Exploring the unknown: assumptions about allelic architecture and strategies for susceptibility variant discovery

Genetic Basis of Chronotype in Humans: Insights From Three Landmark GWAS

Study Objectives

Chronotype, or diurnal preference, refers to behavioral manifestations of the endogenous circadian system that governs preferred timing of sleep and wake. As variations in circadian timing and system perturbations are linked to disease development, the fundamental biology of chronotype has received attention for its role in the regulation and dysregulation of sleep and related illnesses. Family studies indicate that chronotype is a heritable trait, thus directing attention toward its genetic basis. Although discoveries from molecular studies of candidate genes have shed light onto its genetic architecture, the contribution of genetic variation to chronotype has remained unclear with few related variants identified. In the advent of large-scale genome-wide association studies (GWAS), scientists now have the ability to discover novel common genetic variants associated with complex phenotypes. Three recent large-scale GWASs of chronotype were conducted on subjects of European ancestry from the 23andMe cohort and the UK Biobank. This review discusses the findings of these landmark GWASs in the context of prior research.

Methods

We systematically reviewed and compared methodological and analytical approaches and results across the three GWASs of chronotype.

Results

A good deal of consistency was observed across studies with 9 genes identified in 2 of the 3 GWASs. Several genes previously unknown to influence chronotype were identified.

Conclusions

GWAS is an important tool in identifying common variants associated with the complex chronotype phenotype, the findings of which can supplement and guide molecular science. Future directions in model systems and discovery of rare variants are discussed.

Rare Copy Number Variants Identified Suggest the Regulating Pathways in Hypertension-Related Left Ventricular Hypertrophy

Left ventricular hypertrophy (LVH) is an independent risk factor for cardiovascular morbidity and mortality, and a powerful predictor of adverse cardiovascular outcomes in the hypertensive patients. It has complex multifactorial and polygenic basis for its pathogenesis. We hypothesized that rare copy number variants (CNVs) contribute to the LVH pathogenesis in hypertensive patients. Copy number variants (CNV) were identified in 258 hypertensive patients, 95 of whom had LVH, after genotyping with a high resolution SNP array. Following stringent filtering criteria, we identified 208 rare, or private CNVs that were only present in our patients with hypertension related LVH. Preliminary findings from Gene Ontology and pathway analysis of this study confirmed the involvement of the genes known to be functionally involved in cardiac development and phenotypes, in line with previously reported transcriptomic studies. Network enrichment analyses suggested that the gene-set was, directly or indirectly, involved in the transcription factors regulating the "foetal cardiac gene programme" which triggered the hypertrophic cascade, confirming previous reports. These findings suggest that multiple, individually rare copy number variants altering genes may contribute to the pathogenesis of hypertension-related LVH. In summary, we have provided further supporting evidence that rare CNV could potentially impact this common and complex disease susceptibility with lower heritability.

A case study: semantic integration of gene-disease associations for type 2 diabetes mellitus from literature and biomedical data resources

In the Semantic Enrichment of the Scientific Literature (SESL) project, researchers from academia and from life science and publishing companies collaborated in a pre-competitive way to integrate and share information for type 2 diabetes mellitus (T2DM) in adults. This case study exposes benefits from semantic interoperability after integrating the scientific literature with biomedical data resources, such as UniProt Knowledgebase (UniProtKB) and the Gene Expression Atlas (GXA). We annotated scientific documents in a standardized way, by applying public terminological resources for diseases and proteins, and other text-mining approaches. Eventually, we compared the genetic causes of T2DM across the data resources to demonstrate the benefits from the SESL triple store. Our solution enables publishers to distribute their content with little overhead into remote data infrastructures, such as into any Virtual Knowledge Broker.

The utility of copy number variation (CNV) in studies of hypertension-related left ventricular hypertrophy (LVH): rationale, potential and challenges

The ultimate goal of human genetics is to understand the role of genome variation in elucidating human traits and diseases. Besides single nucleotide polymorphism (SNP), copy number variation (CNV), defined as gains or losses of a DNA segment larger than 1 kb, has recently emerged as an important tool in understanding heritable source of human genomic differences. It has been shown to contribute to genetic susceptibility of various common and complex diseases. Despite a handful of publications, its role in cardiovascular diseases remains largely unknown. Here, we deliberate on the currently available technologies for CNV detection. The possible utility and the potential roles of CNV in exploring the mechanisms of cardiac remodeling in hypertension will also be addressed. Finally, we discuss the challenges for investigations of CNV in cardiovascular diseases and its possible implications in diagnosis of hypertension-related left ventricular hypertrophy (LVH).

Rare and low frequency variant stratification in the UK population: description and impact on association tests

Although variations in allele frequencies at common SNPs have been extensively studied in different populations, little is known about the stratification of rare variants and its impact on association tests. In this paper, we used Affymetrix 500K genotype data from the WTCCC to investigate if variants in three different frequency categories (below 1%, between 1 and 5%, above 5%) show different stratification patterns in the UK population. We found that these patterns are indeed different. The top principal component extracted from the rare variant category shows poor correlations with any principal component or combination of principal components from the low frequency or common variant categories. These results could suggest that a suitable solution to avoid false positive association due to population stratification would involve adjusting for the respective PCs when testing for variants in different allele frequency categories. However, we found this was not the case both on type 2 diabetes data and on simulated data. Indeed, adjusting rare variant association tests on PCs derived from rare variants does no better to correct for population stratification than adjusting on PCs derived from more common variants. Mixed models perform slightly better for low frequency variants than PC based adjustments but less well for the rarest variants. These results call for the need of new methodological developments specifically devoted to address rare variant stratification issues in association tests.

Human population structure and the adaptive response to pathogen-induced selection pressures

The past few years of research in human evolutionary genetics have provided novel insights and questions regarding how human adaptations to recent selective pressures have taken place. Here, we review the advances most relevant to understanding human evolution in response to pathogen-induced selective pressures. Key insights come from theoretical models of adaptive evolution, particularly those that consider spatially structured populations, and from empirical population genomic studies of adaptive evolution in humans. We also review the CCR5-Δ32 HIV resistance allele as a case study of pathogen resistance in humans. Taken together, the results make clear that the human response to pathogen-induced selection pressures depends on a complex interplay between the age of the pathogen, the genetic basis of potential resistance phenotypes, and how population structure impacts the adaptive process in humans.

A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.

Applications of second generation sequencing technologies in complex disorders

Second generation sequencing (2ndGS) technologies generate unprecedented amounts of sequence data very rapidly and at relatively limited costs, allowing the sequence of a human genome to be completed in a few weeks. The principle is on the basis of generating millions of relatively short reads from amplified single DNA fragments using iterative cycles of nucleotide extensions. However, the data generated on this scale present new challenges in interpretation, data analysis and data management. 2ndGS technologies are becoming widespread and are profoundly impacting biomedical research. Common applications include whole-genome sequencing, target resequencing, characterization of structural and copy number variation, profiling epigenetic modifications, transcriptome sequencing and identification of infectious agents. New methodologies and instruments that will enable to sequence the complete human genome in less than a day at a cost of less than $1,000 are currently in development.

Effect of five genetic variants associated with lung function on the risk of chronic obstructive lung disease, and their joint effects on lung function

RATIONALE

Genomic loci are associated with FEV1 or the ratio of FEV1 to FVC in population samples, but their association with chronic obstructive pulmonary disease (COPD) has not yet been proven, nor have their combined effects on lung function and COPD been studied.

OBJECTIVES

To test association with COPD of variants at five loci (TNS1, GSTCD, HTR4, AGER, and THSD4) and to evaluate joint effects on lung function and COPD of these single-nucleotide polymorphisms (SNPs), and variants at the previously reported locus near HHIP.

METHODS

By sampling from 12 population-based studies (n = 31,422), we obtained genotype data on 3,284 COPD case subjects and 17,538 control subjects for sentinel SNPs in TNS1, GSTCD, HTR4, AGER, and THSD4. In 24,648 individuals (including 2,890 COPD case subjects and 13,862 control subjects), we additionally obtained genotypes for rs12504628 near HHIP. Each allele associated with lung function decline at these six SNPs contributed to a risk score. We studied the association of the risk score to lung function and COPD.

MEASUREMENTS AND MAIN RESULTS

Association with COPD was significant for three loci (TNS1, GSTCD, and HTR4) and the previously reported HHIP locus, and suggestive and directionally consistent for AGER and TSHD4. Compared with the baseline group (7 risk alleles), carrying 10-12 risk alleles was associated with a reduction in FEV1 (β = -72.21 ml, P = 3.90 × 10(-4)) and FEV1/FVC (β = -1.53%, P = 6.35 × 10(-6)), and with COPD (odds ratio = 1.63, P = 1.46 × 10(-5)).

CONCLUSIONS

Variants in TNS1, GSTCD, and HTR4 are associated with COPD. Our highest risk score category was associated with a 1.6-fold higher COPD risk than the population average score.

A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis

Genome-wide association studies (GWAS) have yielded novel genetic loci underlying common diseases. We propose a systems genetics approach to utilize these discoveries for better understanding of the genetic architecture of rheumatoid arthritis (RA). Current evidence of genetic associations with RA was sought through PubMed and the NHGRI GWAS catalog. The associations of 15 single nucleotide polymorphisms and HLA-DRB1 alleles were confirmed in 1,287 cases and 1,500 controls of Japanese subjects. Among these, HLA-DRB1 alleles and eight SNPs showed significant associations and all but one of the variants had the same direction of effect as identified in the previous studies, indicating that the genetic risk factors underlying RA are shared across populations. By receiver operating characteristic curve analysis, the area under the curve (AUC) for the genetic risk score based on the selected variants was 68.4%. For seropositive RA patients only, the AUC improved to 70.9%, indicating good but suboptimal predictive ability. A simulation study shows that more than 200 additional loci with similar effect size as recent GWAS findings or 20 rare variants with intermediate effects are needed to achieve AUC = 80.0%. We performed the random walk with restart (RWR) algorithm to prioritize genes for future mapping studies. The performance of the algorithm was confirmed by leave-one-out cross-validation. The RWR algorithm pointed to ZAP70 in the first rank, in which mutation causes RA-like autoimmune arthritis in mice. By applying the hierarchical clustering method to a subnetwork comprising RA-associated genes and top-ranked genes by the RWR, we found three functional modules relevant to RA etiology: "leukocyte activation and differentiation", "pattern-recognition receptor signaling pathway", and "chemokines and their receptors".These results suggest that the systems genetics approach is useful to find directions of future mapping strategies to illuminate biological pathways.

Genome-wide association study of Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis in Europe

BACKGROUND

Stevens-Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) are rare but extremely severe cutaneous adverse drug reactions in which drug-specific associations with HLA-B alleles were described.

OBJECTIVES

To investigate genetic association at a genome-wide level on a large sample of SJS/TEN patients.

METHODS

We performed a genome wide association study on a sample of 424 European cases and 1,881 controls selected from a Reference Control Panel.

RESULTS

Six SNPs located in the HLA region showed significant evidence for association (OR range: 1.53-1.74). The haplotype formed by their risk allele was more associated with the disease than any of the single SNPs and was even much stronger in patients exposed to allopurinol (OR(allopurinol) = 7.77, 95%CI = [4.66; 12.98]). The associated haplotype is in linkage disequilibrium with the HLA-B*5801 allele known to be associated with allopurinol induced SJS/TEN in Asian populations.

CONCLUSION

The involvement of genetic variants located in the HLA region in SJS/TEN is confirmed in European samples, but no other locus reaches genome-wide statistical significance in this sample that is also the largest one collected so far. If some loci outside HLA play a role in SJS/TEN, their effect is thus likely to be very small.

Genomics in the post-GWAS era

The field of genomics has entered a new era in which the ability to identify genetic variants that impact complex human traits and disease in an unbiased fashion using genome-wide approaches is widely accessible. To date, the workhorse of these efforts has been the genome-wide association study (GWAS), which has quickly moved from novel to routine, and has provided key insights into aspects of the underlying allelic architecture of complex traits. The main lesson learned from the early GWAS efforts is that though many disease-associated variants are often discovered, most have only a minor effect on disease, and in total explain only a small amount of the apparent heritability. Here we provide a brief overview of the genetic variation classes that may harbor the heritability missing from GWAS, and touch on approaches that will be leveraged in the coming years as genomics-and by extension medicine-becomes increasingly personalized.

Genetic architecture of cancer and other complex diseases: lessons learned and future directions

Genome-wide association studies have broadened our understanding of the genetic architecture of cancer to include common variants, in addition to the rare variants previously identified by linkage analysis. We review current knowledge on the genetic architecture of four cancers--breast, lung, prostate and colorectal--for which the balance of common and rare alleles identified ranges from fewer common alleles (lung cancer) to more common alleles (prostate cancer). Although most variants are cancer specific, pleiotropy has been observed for several variants, for example, variants at the 8q24 locus and breast, ovarian and prostate cancers or variants in KITLG in relation to hair color and testicular cancer. Although few studies have been adequately powered to investigate heterogeneity among ancestry groups, effect sizes associated with common variants have been reported to be fairly homogenous among ethnic groups. Some associations appear to be ancestry specific, such as HNF1B, which is associated with prostate cancer in European Americans and Latinos but not in African-Americans. Studies of cancer and other complex diseases suggest that a simple dichotomy between rare and common allelic architectures may be too simplistic and that future research is needed to characterize a fuller spectrum of allele frequency (common (>5%), uncommon (1-5%) and rare (<<1%) alleles) and effect size. In addition, a broadening of the concept of genetic architecture to encompass both population architecture, which reflects differences in exposures, genetic factors and population level risk among diverse groups of people, and genomic architecture, which includes structural, epigenomic and somatic variation, is envisioned.

Identification of novel loci for Alzheimer disease and replication of CLU, PICALM, and BIN1 in Caribbean Hispanic individuals

OBJECTIVES

To identify novel loci for late-onset Alzheimer disease (LOAD) in Caribbean Hispanic individuals and to replicate the findings in a publicly available data set from the National Institute on Aging Late-Onset Alzheimer's Disease Family Study.

DESIGN

Nested case-control genome-wide association study.

SETTING

The Washington Heights-Inwood Columbia Aging Project and the Estudio Familiar de Influencia Genetica de Alzheimer study.

PARTICIPANTS

Five hundred forty-nine affected and 544 unaffected individuals of Caribbean Hispanic ancestry.

INTERVENTION

The Illumina HumanHap 650Y chip for genotyping.

MAIN OUTCOME MEASURE

Clinical diagnosis or pathologically confirmed diagnosis of LOAD.

RESULTS

The strongest support for allelic association was for rs9945493 on 18q23 (P=1.7×10(-7)), but 22 additional single-nucleotide polymorphisms (SNPs) had a P value less than 9×10(-6) under 3 different analyses: unadjusted and stratified by the presence or absence of the APOE ε4 allele. Of these SNPs, 5 SNPs (rs4669573 and rs10197851 on 2p25.1; rs11711889 on 3q25.2; rs1117750 on 7p21.1; and rs7908652 on 10q23.1) were associated with LOAD in an independent cohort from the National Institute on Aging Late-Onset Alzheimer's Disease Family Study. We also replicated genetic associations for CLU, PICALM, and BIN1.

CONCLUSIONS

Our genome-wide search of Caribbean Hispanic individuals identified several novel genetic variants associated with LOAD and replicated these associations in a white cohort. We also replicated associations in CLU, PICALM, and BIN1 in the Caribbean Hispanic cohort.

Progress and promise of genome-wide association studies for human complex trait genetics

Enormous progress in mapping complex traits in humans has been made in the last 5 yr. There has been early success for prevalent diseases with complex phenotypes. These studies have demonstrated clearly that, while complex traits differ in their underlying genetic architectures, for many common disorders the predominant pattern is that of many loci, individually with small effects on phenotype. For some traits, loci of large effect have been identified. For almost all complex traits studied in humans, the sum of the identified genetic effects comprises only a portion, generally less than half, of the estimated trait heritability. A variety of hypotheses have been proposed to explain why this might be the case, including untested rare variants, and gene-gene and gene-environment interaction. Effort is currently being directed toward implementation of novel analytic approaches and testing rare variants for association with complex traits using imputed variants from the publicly available 1000 Genomes Project resequencing data and from direct resequencing of clinical samples. Through integration with annotations and functional genomic data as well as by in vitro and in vivo experimentation, mapping studies continue to characterize functional variants associated with complex traits and address fundamental issues such as epistasis and pleiotropy. This review focuses primarily on the ways in which genome-wide association studies (GWASs) have revolutionized the field of human quantitative genetics.

Three ways of combining genotyping and resequencing in case-control association studies

We describe three statistical results that we have found to be useful in case-control genetic association testing. All three involve combining the discovery of novel genetic variants, usually by sequencing, with genotyping methods that recognize previously discovered variants. We first consider expanding the list of known variants by concentrating variant-discovery in cases. Although the naive inclusion of cases-only sequencing data would create a bias, we show that some sequencing data may be retained, even if controls are not sequenced. Furthermore, for alleles of intermediate frequency, cases-only sequencing with bias-correction entails little if any loss of power, compared to dividing the same sequencing effort among cases and controls. Secondly, we investigate more strongly focused variant discovery to obtain a greater enrichment for disease-related variants. We show how case status, family history, and marker sharing enrich the discovery set by increments that are multiplicative with penetrance, enabling the preferential discovery of high-penetrance variants. A third result applies when sequencing is the primary means of counting alleles in both cases and controls, but a supplementary pooled genotyping sample is used to identify the variants that are very rare. We show that this raises no validity issues, and we evaluate a less expensive and more adaptive approach to judging rarity, based on group-specific variants. We demonstrate the important and unusual caveat that this method requires equal sample sizes for validity. These three results can be used to more efficiently detect the association of rare genetic variants with disease.

A data-adaptive sum test for disease association with multiple common or rare variants

Since associations between complex diseases and common variants are typically weak, and approaches to genotyping rare variants (e.g. by next-generation resequencing) multiply, there is an urgent demand to develop powerful association tests that are able to detect disease associations with both common and rare variants. In this article we present such a test. It is based on data-adaptive modifications to a so-called Sum test originally proposed for common variants, which aims to strike a balance between utilizing information on multiple markers in linkage disequilibrium and reducing the cost of large degrees of freedom or of multiple testing adjustment. When applied to multiple common or rare variants in a candidate region, the proposed test is easy to use with 1 degree of freedom and without the need for multiple testing adjustment. We show that the proposed test has high power across a wide range of scenarios with either common or rare variants, or both. In particular, in some situations the proposed test performs better than several commonly used methods.

Refined QTLs of osteoporosis-related traits by linkage analysis with genome-wide SNPs: Framingham SHARe

Genome-wide association studies (GWAS) using high-density array of single-nucleotide polymorphisms (SNPs) offer an unbiased strategy to identify new candidate genes for osteoporosis. We used a subset of autosomal SNPs from the Affymetrix 500K+50K SNP GeneChip marker set to examine genetic linkage with multiple highly heritable osteoporosis-related traits, including BMD of the hip and spine, heel ultrasound (attenuation and speed of sound), and geometric indices of the hip, in two generations from the Framingham Osteoporosis Study. Variance component linkage analysis was performed using normalized residuals (adjusted for age, height, BMI, and estrogen status in women). Multipoint linkage analyses produced LOD scores > or =3.0 for BMD on chromosomes (chr.) 9 and 11 and for ultrasound speed of sound on chr. 5. Hip geometric traits were linked with higher LOD scores, such as with shaft width on chr. 4 (LOD=3.9) and chr. 16 (LOD=3.8) and with shaft section modulus on chr. 22 (LOD=4.0). LOD score > or =5.0 was obtained for femoral neck width on chr. 7. In conclusion, with an SNP-based linkage approach, we identified several novel potential QTLs and confirmed previously identified chromosomal regions linked to bone mass and geometry. Subsequent focus on the spectrum of genetic polymorphisms in these refined regions may contribute to finding variants predisposing to osteoporosis.

Sequencing genomes: from individuals to populations

The whole genome sequences of Jim Watson and Craig Venter are early examples of personalized genomics, which promises to change how we approach healthcare in the future. Before personal sequencing can have practical medical benefits, however, and before it should be advocated for implementation at the population-scale, there needs to be a better understanding of which genetic variants influence which traits and how their effects are modified by epigenetic factors. Nonetheless, for forging links between DNA sequence and phenotype, efforts to sequence the genomes of individuals need to continue; this includes sequencing sub-populations for association studies which analyse the difference in sequence between disease affected and unaffected individuals. Such studies can only be applied on a large enough scale to be effective if the massive strides in sequencing technology that have recently occurred also continue.

Missense mutations in the MEFV gene are associated with fibromyalgia syndrome and correlate with elevated IL-1beta plasma levels

Background

Fibromyalgia syndrome (FMS), a common, chronic, widespread musculoskeletal pain disorder found in 2% of the general population and with a preponderance of 85% in females, has both genetic and environmental contributions. Patients and their parents have high plasma levels of the chemokines MCP-1 and eotaxin, providing evidence for both a genetic and an immunological/inflammatory origin for the syndrome (Zhang et al., 2008, Exp. Biol. Med. 233: 1171–1180).

Methods and Findings

In a search for a candidate gene affecting inflammatory pathways, among five screened in our patient samples (100 probands with FMS and their parents), we found 10 rare and one common alleles for MEFV, a gene in which various compound heterozygous mutations lead to Familial Mediterranean Fever (FMF). A total of 2.63 megabases of genomic sequence of the MEFV gene were scanned by direct sequencing. The collection of rare missense mutations (all heterozygotes and tested in the aggregate) had a significant elevated frequency of transmission to affecteds (p = 0.0085, one-sided, exact binomial test). Our data provide evidence that rare missense variants of the MEFV gene are, collectively, associated with risk of FMS and are present in a subset of 15% of FMS patients. This subset had, on average, high levels of plasma IL-1β (p = 0.019) compared to FMS patients without rare variants, unaffected family members with or without rare variants, and unrelated controls of unknown genotype. IL-1β is a cytokine associated with the function of the MEFV gene and thought to be responsible for its symptoms of fever and muscle aches.

Conclusions

Since misregulation of IL-1β expression has been predicted for patients with mutations in the MEFV gene, we conclude that patients heterozygous for rare missense variants of this gene may be predisposed to FMS, possibly triggered by environmental factors.