Common vs. rare allele hypotheses for complex diseases

Down-regulated SHARPIN may accelerate the development of atopic dermatitis through activating interleukin-33/ST2 signalling

SHARPIN is an important component of the linear ubiquitin chain assembly complex (LUBAC). Loss of function of SHARPIN results in eosinophilic inflammation in multiple organs including skin with Th₂ -dominant cytokines and dysregulated development of lymphoid tissues in mice. The clinicopathological features are similar to atopic dermatitis (AD) in humans. In order to investigate the potential role of SHARPIN in the pathogenesis of AD, we performed genetic association study of the genotypes and haplotypes as well as SHARPIN's expression between AD cases and controls. We found three mutations (g.480G>A, g.4576A>G and g.5070C>T) in patient group, and significantly decreased expression in AD lesions, suggesting a primary role of SHARPIN during AD development. Lentivirus-mediated in vitro assays identified that knockdown of SHARPIN can induce elevated expression of IL-33 and its orphan receptor ST2, FLG and STAT3 and NF-κB inactivation in HaCaT keratinocytes, which has been widely evidenced in regulating AD development. ST2 expression was highly induced in SHARPIN-silenced HaCaT keratinocytes after the combined stimulation of IL-4 and IL-13. Our in vivo and in vitro findings implicated that SHARPIN may be a novel participant in the pathogenesis and/or new therapeutic target of AD.

Meta-analysis identifies mitochondrial DNA sequence variants associated with walking speed

Declines in walking speed are associated with a variety of poor health outcomes including disability, comorbidity, and mortality. While genetic factors are putative contributors to variability in walking, few genetic loci have been identified for this trait. We examined the role of mitochondrial genomic variation on walking speed by sequencing the entire mitochondrial DNA (mtDNA). Data were meta-analyzed from 1758 Lifestyle Interventions and Independence for Elders (LIFE) Study and replication data from 730 Health, Aging, and Body Composition (HABC) Study participants with baseline walking speed information. Participants were 69+ years old of diverse racial backgrounds (African, European, and other race/ethnic groups) and had a wide range of mean walking speeds [4-6 m (0.78-1.09 m/s) and 400 m (0.83-1.24 m/s)]. Meta-analysis across studies and racial groups showed that m.12705C>T, ND5 variant was significantly associated (p < 0.0001) with walking speed at both short and long distances. Replication and meta-analysis also identified statistically significant walking speed associations (p < 0.0001) between the m.5460.G>A, ND2 and m.309C>CT, HV2 variants at short and long distances, respectively. All results remained statistically significant after multiple comparisons adjustment for 499 mtDNA variants. The m.12705C>T variant can be traced to the beginnings of human global migration and that cells carrying this variant display altered tRNA expression. Significant pooled effects related to stopping during the long-distance walk test were observed across OXPHOS complexes I (p = 0.0017) and III (p = 0.0048). These results suggest that mtDNA-encoded variants are associated with differences in walking speed among older adults, potentially identifying those at risk of developing mobility impairments.

Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits

Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.

Methods and results from the genome-wide association group at GAW20

BACKGROUND

This paper summarizes the contributions from the Genome-wide Association Study group (GWAS group) of the GAW20. The GWAS group contributions focused on topics such as association tests, phenotype imputation, and application of empirical kinships. The goals of the GWAS group contributions were varied. A real or a simulated data set based on the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study was employed by different methods. Different outcomes and covariates were considered, and quality control procedures varied throughout the contributions.

RESULTS

The consideration of heritability and family structure played a major role in some contributions. The inclusion of family information and adaptive weights based on data were found to improve power in genome-wide association studies. It was proven that gene-level approaches are more powerful than single-marker analysis. Other contributions focused on the comparison between pedigree-based kinship and empirical kinship matrices, and investigated similar results in heritability estimation, association mapping, and genomic prediction. A new approach for linkage mapping of triglyceride levels was able to identify a novel linkage signal.

CONCLUSIONS

This summary paper reports on promising statistical approaches and findings of the members of the GWAS group applied on real and simulated data which encompass the current topics of epigenetic and pharmacogenomics.

Exome sequencing-based identification of novel type 2 diabetes risk allele loci in the Qatari population

BACKGROUND

Type 2 diabetes (T2D) susceptibility is influenced by genetic and lifestyle factors. To date, the majority of genetic studies of T2D have been in populations of European and Asian descent. The focus of this study is on genetic variations underlying T2D in Qataris, a population with one of the highest incidences of T2D worldwide.

RESULTS

Illumina HiSeq exome sequencing was performed on 864 Qatari subjects (574 T2D cases, 290 controls). Sequence kernel association test (SKAT) gene-based analysis identified an association for low frequency potentially deleterious variants in 6 genes. However, these findings were not replicated by SKAT analysis in an independent cohort of 12,699 exomes, primarly due to the absence of low frequency potentially deleterious variants in 5 of the 6 genes. Interestingly one of the genes identified, catenin beta 1 (CTNNB1, β-catenin), is the key effector of the Wnt pathway and interacts with the nuclear receptor transcription factor 7-like 2 (TCF7L2), variants which are the most strongly associated with risk of developing T2D worldwide. Single variant analysis did not identify any associated variants, suggesting the SKAT association signal was not driven by individual variants. None of the 6 associated genes were among 634 previously described T2D genes.

CONCLUSIONS

The observation that genes not previously linked to T2D in prior studies of European and Asian populations are associated with T2D in Qatar provides new insights into the complexity of T2D pathogenesis and emphasizes the importance of understudied populations when assessing genetic variation in the pathogenesis of common disorders.

Gene editing in the context of an increasingly complex genome

The reporting of the first draft of the human genome in 2000 brought with it much hope for the future in what was felt as a paradigm shift toward improved health outcomes. Indeed, we have now mapped the majority of variation across human populations with landmark projects such as 1000 Genomes; in cancer, we have catalogued mutations across the primary carcinomas; whilst, for other diseases, we have identified the genetic variants with strongest association. Despite this, we are still awaiting the genetic revolution in healthcare to materialise and translate itself into the health benefits for which we had hoped. A major problem we face relates to our underestimation of the complexity of the genome, and that of biological mechanisms, generally. Fixation on DNA sequence alone and a 'rigid' mode of thinking about the genome has meant that the folding and structure of the DNA molecule -and how these relate to regulation- have been underappreciated. Projects like ENCODE have additionally taught us that regulation at the level of RNA is just as important as that at the spatiotemporal level of chromatin.In this review, we chart the course of the major advances in the biomedical sciences in the era pre- and post the release of the first draft sequence of the human genome, taking a focus on technology and how its development has influenced these. We additionally focus on gene editing via CRISPR/Cas9 as a key technique, in particular its use in the context of complex biological mechanisms. Our aim is to shift the mode of thinking about the genome to that which encompasses a greater appreciation of the folding of the DNA molecule, DNA- RNA/protein interactions, and how these regulate expression and elaborate disease mechanisms.Through the composition of our work, we recognise that technological improvement is conducive to a greater understanding of biological processes and life within the cell. We believe we now have the technology at our disposal that permits a better understanding of disease mechanisms, achievable through integrative data analyses. Finally, only with greater understanding of disease mechanisms can techniques such as gene editing be faithfully conducted.

Opportunities and Challenges for Genetic Studies of End-Stage Renal Disease in Canada

Purpose of review:

Genetic testing can improve diagnostic precision in some patients with end-stage renal disease (ESRD) providing the potential for targeted therapy and improved patient outcomes. We sought to describe the genetic architecture of ESRD and Canadian data sources available for further genetic investigation into ESRD.

Sources of information:

We performed PubMed searches of English, peer-reviewed articles using keywords “chronic kidney disease,” “ESRD,” “genetics,” “sequencing,” and “administrative databases,” and searched for nephrology-related Mendelian diseases on the Online Mendelian Inheritance in Man database.

Methods:

In this narrative review, we discuss our evolving understanding of the genetic architecture of kidney disease and ESRD, the risks and benefits of using genetic data to help diagnose and manage patients with ESRD, existing public Canadian biobanks and databases, and a vision for future genetic studies of ESRD in Canada.

Key findings:

ESRD has a polygenic architecture including rare Mendelian mutations and common small effect genetic polymorphism contributors. Genetic testing will improve diagnostic accuracy and contribute to a precision medicine approach in nephrology. However, the risk and benefits of genetic testing needs to be considered from an individual and societal perspective, and further research is required. Merging existing health data, linking biobanks and administrative databases, and forming Canadian collaborations hold great potential for genetic research into ESRD. Large sample sizes are necessary to perform the suitably powered investigations required to bring this vision to reality.

Limitations:

This is a narrative review of the literature discussing future directions and opportunities. It reflects the views and academic biases of the authors.

Implications:

National collaborations will be required to obtain sample sizes required for impactful, robust research. Merging established datasets may be one approach to obtain adequate samples. Patient education and engagement will improve the value of knowledge gained.

From lipid locus to drug target through human genomics

In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some (SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.

Stepwise approach to SNP-set analysis illustrated with the Metabochip and colorectal cancer in Japanese Americans of the Multiethnic Cohort

BACKGROUND

Common variants have explained less than the amount of heritability expected for complex diseases, which has led to interest in less-common variants and more powerful approaches to the analysis of whole-genome scans. Because of low frequency (low statistical power), less-common variants are best analyzed using SNP-set methods such as gene-set or pathway-based analyses. However, there is as yet no clear consensus regarding how to focus in on potential risk variants following set-based analyses. We used a stepwise, telescoping approach to analyze common- and rare-variant data from the Illumina Metabochip array to assess genomic association with colorectal cancer (CRC) in the Japanese sub-population of the Multiethnic Cohort (676 cases, 7180 controls). We started with pathway analysis of SNPs that are in genes and pathways having known mechanistic roles in colorectal cancer, then focused on genes within the pathways that evidenced association with CRC, and finally assessed individual SNPs within the genes that evidenced association. Pathway SNPs downloaded from the dbSNP database were cross-matched with Metabochip SNPs and analyzed using the logistic kernel machine regression approach (logistic SNP-set kernel-machine association test, or sequence kernel association test; SKAT) and related methods.

RESULTS

The TGF-β and WNT pathways were associated with all CRC, and the WNT pathway was associated with colon cancer. Individual genes demonstrating the strongest associations were TGFBR2 in the TGF-β pathway and SMAD7 (which is involved in both the TGF-β and WNT pathways). As partial validation of our approach, a known CRC risk variant in SMAD7 (in both the TGF-β and WNT pathways: rs11874392) was associated with CRC risk in our data. We also detected two novel candidate CRC risk variants (rs13075948 and rs17025857) in TGFBR2, a gene known to be associated with CRC risk.

CONCLUSIONS

A stepwise, telescoping approach identified some potentially novel risk variants associated with colorectal cancer, so it may be a useful method for following up on results of set-based SNP analyses. Further work is required to assess the statistical characteristics of the approach, and additional applications should aid in better clarifying its utility.

Novel Methods for Family-Based Genetic Studies

The recent development of microarray and sequencing technology allows identification of disease susceptibility genes. Although the genome-wide association studies (GWAS) have successfully identified many genetic markers related to human diseases, the traditional statistical methods are not powerful to detect rare genetic markers. The rare genetic markers are usually grouped together and tested at the set level. One of such methods is the sequence kernel association test (SKAT), which has been commonly used in the rare genetic marker analysis. In recent publications, SKAT has been extended to be applicable for family-based rare variant analysis. Here, I present three published statistical approaches for family-based rare variant analysis for: 1. continuous traits, 2. binary traits, and 3. multiple correlated traits.

Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family

BACKGROUND

Multiple sclerosis (MS) is a complex disease resulting from the joint effect of many genes. It has been speculated that rare variants might explain part of the missing heritability of MS.

OBJECTIVE

To identify rare coding genetic variants by analyzing a large MS pedigree with 11 affected individuals in several generations.

METHODS

Genome-wide linkage screen and whole exome sequencing (WES) were performed to identify novel coding variants in the shared region(s) and in the known 110 MS risk loci. The candidate variants were then assessed in 591 MS patients and 3169 controls.

RESULTS

Suggestive evidence for linkage was obtained to 7q11.22-q11.23. In WES data, a rare missense variant p.R183C in FKBP6 was identified that segregated with the disease in this family. The minor allele frequency was higher in an independent cohort of MS patients than in healthy controls (1.27% vs 0.95%), but not significant (odds ratio (OR) = 1.33 (95% confidence interval (CI): 0.8-2.4), p = 0.31).

CONCLUSION

The rare missense variant in FKBP6 was identified in a large Dutch MS family segregating with the disease. This association to MS was not found in an independent MS cohort. Overall, genome-wide studies in larger cohorts are needed to adequately investigate the role of rare variants in MS risk.

Using Gene Genealogies to Localize Rare Variants Associated with Complex Traits in Diploid Populations

BACKGROUND AND AIMS

Many methods can detect trait association with causal variants in candidate genomic regions; however, a comparison of their ability to localize causal variants is lacking. We extend a previous study of the detection abilities of these methods to a comparison of their localization abilities.

METHODS

Through coalescent simulation, we compare several popular association methods. Cases and controls are sampled from a diploid population to mimic human studies. As benchmarks for comparison, we include two methods that cluster phenotypes on the true genealogical trees: a naive Mantel test considered previously in haploid populations and an extension that takes into account whether case haplotypes carry a causal variant. We first work through a simulated dataset to illustrate the methods. We then perform a simulation study to score the localization and detection properties.

RESULTS

In our simulations, the association signal was localized least precisely by the naive Mantel test and most precisely by its extension. Most other approaches had intermediate performance similar to the single-variant Fisher exact test.

CONCLUSIONS

Our results confirm earlier findings in haploid populations about potential gains in performance from genealogy-based approaches. They also highlight differences between haploid and diploid populations when localizing and detecting causal variants.

Identification of non-HLA genes associated with development of islet autoimmunity and type 1 diabetes in the prospective TEDDY cohort

Traditional linkage analysis and genome-wide association studies have identified HLA and a number of non-HLA genes as genetic factors for islet autoimmunity (IA) and type 1 diabetes (T1D). However, the relative risk associated with previously identified non-HLA genes is usually very small as measured in cases/controls from mixed populations. Genetic associations for IA and T1D may be more accurately assessed in prospective cohorts. In this study, 5806 subjects from the TEDDY (The Environmental Determinants of Diabetes in the Young) study, an international prospective cohort study, were genotyped for 176,586 SNPs on the ImmunoChip. Cox proportional hazards analyses were performed to discover the SNPs associated with the risk for IA, T1D, or both. Three regions were associated with the risk of developing any persistent confirmed islet autoantibody: one known region near SH2B3 (HR = 1.35, p = 3.58 × 10-7) with Bonferroni-corrected significance and another known region near PTPN22 (HR = 1.46, p = 2.17 × 10-6) and one novel region near PPIL2 (HR = 2.47, p = 9.64 × 10-7) with suggestive evidence (p < 10-5). Two known regions (PTPN22: p = 2.25 × 10-6, INS; p = 1.32 × 10-7) and one novel region (PXK/PDHB: p = 8.99 × 10-6) were associated with the risk for multiple islet autoantibodies. First appearing islet autoantibodies differ with respect to association. Two regions (INS: p = 5.67 × 10-6 and TTC34/PRDM16: 6.45 × 10-6) were associated if the fist appearing autoantibody was IAA and one region (RBFOX1: p = 8.02 × 10-6) was associated if the first appearing autoantibody was GADA. The analysis of T1D identified one region already known to be associated with T1D (INS: p = 3.13 × 10-7) and three novel regions (RNASET2, PLEKHA1, and PPIL2; 5.42 × 10-6 > p > 2.31 × 10-6). These results suggest that a number of low frequency variants influence the risk of developing IA and/or T1D and these variants can be identified by large prospective cohort studies using a survival analysis approach.

Whole exome sequencing reveals rare variants linked to congenital pouch colon

We demonstrate the application of whole exome sequencing to discover the rare variants for congenital pouch colon, acronymed CPC. For 18 affected individuals in a total of 64 samples, we sequenced coding regions to a mean coverage of 100×. A sufficient depth of ca. 94% of targeted exomes was achieved. Filtering against the public SNP/variant repositories, we identified a host of candidate genes, EPB41L4A and CTC1 associated with colon, neural/brain muscles and Dyskeratosis Congenita maladies. Furthermore, the stop gain mutations in the form of JAG1,OR5AR1,SLC22A24,PEX16,TSPAN32,TAF1B,MAP2K3 and SLC25A19 appears to be localized to Chromosomes 2, 11, 17 and 20 in addition to the three stop lost mutants across three genes, viz. OAS2, GBA3 and PKD1L2 affecting the colon tissue. While our results have paved way for transcendence of monogenic traits in identifying the genes underlying rare genetic disorders, it will provide helpful clues for further investigating genetic factors associated with anorectal anomalies, particularly CPC.

Genetic susceptibility to neuroblastoma: current knowledge and future directions

Neuroblastoma, a malignancy of the developing peripheral nervous system that affects infants and young children, is a complex genetic disease. Over the past two decades, significant progress has been made toward understanding the genetic determinants that predispose to this often lethal childhood cancer. Approximately 1-2% of neuroblastomas are inherited in an autosomal dominant fashion and a combination of co-morbidity and linkage studies has led to the identification of germline mutations in PHOX2B and ALK as the major genetic contributors to this familial neuroblastoma subset. The genetic basis of "sporadic" neuroblastoma is being studied through a large genome-wide association study (GWAS). These efforts have led to the discovery of many common susceptibility alleles, each with modest effect size, associated with the development and progression of sporadic neuroblastoma. More recently, next-generation sequencing efforts have expanded the list of potential neuroblastoma-predisposing mutations to include rare germline variants with a predicted larger effect size. The evolving characterization of neuroblastoma's genetic basis has led to a deeper understanding of the molecular events driving tumorigenesis, more precise risk stratification and prognostics and novel therapeutic strategies. This review details the contemporary understanding of neuroblastoma's genetic predisposition, including recent advances and discusses ongoing efforts to address gaps in our knowledge regarding this malignancy's complex genetic underpinnings.

Another Round of "Clue" to Uncover the Mystery of Complex Traits

A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.

Family studies to find rare high risk variants in migraine

INTRODUCTION

Migraine has long been known as a common complex disease caused by genetic and environmental factors. The pathophysiology and the specific genetic susceptibility are poorly understood. Common variants only explain a small part of the heritability of migraine. It is thought that rare genetic variants with bigger effect size may be involved in the disease. Since migraine has a tendency to cluster in families, a family approach might be the way to find these variants. This is also indicated by identification of migraine-associated loci in classical linkage-analyses in migraine families. A single migraine study using a candidate-gene approach was performed in 2010 identifying a rare mutation in the TRESK potassium channel segregating in a large family with migraine with aura, but this finding has later become questioned. The technologies of next-generation sequencing (NGS) now provides an affordable tool to investigate the genetic variation in the entire exome or genome. The family-based study design using NGS is described in this paper. We also review family studies using NGS that have been successful in finding rare variants in other common complex diseases in order to argue the promising application of a family approach to migraine.

METHOD

PubMed was searched to find studies that looked for rare genetic variants in common complex diseases through a family-based design using NGS, excluding studies looking for de-novo mutations, or using a candidate-gene approach and studies on cancer. All issues from Nature Genetics and PLOS genetics 2014, 2015 and 2016 (UTAI June) were screened for relevant papers. Reference lists from included and other relevant papers were also searched. For the description of the family-based study design using NGS an in-house protocol was used.

RESULTS

Thirty-two successful studies, which covered 16 different common complex diseases, were included in this paper. We also found a single migraine study. Twenty-three studies found one or a few family specific variants (less than five), while other studies found several possible variants. Not all of them were genome wide significant. Four studies performed follow-up analyses in unrelated cases and controls and calculated odds ratios that supported an association between detected variants and risk of disease. Studies of 11 diseases identified rare variants that segregated fully or to a large degree with the disease in the pedigrees.

CONCLUSION

It is possible to find rare high risk variants for common complex diseases through a family-based approach. One study using a family approach and NGS to find rare variants in migraine has already been published but with strong limitations. More studies are under way.

Association of NOS3 gene polymorphisms with essential hypertension in Sudanese patients: a case control study

BACKGROUND

Essential hypertension (EH) is influenced by various environmental and genetic factors. Nitric oxide is important for the functional integrity of the vascular endothelium and is produced in endothelial cells by the enzyme endothelial nitric oxide synthase (eNOS). EH has a strong genetic component, and the NOS3 gene, which encodes eNOS, represents an interesting candidate for contribution to the phenotype. The most clinically relevant polymorphisms in the NOS3 gene are rs1799983 in exon 7 (encoding Glu298Asp), a variable number tandem repeat (VNTR) in intron 4, and rs2070744 (T-786C) in the promoter region. This study aims to investigate the association between these three polymorphisms in the NOS3 gene and EH in Sudanese patients.

METHODS

Hypertensive patients (n = 157) > 18 years of age with established hypertension from various hospitals in Khartoum, and controls (n = 85) > 18 years of age and with blood pressure measurements <140/90, were included in this case control study. Genotypes at the NOS3 variants were determined using TaqMan and polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) analyses. Genotype and allele frequencies were compared between the two groups by χ² analysis, and differences were expressed as odds ratios with 95% confidence intervals (CIs). P values <0.05 were considered statistically significant.

RESULTS

The rs2070744 polymorphism in NOS3 was found to be associated with EH in the Sudanese population as the patients group had higher frequency of CC genotype compared with the controls (6.6% vs 6.1%, p = 0.02). Considering a dominant inheritance model, the frequency of TC + CC genotypes in patients was significantly higher than that in the control subjects (52.6% vs 34.1%, respectively; p < 0.01), with an odds ratio (95% CI) of 2.14 (1.23-3.74). In addition, the C allele was more frequent in the patients than the control group (29.6% vs 20%, p = 0.03, OR = 1.84 (1.15-2.93)). The c allele of intron 4 VNTR was reported in >1% of the Sudanese population under study.

CONCLUSION

The results of this study indicated that the rs2070744 polymorphism in NOS3 may be a genetic susceptibility factor for EH in the Sudanese population. The c allele of intron 4 VNTR is not rare in the Sudanese population.

Association detection between ordinal trait and rare variants based on adaptive combination of P values

Next-generation sequencing technology not only presents a new method for the detection of human genomic structural variation, but also provides a large number of genetic data of rare variants for us. Currently, how to detect association between human complex diseases and rare variants using genetical data has attracted extensive attention. In the field of medicine, many people's health and disease conditions are measured by ordinal response variables, namely, the trait value reflects the development stage or severity of a certain condition. However, most existing methods to test for association between rare variants and complex diseases are designed to deal with dichotomous or quantitative traits. Association analysis methods of ordinal traits are relatively fewer, and considering ordinal traits as dichotomous and quantitative traits will inevitably lose some valuable information in the original data. Therefore, in this paper, we extend an existing method of adaptive combination of P values (ADA) and propose a new method of association analysis for ordinal trait based on it (called OR-ADA) to test for possible association between ordinal trait and rare variants. In our method, we establish a cumulative logistic regression model, in which the regression coefficients are estimated by the Newton-Raphson algorithm and the likelihood ratio test is used to test the association. Through a large number of simulation studies and an example, we demonstrate the performance of the new method and compare it with several methods. The analysis results show that the OR-ADA strategy is robust to the signs of effects of causal variants and more powerful under many scenarios.

A Decade of Genetic and Metabolomic Contributions to Type 2 Diabetes Risk Prediction

PURPOSE OF REVIEW

The purpose of this review was to summarize and reflect on advances over the past decade in human genetic and metabolomic discovery with particular focus on their contributions to type 2 diabetes (T2D) risk prediction.

RECENT FINDINGS

In the past 10 years, a combination of advances in genotyping efficiency, metabolomic profiling, bioinformatics approaches, and international collaboration have moved T2D genetics and metabolomics from a state of frustration to an abundance of new knowledge. Efforts to control and prevent T2D have failed to stop this global epidemic. New approaches are needed, and although neither genetic nor metabolomic profiling yet have a clear clinical role, the rapid pace of accumulating knowledge offers the possibility for "multi-omic" prediction to improve health.

Helicobacter pylori colonization and obesity - a Mendelian randomization study

Obesity is associated with substantial morbidity, costs, and decreased life expectancy, and continues to rise worldwide. While etiological understanding is needed for prevention, epidemiological studies indicated that colonization with Helicobacter pylori (H. pylori) may affect body mass index (BMI), but with inconsistent results. Here, we examine the relationship between H. pylori colonization and BMI/obesity. Cross-sectional analyses were performed in two independent population-based cohorts of elderly from the Netherlands and Germany (n = 13,044). Genetic risk scores were conducted based on genetic loci associated with either H. pylori colonization or BMI/obesity. We performed a bi-directional Mendelian randomization. Meta-analysis of cross-sectional data revealed no association between anti-H. pylori IgG titer and BMI, nor of H. pylori positivity and BMI. Anti-H. pylori IgG titer was negatively associated with obesity (OR 0.99972; 95% CI 0.99946-0.99997, p = 0.03) and with obesity classes (Beta −6.91 •10⁻⁵; 95% CI −1.38•10⁻⁴, −5.49•10⁻⁷, p = 0.048), but the magnitude of these effects was limited. Mendelian randomization showed no causal relation between H. pylori genetic risk score and BMI/obesity, nor between BMI or obesity genetic risk scores and H. pylori positivity. This study provides no evidence for a clinically relevant association between H. pylori and BMI/obesity.

Illustrating, Quantifying, and Correcting for Bias in Post-hoc Analysis of Gene-Based Rare Variant Tests of Association

To date, gene-based rare variant testing approaches have focused on aggregating information across sets of variants to maximize statistical power in identifying genes showing significant association with diseases. Beyond identifying genes that are associated with diseases, the identification of causal variant(s) in those genes and estimation of their effect is crucial for planning replication studies and characterizing the genetic architecture of the locus. However, we illustrate that straightforward single-marker association statistics can suffer from substantial bias introduced by conditioning on gene-based test significance, due to the phenomenon often referred to as "winner's curse." We illustrate the ramifications of this bias on variant effect size estimation and variant prioritization/ranking approaches, outline parameters of genetic architecture that affect this bias, and propose a bootstrap resampling method to correct for this bias. We find that our correction method significantly reduces the bias due to winner's curse (average two-fold decrease in bias, p < 2.2 × 10^-6) and, consequently, substantially improves mean squared error and variant prioritization/ranking. The method is particularly helpful in adjustment for winner's curse effects when the initial gene-based test has low power and for relatively more common, non-causal variants. Adjustment for winner's curse is recommended for all post-hoc estimation and ranking of variants after a gene-based test. Further work is necessary to continue seeking ways to reduce bias and improve inference in post-hoc analysis of gene-based tests under a wide variety of genetic architectures.

The Empirical Distribution of Singletons for Geographic Samples of DNA Sequences

Rare variants are important for drawing inference about past demographic events in a species history. A singleton is a rare variant for which genetic variation is carried by a unique chromosome in a sample. How singletons are distributed across geographic space provides a local measure of genetic diversity that can be measured at the individual level. Here, we define the empirical distribution of singletons in a sample of chromosomes as the proportion of the total number of singletons that each chromosome carries, and we present a theoretical background for studying this distribution. Next, we use computer simulations to evaluate the potential for the empirical distribution of singletons to provide a description of genetic diversity across geographic space. In a Bayesian framework, we show that the empirical distribution of singletons leads to accurate estimates of the geographic origin of range expansions. We apply the Bayesian approach to estimating the origin of the cultivated plant species Pennisetum glaucum [L.] R. Br. (pearl millet) in Africa, and find support for range expansion having started from Northern Mali. Overall, we report that the empirical distribution of singletons is a useful measure to analyze results of sequencing projects based on large scale sampling of individuals across geographic space.

Concepts, estimation and interpretation of SNP-based heritability

Narrow-sense heritability (h²) is an important genetic parameter that quantifies the proportion of phenotypic variance in a trait attributable to the additive genetic variation generated by all causal variants. Estimation of h² previously relied on closely related individuals, but recent developments allow estimation of the variance explained by all SNPs used in a genome-wide association study (GWAS) in conventionally unrelated individuals, that is, the SNP-based heritability (). In this Perspective, we discuss recently developed methods to estimate for a complex trait (and genetic correlation between traits) using individual-level or summary GWAS data. We discuss issues that could influence the accuracy of , definitions, assumptions and interpretations of the models, and pitfalls of misusing the methods and misinterpreting the models and results.

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer's disease in a three-stage case-control study of 85,133 subjects. In stage 1, we genotyped 34,174 samples using a whole-exome microarray. In stage 2, we tested associated variants (P < 1 × 10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, we used an additional 14,997 samples to test the most significant stage 2 associations (P < 5 × 10-8) using imputed genotypes. We observed three new genome-wide significant nonsynonymous variants associated with Alzheimer's disease: a protective variant in PLCG2 (rs72824905: p.Pro522Arg, P = 5.38 × 10-10, odds ratio (OR) = 0.68, minor allele frequency (MAF)cases = 0.0059, MAFcontrols = 0.0093), a risk variant in ABI3 (rs616338: p.Ser209Phe, P = 4.56 × 10-10, OR = 1.43, MAFcases = 0.011, MAFcontrols = 0.008), and a new genome-wide significant variant in TREM2 (rs143332484: p.Arg62His, P = 1.55 × 10-14, OR = 1.67, MAFcases = 0.0143, MAFcontrols = 0.0089), a known susceptibility gene for Alzheimer's disease. These protein-altering changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified risk genes in Alzheimer's disease. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to the development of Alzheimer's disease.

Multiple common and rare variants of ABCG2 cause gout

Objective

Previous studies have suggested an association between gout susceptibility and common dysfunctional variants in ATP-binding cassette transporter subfamily G member 2/breast cancer resistance protein (ABCG2/BCRP), including rs72552713 (Q126X) and rs2231142 (Q141K). However, the association of rare ABCG2 variants with gout is unknown. Therefore, we investigated the effects of rare ABCG2 variants on gout susceptibility in this study.

Methods

We sequenced the exons of ABCG2 in 480 patients with gout and 480 healthy controls (Japanese males). We also performed functional analyses of non-synonymous variants of ABCG2 and analysed the correlation between urate transport function and scores from the protein prediction algorithms (Sorting Intolerant from Tolerant (SIFT) and Polymorphism Phenotyping v2 (PolyPhen-2)). Stratified association analyses and multivariate logistic regression analysis were performed to evaluate the effects of rare and common ABCG2 variants on gout susceptibility.

Results

We identified 3 common and 19 rare non-synonymous variants of ABCG2. SIFT scores were significantly correlated with the urate transport function, although some ABCG2 variants showed inconsistent scores. When the effects of common variants were removed by stratified association analysis, the rare variants of ABCG2 were associated with a significantly increased risk of gout (OR=3.2, p=6.4×10⁻³). Multivariate logistic regression analysis revealed that the size effect of these rare ABCG2 variants (OR=2.7, p=3.0×10⁻³) was similar to that of the common variants, Q126X (OR=3.4, p=3.2×10⁻⁶) and Q141K (OR=2.3, p=2.7×10⁻¹⁶).

Conclusions

This study revealed that multiple common and rare variants of ABCG2 are independently associated with gout. These results could support both the ‘Common Disease, Common Variant’ and ‘Common Disease, Multiple Rare Variant’ hypotheses for the association between ABCG2 and gout susceptibility.

Human Missense Mutations in Regulator of G Protein Signaling 2 Affect the Protein Function Through Multiple Mechanisms

Regulator of G protein signaling 2 (RGS2) plays a significant role in alleviating vascular contraction and promoting vascular relaxation due to its GTPase accelerating protein activity toward Gαq. Mice lacking RGS2 display a hypertensive phenotype, and several RGS2 missense mutations have been found predominantly in hypertensive human subjects. However, the mechanisms whereby these mutations could impact blood pressure is unknown. Here, we selected 16 rare, missense mutations in RGS2 identified in various human exome sequencing projects and evaluated their ability to inhibit intracellular calcium release mediated by angiotensin II receptor type 1 (AT1R). Four of them had reduced function and were further investigated to elucidate underlying mechanisms. Low protein expression, protein mislocalization, and reduced G protein binding were identified as likely mechanisms of the malfunctioning mutants. The Q2L mutant had 50% lower RGS2 than wild-type (WT) protein detected by Western blot. Confocal microscopy demonstrated that R44H and D40Y had impaired plasma membrane targeting; only 46% and 35% of those proteins translocated to the plasma membrane when coexpressed with Gα_q Q209L compared with 67% for WT RGS2. The R188H mutant had a significant reduction in Gα_q binding affinity (10-fold increase in K_i compared with WT RGS2 in a flow cytometry competition binding assay). This study provides functional data for 16 human RGS2 missense variants on their effects on AT1R-mediated calcium mobilization and provides molecular understanding of those variants with functional loss in vitro. These molecular behaviors can provide insight to inform antihypertensive therapeutics in individuals with variants having reduced function.

Interactions among Variants in Eicosanoid Genes Increase Risk of Atherothrombotic Stroke in Chinese Populations

BACKGROUND

Eicosanoids are lipid mediators that may play a role in ischemic stroke (IS). However, the association of variants in eicosanoid genes and these interactions with IS risk has not been investigated. The aim of the present study was to investigate the association of 11 variants in eicosanoid genes with IS and to determine whether these gene-gene interactions increase the risk of IS.

METHODS

Eleven variants in prostaglandin H synthase-1 (PTGS1), PTGS2, thromboxane A2 synthase (TBXAS1), prostacyclin synthase (PTGIS), and prostaglandin E synthase (PTGES) genes were examined using mass spectrometry method in 297 patients with atherothrombotic stroke and 291 controls. Gene-gene interactions were analyzed using generalized multifactor dimensionality reduction (GMDR) method. Platelet aggregation and platelet-leukocyte aggregates were measured on admission.

RESULTS

There were no significant differences in the genotype distributions of the 11 variants between patients and controls. However, GMDR analysis showed a significant gene-gene interaction among rs20417, rs5602, and rs41708, which scored 10 for cross-validation consistency and 9 for the sign test (P = .014). Logistic regression analysis showed that high-risk interaction among rs20417, rs5602, and rs41708 was an independent risk factor for atherothrombotic stroke (OR = 2.45, 95% CI: 1.33-3.27, P = .019). The high-risk interactive genotypes were associated with higher platelet aggregation and platelet-leukocyte aggregates.

CONCLUSIONS

PTGS2 rs20417, PTGIS rs5602, and TBXAS1 rs41708 three-locus interactions may confer a higher risk for atherothrombotic stroke. The combinatorial analysis used in this study may be helpful to elucidate complex genetic risk for IS.

Genome-wide association study of treatment response to venlafaxine XR in generalized anxiety disorder

We conducted the first genome-wide association study (GWAS) in Generalized Anxiety Disorder (GAD) to identify potential predictors of venlafaxine XR treatment outcome. Ninety-eight European American patients participated in a venlafaxine XR clinical trial for GAD, with Hamilton Anxiety Scale (HAM-A) response/remission at 24 weeks as the primary outcome measure. All participants were genotyped with the Illumina PsychChip, and 266,820 common single nucleotide polymorphisms (SNPs) were analyzed. Although no SNPs reached genome-wide significance, 8 SNPs were marginally associated with treatment response/remission and HAM-A reduction at week 12 and 24 (p<0.00001). Several identified genes may indicate markers crossing neuropsychiatric diagnostic categories.

Reproductive fitness and genetic risk of psychiatric disorders in the general population

The persistence of common, heritable psychiatric disorders that reduce reproductive fitness is an evolutionary paradox. Here, we investigate the selection pressures on sequence variants that predispose to schizophrenia, autism, bipolar disorder, major depression and attention deficit hyperactivity disorder (ADHD) using genomic data from 150,656 Icelanders, excluding those diagnosed with these psychiatric diseases. Polygenic risk of autism and ADHD is associated with number of children. Higher polygenic risk of autism is associated with fewer children and older age at first child whereas higher polygenic risk of ADHD is associated with having more children. We find no evidence for a selective advantage of a high polygenic risk of schizophrenia or bipolar disorder. Rare copy-number variants conferring moderate to high risk of psychiatric illness are associated with having fewer children and are under stronger negative selection pressure than common sequence variants.

Why genetic variants that confer risk for psychiatric disorders persist in the genome is an evolutionary conundrum. Here, Mullins et al. report association of polygenic risk for autism with having fewer children and polygenic risk for ADHD with higher reproductive fitness.

Novel candidate genes may be possible predisposing factors revealed by whole exome sequencing in familial esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma is one of the deadliest of all the cancers. Its metastatic properties portend poor prognosis and high rate of recurrence. A more advanced method to identify new molecular biomarkers predicting disease prognosis can be whole exome sequencing. Here, we report the most effective genetic variants of the Notch signaling pathway in esophageal squamous cell carcinoma susceptibility by whole exome sequencing. We analyzed nine probands in unrelated familial esophageal squamous cell carcinoma pedigrees to identify candidate genes. Genomic DNA was extracted and whole exome sequencing performed to generate information about genetic variants in the coding regions. Bioinformatics software applications were utilized to exploit statistical algorithms to demonstrate protein structure and variants conservation. Polymorphic regions were excluded by false-positive investigations. Gene-gene interactions were analyzed for Notch signaling pathway candidates. We identified novel and damaging variants of the Notch signaling pathway through extensive pathway-oriented filtering and functional predictions, which led to the study of 27 candidate novel mutations in all nine patients. Detection of the trinucleotide repeat containing 6B gene mutation (a slice site alteration) in five of the nine probands, but not in any of the healthy samples, suggested that it may be a susceptibility factor for familial esophageal squamous cell carcinoma. Noticeably, 8 of 27 novel candidate gene mutations (e.g. epidermal growth factor, signal transducer and activator of transcription 3, MET) act in a cascade leading to cell survival and proliferation. Our results suggest that the trinucleotide repeat containing 6B mutation may be a candidate predisposing gene in esophageal squamous cell carcinoma. In addition, some of the Notch signaling pathway genetic mutations may act as key contributors to esophageal squamous cell carcinoma.

Base Excision Repair Variants in Cancer

Base excision repair (BER) is a key genome maintenance pathway that removes endogenously damaged DNA bases that arise in cells at very high levels on a daily basis. Failure to remove these damaged DNA bases leads to increased levels of mutagenesis and chromosomal instability, which have the potential to drive carcinogenesis. Next-generation sequencing of the germline and tumor genomes of thousands of individuals has uncovered many rare mutations in BER genes. Given that BER is critical for genome maintenance, it is important to determine whether BER genomic variants have functional phenotypes. In this chapter, we present our in silico methods for the identification and prioritization of BER variants for further study. We also provide detailed instructions and commentary on the initial cellular assays we employ to dissect potentially important phenotypes of human BER variants and highlight the strengths and weaknesses of our approaches. BER variants possessing interesting functional phenotypes can then be studied in more detail to provide important mechanistic insights regarding the role of aberrant BER in carcinogenesis.

The Weighting is the Hardest Part: On the Behavior of the Likelihood Ratio Test and the Score Test Under a Data-Driven Weighting Scheme in Sequenced Samples

Sequence-based association studies are at a critical inflexion point with the increasing availability of exome-sequencing data. A popular test of association is the sequence kernel association test (SKAT). Weights are embedded within SKAT to reflect the hypothesized contribution of the variants to the trait variance. Because the true weights are generally unknown, and so are subject to misspecification, we examined the efficiency of a data-driven weighting scheme. We propose the use of a set of theoretically defensible weighting schemes, of which, we assume, the one that gives the largest test statistic is likely to capture best the allele frequency-functional effect relationship. We show that the use of alternative weights obviates the need to impose arbitrary frequency thresholds. As both the score test and the likelihood ratio test (LRT) may be used in this context, and may differ in power, we characterize the behavior of both tests. The two tests have equal power, if the weights in the set included weights resembling the correct ones. However, if the weights are badly specified, the LRT shows superior power (due to its robustness to misspecification). With this data-driven weighting procedure the LRT detected significant signal in genes located in regions already confirmed as associated with schizophrenia - the PRRC2A (p = 1.020e-06) and the VARS2 (p = 2.383e-06) - in the Swedish schizophrenia case-control cohort of 11,040 individuals with exome-sequencing data. The score test is currently preferred for its computational efficiency and power. Indeed, assuming correct specification, in some circumstances, the score test is the most powerful test. However, LRT has the advantageous properties of being generally more robust and more powerful under weight misspecification. This is an important result given that, arguably, misspecified models are likely to be the rule rather than the exception in weighting-based approaches.

gsSKAT: Rapid gene set analysis and multiple testing correction for rare-variant association studies using weighted linear kernels

Next-generation sequencing technologies have afforded unprecedented characterization of low-frequency and rare genetic variation. Due to low power for single-variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernel-machine regression and adaptive testing methods for aggregative rare-variant association testing have been demonstrated to be powerful approaches for pathway-level analysis, although these methods tend to be computationally intensive at high-variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rare-variant analysis using component gene-level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for family-wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two case-control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide open-source R code for public use to facilitate easy application of our methods to existing rare-variant analysis results.

Genetic alterations in necrotizing enterocolitis

An underlying genetic predisposition to necrotizing enterocolitis (NEC) is increasingly being recognized. Candidate gene or pathway approaches as well as genome-wide approaches are beginning to identify potential pathogenic variants for NEC in premature infants. However, a majority of these studies have not yielded definitive results because of limited sample size and lack of validation. Despite these challenges, understanding the contribution of genetic variation to NEC is important for providing new insights into the pathogenesis of NEC as well as allowing for targeted care of infants with inherent susceptibility. In this review we provide a summary of published genetic association studies in NEC along with defining the challenges and possible future approaches.

Gene-Environment Interactions in Preventive Medicine: Current Status and Expectations for the Future

The progression of many common disorders involves a complex interplay of multiple factors, including numerous different genes and environmental factors. Gene-environmental cohort studies focus on the identification of risk factors that cannot be discovered by conventional epidemiological methodologies. Such epidemiological methodologies preclude precise predictions, because the exact risk factors can be revealed only after detailed analyses of the interactions among multiple factors, that is, between genes and environmental factors. To date, these cohort studies have reported some promising results. However, the findings do not yet have sufficient clinical significance for the development of precise, personalized preventive medicine. Especially, some promising preliminary studies have been conducted in terms of the prevention of obesity. Large-scale validation studies of those preliminary studies, using a prospective cohort design and long follow-ups, will produce useful and practical evidence for the development of preventive medicine in the future.

Genomics era and complex disorders: Implications of GWAS with special reference to coronary artery disease, type 2 diabetes mellitus, and cancers

The Human Genome Project (HGP) has identified millions of single nucleotide polymorphisms (SNPs) and their association with several diseases, apart from successfully characterizing the Mendelian/monogenic diseases. However, the dissection of precise etiology of complex genetic disorders still poses a challenge for human geneticists. This review outlines the landmark results of genome-wide association studies (GWAS) with respect to major complex diseases - Coronary artery disease (CAD), type 2 diabetes mellitus (T2DM), and predominant cancers. A brief account on the current Indian scenario is also given. All the relevant publications till mid-2015 were accessed through web databases such as PubMed and Google. Several databases providing genetic information related to these diseases were tabulated and in particular, the list of the most significant SNPs identified through GWAS was made, which may be useful for designing studies in functional validation. Post-GWAS implications and emerging concepts such as epigenomics and pharmacogenomics were also discussed.

A model-free test for detecting disease association signals with multiple genetic variants and covariates

Discoveries and analyses of genetic variants at a gene or exome based on high-throughput sequencing technology are increasingly feasible. Although many well-known association tests have already been proposed in literature for testing whether a group of variants in a target region is associated with a disease of interest, however, the analytic challenges still remain profound. The power performance of these tests generally depends on the sample size, numbers of causal and neutral variants, variant frequency, effect size, and direction. Some of these factors are not easily controllable in practical applications. Further complications arise from missing genotype, population stratification or misspecification of the working model. Previous studies showed that many model-based tests might create false positive results or decrease power when there was population stratification effect or missing genotype and simple imputation was used. Here, we demonstrate by simulations that type I errors of the well-known model-based tests are often inflated as well, even the working model deviates slightly from the true model. We propose a model-free test and show this test to be almost uniformly most powerful among the competing tests under very general simulation conditions with covariates. This test does not require genotype data to be complete and hence difficult imputation can be avoided. We also discuss how to adjust for the effect of population stratification based on principal components, and use a Shanghai Breast Cancer Study to demonstrate application of the new test.

The continuum of causality in human genetic disorders

Studies of human genetic disorders have traditionally followed a reductionist paradigm. Traits are defined as Mendelian or complex based on family pedigree and population data, whereas alleles are deemed rare, common, benign, or deleterious based on their population frequencies. The availability of exome and genome data, as well as gene and allele discovery for various conditions, is beginning to challenge classic definitions of genetic causality. Here, I discuss recent advances in our understanding of the overlap between rare and complex diseases and the context-dependent effect of both rare and common alleles that underscores the need for revising the traditional categorizations of genetic traits.

Understanding rare and common diseases in the context of human evolution

The wealth of available genetic information is allowing the reconstruction of human demographic and adaptive history. Demography and purifying selection affect the purge of rare, deleterious mutations from the human population, whereas positive and balancing selection can increase the frequency of advantageous variants, improving survival and reproduction in specific environmental conditions. In this review, I discuss how theoretical and empirical population genetics studies, using both modern and ancient DNA data, are a powerful tool for obtaining new insight into the genetic basis of severe disorders and complex disease phenotypes, rare and common, focusing particularly on infectious disease risk.

Detecting multiple variants associated with disease based on sequencing data of case-parent trios

With the advance of next-generation sequencing technology, the rare variants join the common ones in explaining more proportions of heritability. The coexistence of variants of common with rare, causal with neutral and deleterious with protective is a norm and should be appropriately addressed. Some existing methods suffer from low power when one or more forms of coexistence present, impeding their applications in practice. In this paper, for case-parent trios, pseudocontrols are constructed using the nontransmitted alleles of the parents. The Kullback-Leibler divergence is utilized to measure the difference between the distributions of variants in a genetic region for the affected children and pseudocontrols, and two nonparametric test statistics KLTT and cKLTT are proposed. Extensive simulations show that they are robust to the opposite directions of the causal variants and the amount of neutral variants, and have superiority over the existing methods when both rare and common variants are involved. Furthermore, their efficiency is demonstrated in the application to the data from Framingham Heart Study.

Common Genetic Variants Found in HLA and KIR Immune Genes in Autism Spectrum Disorder

The "common variant-common disease" hypothesis was proposed to explain diseases with strong inheritance. This model suggests that a genetic disease is the result of the combination of several common genetic variants. Common genetic variants are described as a 5% frequency differential between diseased vs. matched control populations. This theory was recently supported by an epidemiology paper stating that about 50% of genetic risk for autism resides in common variants. However, rare variants, rather than common variants, have been found in numerous genome wide genetic studies and many have concluded that the "common variant-common disease" hypothesis is incorrect. One interpretation is that rare variants are major contributors to genetic diseases and autism involves the interaction of many rare variants, especially in the brain. It is obvious there is much yet to be learned about autism genetics. Evidence has been mounting over the years indicating immune involvement in autism, particularly the HLA genes on chromosome 6 and KIR genes on chromosome 19. These two large multigene complexes have important immune functions and have been shown to interact to eliminate unwanted virally infected and malignant cells. HLA proteins have important functions in antigen presentation in adaptive immunity and specific epitopes on HLA class I proteins act as cognate ligands for KIR receptors in innate immunity. Data suggests that HLA alleles and KIR activating genes/haplotypes are common variants in different autism populations. For example, class I allele (HLA-A2 and HLA-G 14 bp-indel) frequencies are significantly increased by more than 5% over control populations (Table 2). The HLA-DR4 Class II and shared epitope frequencies are significantly above the control populations (Table 2). Three activating KIR genes: 3DS1, 2DS1, and 2DS2 have increased frequencies of 15, 22, and 14% in autism populations, respectively. There is a 6% increase in total activating KIR genes in autism over control subjects. And, more importantly there is a 12% increase in activating KIR genes and their cognate HLA alleles over control populations (Torres et al., 2012a). These data suggest the interaction of HLA ligand/KIR receptor pairs encoded on two different chromosomes is more significant as a ligand/receptor complex than separately in autism.

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

Rare mutations and hypermethylation of the ARC gene associated with schizophrenia

Activity-regulated cytoskeleton-associated protein (ARC), which interacts with the N-methyl-d-aspartate receptor (NMDAR) complex, is a critical effector molecule downstream of multiple neuronal signaling pathways. Dysregulation of the ARC/NMDAR complex can disrupt learning, memory, and normal brain functions. This study examined the role of ARC in susceptibility to schizophrenia. We used a resequencing strategy to identify the variants of ARC in 1078 subjects, including patients with schizophrenia and normal controls. We identified 16 known SNPs and 27 rare mutations. SNP-based analysis showed no association of ARC with schizophrenia. In addition, the rare mutations did not increase the burden in patients compared with controls. However, one patient-specific allele in the putative ARC promoter region and seven patient-specific mutants in ARC exon regions significantly reduced the reporter gene activity compared with ARC wild-type. Methylation of a putative ARC promoter attenuated reporter activity in vitro, suggesting that ARC expression is regulated by DNA methylation. Pyrosequencing revealed eight hypermethylated CpG sites in the putative ARC promoter region in 64 schizophrenic patients compared with 63 controls. Taken together, our results suggest that both rare variants and epigenetic regulation of ARC contribute to the pathogenesis of schizophrenia in some patients.