanding of the genetics of these complex traits and the development of more sophisticated molecular biology tools have advanced our understanding of the etiology of EDs. The aim of this review is to critically evaluate the literature on the genetic research conducted on three major EDs: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). We will first review the diagnostic criteria, clinical features, prevalence, and prognosis of AN, BN, and BED, followed by a review of family, twin, and adoption studies. We then review the history of genetic studies of EDs covering linkage analysis, candidate gene association studies, genome-wide association studies, and the study of rare variants in EDs. Our review also incorporates a translational perspective by covering animal models of ED-related phenotypes. Finally, we review the nascent field of epigenetics of EDs and a look forward to future directions for ED genetic research.

A powerful approach to test an optimally weighted combination of rare variants in admixed populations

Population stratification has long been recognized as an issue in genetic association studies because unrecognized population stratification can lead to both false-positive and false-negative findings and can obscure true association signals if not appropriately corrected. This issue can be even worse in rare variant association analyses because rare variants often demonstrate stronger and potentially different patterns of stratification than common variants. To correct for population stratification in genetic association studies, we proposed a novel method to Test the effect of an Optimally Weighted combination of variants in Admixed populations (TOWA) in which the analytically derived optimal weights can be calculated from existing phenotype and genotype data. TOWA up weights rare variants and those variants that have strong associations with the phenotype. Additionally, it can adjust for the direction of the association, and allows for local ancestry difference among study subjects. Extensive simulations show that the type I error rate of TOWA is under control in the presence of population stratification and it is more powerful than existing methods. We have also applied TOWA to a real sequencing data. Our simulation studies as well as real data analysis results indicate that TOWA is a useful tool for rare variant association analyses in admixed populations.

Cytochrome 4A11 Genetic Polymorphisms Increase Susceptibility to Ischemic Stroke and Associate with Atherothrombotic Events After Stroke in Chinese

To evaluate the associations between four single-nucleotide polymorphisms (SNPs) in CYP4A11 and CYP4F2 and ischemic stroke (IS), and between these variants and atherothrombotic events after stroke. IS patients (n=396) and controls (n=378) were genotyped for two CYP4A11 SNPs (rs2269231 and rs9333025) and two CYP4F2 SNPs (rs2108622 and rs3093135). Patients were followed up for 12 months after the stroke for the atherothrombotic events. The frequency of the rs9333025 GG genotype was significantly higher in IS patients than in controls. Logistic regression analysis showed that the presence of rs9333025 GG in patients was associated with significantly higher risk of IS. Cox regression analysis revealed that the rs9333025 GG genotype was an independent risk factor for atherothrombotic events after stroke. The rs9333025 GG genotype increases patients' susceptibility to IS and is associated with high frequencies of atherothrombotic events in stroke patients.

Kullback-Leibler distance methods for detecting disease association with rare variants from sequencing data

Because next generation sequencing technology that can rapidly genotype most genetic variations genome, there is considerable interest in investigating the effects of rare variants on complex diseases. In this paper, we propose four Kullback-Leibler distance-based Tests (KLTs) for detecting genotypic differences between cases and controls. There are several features that set the proposed tests apart from existing ones. First, by explicitly considering and comparing the distributions of genotypes, existence of variants with opposite directional effects does not compromise the power of KLTs. Second, it is not necessary to set a threshold for rare variants as the KL definition makes it reasonable to consider rare and common variants together without worrying about the contribution from one type overshadowing the other. Third, KLTs are robust to null variants thanks to a built-in noise fighting mechanism. Finally, correlation among variants is taken into account implicitly so the KLTs work well regardless of the underlying LD structure. Through extensive simulations, we demonstrated good performance of KLTs compared to the sum of squared score test (SSU) and optimal sequence kernel association test (SKAT-O). Moreover, application to the Dallas Heart Study data illustrates the feasibility and performance of KLTs in a realistic setting.

Mitochondrial genomes and exceptional longevity in a Chinese population: the Rugao longevity study

Genetic variants of whole mitochondrial DNA (mtDNA) that predispose to exceptional longevity need to be systematically identified and appraised. Here, we conducted a case-control study with 237 exceptional longevity subjects (aged 95-107) and 444 control subjects (aged 40-69) randomly recruited from a "longevity town"-the city of Rugao in China-to investigate the effects of mtDNA variants on exceptional longevity. We sequenced the entire mtDNA genomes of the 681 subjects using a next-generation platform and employed a complete mtDNA phylogenetic analytical strategy. We identified T3394C as a candidate that counteracts longevity, and we observed a higher load of private nonsynonymous mutations in the COX1 gene predisposing to female longevity. Additionally, for the first time, we identified several variants and new subhaplogroups related to exceptional longevity. Our results provide new clues for genetic mechanisms of longevity and shed light on strategies for evaluating rare mitochondrial variants that underlie complex traits.

Multiple system atrophy: the application of genetics in understanding etiology

Classically defined phenotypically by a triad of cerebellar ataxia, parkinsonism, and autonomic dysfunction in conjunction with pyramidal signs, multiple system atrophy (MSA) is a rare and progressive neurodegenerative disease affecting an estimated 3-4 per every 100,000 individuals among adults 50-99 years of age. With a pathological hallmark of alpha-synuclein-immunoreactive glial cytoplasmic inclusions (GCIs; Papp-Lantos inclusions), MSA patients exhibit marked neurodegenerative changes in the striatonigral and/or olivopontocerebellar structures of the brain. As a member of the alpha-synucleinopathy family, which is defined by its well-demarcated alpha-synuclein-immunoreactive inclusions and aggregation, MSA's clinical presentation exhibits several overlapping features with other members including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Given the extensive fund of knowledge regarding the genetic etiology of PD revealed within the past several years, a genetic investigation of MSA is warranted. While a current genome-wide association study is underway for MSA to further clarify the role of associated genetic loci and single-nucleotide polymorphisms, several cases have presented solid preliminary evidence of a genetic etiology. Naturally, genes and variants manifesting known associations with PD (and other phenotypically similar neurodegenerative disorders), including SNCA and MAPT, have been comprehensively investigated in MSA patient cohorts. More recently variants in COQ2 have been linked to MSA in the Japanese population although this finding awaits replication. Nonetheless, significant positive associations with subsequent independent replication studies have been scarce. With very limited information regarding genetic mutations or alterations in gene dosage as a cause of MSA, the search for novel risk genes, which may be in the form of common variants or rare variants, is the logical nexus for MSA research. We believe that the application of next generation genetic methods to MSA will provide valuable insight into the underlying causes of this disease, and will be central to the identification of etiologic-based therapies.

Exome sequencing of 75 individuals from multiply affected coeliac families and large scale resequencing follow up

Coeliac disease (CeD) is a highly heritable common autoimmune disease involving chronic small intestinal inflammation in response to dietary wheat. The human leukocyte antigen (HLA) region, and 40 newer regions identified by genome wide association studies (GWAS) and dense fine mapping, account for ∼40% of the disease heritability. We hypothesized that in pedigrees with multiple individuals with CeD rare [minor allele frequency (MAF) <0.5%] mutations of larger effect size (odds ratios of ∼2-5) might exist. We sequenced the exomes of 75 coeliac individuals of European ancestry from 55 multiply affected families. We selected interesting variants and genes for further follow up using a combination of: an assessment of shared variants between related subjects, a model-free linkage test, and gene burden tests for multiple, potentially causal, variants. We next performed highly multiplexed amplicon resequencing of all RefSeq exons from 24 candidate genes selected on the basis of the exome sequencing data in 2,248 unrelated coeliac cases and 2,230 controls. 1,335 variants with a 99.9% genotyping call rate were observed in 4,478 samples, of which 939 were present in coding regions of 24 genes (Ti/Tv 2.99). 91.7% of coding variants were rare (MAF <0.5%) and 60% were novel. Gene burden tests performed on rare functional variants identified no significant associations (p<1×10(-3)) in the resequenced candidate genes. Our strategy of sequencing multiply affected families with deep follow up of candidate genes has not identified any new CeD risk mutations.

GWAS and Meta-Analysis in Aging/Longevity

Longevity is an extremely complex phenotype that is determined by environment, life style and genetics. Genome wide association studies (GWAS) have been a powerful tool to identify the genetic origin of other complex outcome with a similar heritability. In this chapter we discuss the findings all GWAS of longevity conducted to date. Various cut-off to define longevity have been used varying from 85+, 90+ and 100+ years and the impact of these difference are addressed in this chapter. The only consistent association emerging from GWAS to data is the APOE gene that has been already identified as a candidate gene. Although (GWAS) have identified biologically plausible genes and pathways, no new loci for longevity have been conclusively proven. A reason for not finding any replicated associations for longevity could be the complexity of the phenotype, although heterogeneity also underlies many other traits for which GWAS has been successful. One may argue that rare variants explain the high heritability of longevity and the segregation of the trait in families. Yet, whole genome analyses of GWAS data still suggest that over 80 % of the heritability is explained by common variants. Although findings of GWAS to date have been disappointing, there is ample opportunity to improve the statistical power of studies to find common variants with small effects. In the near future, joining of the published studies and new ones emerging may bring to surface new loci.