A versatile gene-based test for genome-wide association studies

Pooling/bootstrap-based GWAS (pbGWAS) identifies new loci modifying the age of onset in PSEN1 p.Glu280Ala Alzheimer's disease

The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P=1.29 × 10⁻¹²; NPHP1, rs10173717, P=1.74 × 10⁻¹²; CADPS2, rs3757536, P=1.54 × 10⁻¹⁰; GREM2, rs12129547, P=1.69 × 10⁻¹³, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.

Association between putative functional variants in the PSMB9 gene and risk of melanoma--re-analysis of published melanoma genome-wide association studies

To mine possibly hidden causal single-nucleotide polymorphisms (SNPs) of melanoma, we investigated the association of SNPs in 76 M/G1 transition genes with melanoma risk using our published genome-wide association study (GWAS) data set with 1804 melanoma cases and 1026 cancer-free controls. We found multiple SNPs with P < 0.01 and performed validation studies for 18 putative functional SNPs in PSMB9 in two other GWAS data sets. Two SNPs (rs1351383 and rs2127675) were associated with melanoma risk in the GenoMEL data set (P = 0.013 and 0.004, respectively), but failed in validation using the Australian data set. Genotype-phenotype analysis revealed these two SNPs were significantly correlated with mRNA expression level of PSMB9. Further experiments revealed that SNP rs2071480, which is in high LD with rs1351383 and rs2127675, may have a weak effect on the promoter activity of PSMB9. Taken together, our data suggested that functional variants in PSMB9 may contribute to melanoma susceptibility.

Genome-wide association study of a quantitative disordered gambling trait

Disordered gambling is a moderately heritable trait, but the underlying genetic basis is largely unknown. We performed a genome-wide association study (GWAS) for disordered gambling using a quantitative factor score in 1312 twins from 894 Australian families. Association was conducted for 2 381 914 single-nucleotide polymorphisms (SNPs) using the family-based association test in Merlin followed by gene and pathway enrichment analyses. Although no SNP reached genome-wide significance, six achieved P-values < 1 × 10(-5) with variants in three genes (MT1X, ATXN1 and VLDLR) implicated in disordered gambling. Secondary case-control analyses found two SNPs on chromosome 9 (rs1106076 and rs12305135 near VLDLR) and rs10812227 near FZD10 on chromosome 12 to be significantly associated with lifetime Diagnostic and Statistical Manual of Mental Disorders, fourth edition pathological gambling and South Oaks Gambling Screen classified probable pathological gambling status. Furthermore, several addiction-related pathways were enriched for SNPs associated with disordered gambling. Finally, gene-based analysis of 24 candidate genes for dopamine agonist-induced gambling in individuals with Parkinson's disease suggested an enrichment of SNPs associated with disordered gambling. We report the first GWAS of disordered gambling. While further replication is required, the identification of susceptibility loci and biological pathways will be important in characterizing the biological mechanisms that underpin disordered gambling.

White matter integrity as an intermediate phenotype: exploratory genome-wide association analysis in individuals at high risk of bipolar disorder

White matter integrity, as measured using diffusion tensor imaging (DTI), is reduced in individuals with bipolar disorder (BD), their unaffected relatives and carriers of specific risk-alleles. Fractional anisotropy (FA), an index of white matter integrity, is highly heritable but the genetic architecture of this trait has received little investigation. In this study we performed a genome-wide association study with FA as quantitative phenotype, in unaffected relatives of patients with BD (N=70) and a matched control group (N=80). Amongst our top results were SNPs located in genes involved in cell adhesion, white matter development and neuronal plasticity. Pathway analysis of the top associated polymorphisms and genes confirmed the enrichment of processes relevant to BD and white matter development, including axon guidance, ErbB-signalling neurotrophin signalling, phosphatidylinositol signalling, and cell adhesion. The majority of genes implicated in these pathways were differentially associated with FA in individuals at high familial risk, suggesting interactions with genetic background or environmental factors secondary to familial risk for BD. Although the present findings require independent replication, the results encourage the use of global FA as a quantitative phenotype in future large-scale studies which may help to identify the biological processes underlying reduced FA in BD and other psychiatric disorders.

Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E ϵ4,and the risk of late-onset Alzheimer disease in African Americans

IMPORTANCE

Genetic variants associated with susceptibility to late-onset Alzheimer disease are known for individuals of European ancestry, but whether the same or different variants account for the genetic risk of Alzheimer disease in African American individuals is unknown. Identification of disease-associated variants helps identify targets for genetic testing, prevention, and treatment.

OBJECTIVE

To identify genetic loci associated with late-onset Alzheimer disease in African Americans.

DESIGN, SETTING, AND PARTICIPANTS

The Alzheimer Disease Genetics Consortium (ADGC) assembled multiple data sets representing a total of 5896 African Americans (1968 case participants, 3928 control participants) 60 years or older that were collected between 1989 and 2011 at multiple sites. The association of Alzheimer disease with genotyped and imputed single-nucleotide polymorphisms (SNPs) was assessed in case-control and in family-based data sets. Results from individual data sets were combined to perform an inverse variance-weighted meta-analysis, first with genome-wide analyses and subsequently with gene-based tests for previously reported loci.

MAIN OUTCOMES AND MEASURES

Presence of Alzheimer disease according to standardized criteria.

RESULTS

Genome-wide significance in fully adjusted models (sex, age, APOE genotype, population stratification) was observed for a SNP in ABCA7 (rs115550680, allele = G; frequency, 0.09 cases and 0.06 controls; odds ratio [OR], 1.79 [95% CI, 1.47-2.12]; P = 2.2 × 10(-9)), which is in linkage disequilibrium with SNPs previously associated with Alzheimer disease in Europeans (0.8 < D' < 0.9). The effect size for the SNP in ABCA7 was comparable with that of the APOE ϵ4-determining SNP rs429358 (allele = C; frequency, 0.30 cases and 0.18 controls; OR, 2.31 [95% CI, 2.19-2.42]; P = 5.5 × 10(-47)). Several loci previously associated with Alzheimer disease but not reaching significance in genome-wide analyses were replicated in gene-based analyses accounting for linkage disequilibrium between markers and correcting for number of tests performed per gene (CR1, BIN1, EPHA1, CD33; 0.0005 < empirical P < .001).

CONCLUSIONS AND RELEVANCE

In this meta-analysis of data from African American participants, Alzheimer disease was significantly associated with variants in ABCA7 and with other genes that have been associated with Alzheimer disease in individuals of European ancestry. Replication and functional validation of this finding is needed before this information is used in clinical settings.

The molecular genetic architecture of self-employment

Economic variables such as income, education, and occupation are known to affect mortality and morbidity, such as cardiovascular disease, and have also been shown to be partly heritable. However, very little is known about which genes influence economic variables, although these genes may have both a direct and an indirect effect on health. We report results from the first large-scale collaboration that studies the molecular genetic architecture of an economic variable-entrepreneurship-that was operationalized using self-employment, a widely-available proxy. Our results suggest that common SNPs when considered jointly explain about half of the narrow-sense heritability of self-employment estimated in twin data (σ(g)(2)/σ(P)(2) = 25%, h(2) = 55%). However, a meta-analysis of genome-wide association studies across sixteen studies comprising 50,627 participants did not identify genome-wide significant SNPs. 58 SNPs with p<10(-5) were tested in a replication sample (n = 3,271), but none replicated. Furthermore, a gene-based test shows that none of the genes that were previously suggested in the literature to influence entrepreneurship reveal significant associations. Finally, SNP-based genetic scores that use results from the meta-analysis capture less than 0.2% of the variance in self-employment in an independent sample (p≥0.039). Our results are consistent with a highly polygenic molecular genetic architecture of self-employment, with many genetic variants of small effect. Although self-employment is a multi-faceted, heavily environmentally influenced, and biologically distal trait, our results are similar to those for other genetically complex and biologically more proximate outcomes, such as height, intelligence, personality, and several diseases.

Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk

Common genetic variants contribute to the observed variation in breast cancer risk for BRCA2 mutation carriers; those known to date have all been found through population-based genome-wide association studies (GWAS). To comprehensively identify breast cancer risk modifying loci for BRCA2 mutation carriers, we conducted a deep replication of an ongoing GWAS discovery study. Using the ranked P-values of the breast cancer associations with the imputed genotype of 1.4 M SNPs, 19,029 SNPs were selected and designed for inclusion on a custom Illumina array that included a total of 211,155 SNPs as part of a multi-consortial project. DNA samples from 3,881 breast cancer affected and 4,330 unaffected BRCA2 mutation carriers from 47 studies belonging to the Consortium of Investigators of Modifiers of BRCA1/2 were genotyped and available for analysis. We replicated previously reported breast cancer susceptibility alleles in these BRCA2 mutation carriers and for several regions (including FGFR2, MAP3K1, CDKN2A/B, and PTHLH) identified SNPs that have stronger evidence of association than those previously published. We also identified a novel susceptibility allele at 6p24 that was inversely associated with risk in BRCA2 mutation carriers (rs9348512; per allele HR = 0.85, 95% CI 0.80-0.90, P = 3.9 × 10(-8)). This SNP was not associated with breast cancer risk either in the general population or in BRCA1 mutation carriers. The locus lies within a region containing TFAP2A, which encodes a transcriptional activation protein that interacts with several tumor suppressor genes. This report identifies the first breast cancer risk locus specific to a BRCA2 mutation background. This comprehensive update of novel and previously reported breast cancer susceptibility loci contributes to the establishment of a panel of SNPs that modify breast cancer risk in BRCA2 mutation carriers. This panel may have clinical utility for women with BRCA2 mutations weighing options for medical prevention of breast cancer.

Genes, environments, and developmental research: methods for a multi-site study of early substance abuse

The importance of including developmental and environmental measures in genetic studies of human pathology is widely acknowledged, but few empirical studies have been published. Barriers include the need for longitudinal studies that cover relevant developmental stages and for samples large enough to deal with the challenge of testing gene-environment-development interaction. A solution to some of these problems is to bring together existing data sets that have the necessary characteristics. As part of the National Institute on Drug Abuse-funded Gene-Environment-Development Initiative, our goal is to identify exactly which genes, which environments, and which developmental transitions together predict the development of drug use and misuse. Four data sets were used of which common characteristics include (1) general population samples, including males and females; (2) repeated measures across adolescence and young adulthood; (3) assessment of nicotine, alcohol, and cannabis use and addiction; (4) measures of family and environmental risk; and (5) consent for genotyping DNA from blood or saliva. After quality controls, 2,962 individuals provided over 15,000 total observations. In the first gene-environment analyses, of alcohol misuse and stressful life events, some significant gene-environment and gene-development effects were identified. We conclude that in some circumstances, already collected data sets can be combined for gene-environment and gene-development analyses. This greatly reduces the cost and time needed for this type of research. However, care must be taken to ensure careful matching across studies and variables.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Gene-based testing of interactions in association studies of quantitative traits

Various methods have been developed for identifying gene–gene interactions in genome-wide association studies (GWAS). However, most methods focus on individual markers as the testing unit, and the large number of such tests drastically erodes statistical power. In this study, we propose novel interaction tests of quantitative traits that are gene-based and that confer advantage in both statistical power and biological interpretation. The framework of gene-based gene–gene interaction (GGG) tests combine marker-based interaction tests between all pairs of markers in two genes to produce a gene-level test for interaction between the two. The tests are based on an analytical formula we derive for the correlation between marker-based interaction tests due to linkage disequilibrium. We propose four GGG tests that extend the following P value combining methods: minimum P value, extended Simes procedure, truncated tail strength, and truncated P value product. Extensive simulations point to correct type I error rates of all tests and show that the two truncated tests are more powerful than the other tests in cases of markers involved in the underlying interaction not being directly genotyped and in cases of multiple underlying interactions. We applied our tests to pairs of genes that exhibit a protein–protein interaction to test for gene-level interactions underlying lipid levels using genotype data from the Atherosclerosis Risk in Communities study. We identified five novel interactions that are not evident from marker-based interaction testing and successfully replicated one of these interactions, between SMAD3 and NEDD9, in an independent sample from the Multi-Ethnic Study of Atherosclerosis. We conclude that our GGG tests show improved power to identify gene-level interactions in existing, as well as emerging, association studies.

Epistasis is likely to play a significant role in complex diseases or traits and is one of the many possible explanations for “missing heritability.” However, epistatic interactions have been difficult to detect in genome-wide association studies (GWAS) due to the limited power caused by the multiple-testing correction from the large number of tests conducted. Gene-based gene–gene interaction (GGG) tests might hold the key to relaxing the multiple-testing correction burden and increasing the power for identifying epistatic interactions in GWAS. Here, we developed GGG tests of quantitative traits by extending four P value combining methods and evaluated their type I error rates and power using extensive simulations. All four GGG tests are more powerful than a principal component-based test. We also applied our GGG tests to data from the Atherosclerosis Risk in Communities study and found five gene-level interactions associated with the levels of total cholesterol and high-density lipoprotein cholesterol (HDL-C). One interaction between SMAD3 and NEDD9 on HDL-C was further replicated in an independent sample from the Multi-Ethnic Study of Atherosclerosis.

NCK2 is significantly associated with opiates addiction in African-origin men

Substance dependence is a complex environmental and genetic disorder with significant social and medical concerns. Understanding the etiology of substance dependence is imperative to the development of effective treatment and prevention strategies. To this end, substantial effort has been made to identify genes underlying substance dependence, and in recent years, genome-wide association studies (GWASs) have led to discoveries of numerous genetic variants for complex diseases including substance dependence. Most of the GWAS discoveries were only based on single nucleotide polymorphisms (SNPs) and a single dichotomized outcome. By employing both SNP- and gene-based methods of analysis, we identified a strong (odds ratio = 13.87) and significant (P value = 1.33E - 11) association of an SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Codependence analysis also identified a genome-wide significant association between NCK2 and comorbidity of substance dependence (P value = 3.65E - 08) in African-origin men. Furthermore, we observed that the association between the NCK2 gene (P value = 3.12E - 10) and opiates addiction reached the gene-based genome-wide significant level. In summary, our findings provided the first evidence for the involvement of NCK2 in the susceptibility to opiates addiction and further revealed the racial and gender specificities of its impact.

Empirical pathway analysis, without permutation

Resampling-based expression pathway analysis techniques have been shown to preserve type I error rates, in contrast to simple gene-list approaches that implicitly assume the independence of genes in ranked lists. However, resampling is intensive in computation time and memory requirements. We describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson's correlation, and summed score statistics, that have good performance for even relatively small sample sizes. Our approach preserves the essence of permutation pathway analysis, but with greatly reduced computation. Extensions for inclusion of covariates and censored data are described, and we test the performance of our procedures using simulations based on real datasets. These approaches have been implemented in the new R package safeExpress.

Role of p73 in Alzheimer disease: lack of association in mouse models or in human cohorts

Background

P73 belongs to the p53 family of cell survival regulators with the corresponding locus Trp73 producing the N-terminally distinct isoforms, TAp73 and DeltaNp73. Recently, two studies have implicated the murine Trp73 in the modulation in phospho-tau accumulation in aged wild type mice and in young mice modeling Alzheimer’s disease (AD) suggesting that Trp73, particularly the DeltaNp73 isoform, links the accumulation of amyloid peptides to the creation of neurofibrillary tangles (NFTs). Here, we reevaluated tau pathologies in the same TgCRND8 mouse model as the previous studies.

Results

Despite the use of the same animal models, our in vivo studies failed to demonstrate biochemical or histological evidence for misprocessing of tau in young compound Trp73^+/- + TgCRND8 mice or in aged Trp73^+/- mice analyzed at the ages reported previously, or older. Secondly, we analyzed an additional mouse model where the DeltaNp73 was specifically deleted and confirmed a lack of impact of the DeltaNp73 allele, either in heterozygous or homozygous form, upon tau pathology in aged mice. Lastly, we also examined human TP73 for single nucleotide polymorphisms (SNPs) and/or copy number variants in a meta-analysis of 10 AD genome-wide association datasets. No SNPs reached significance after correction for multiple testing and no duplications/deletions in TP73 were found in 549 cases of AD and 544 non-demented controls.

Conclusion

Our results fail to support P73 as a contributor to AD pathogenesis.

Susceptibility loci associated with specific and shared subtypes of lymphoid malignancies

The genetics of lymphoma susceptibility reflect the marked heterogeneity of diseases that comprise this broad phenotype. However, multiple subtypes of lymphoma are observed in some families, suggesting shared pathways of genetic predisposition to these pathologically distinct entities. Using a two-stage GWAS, we tested 530,583 SNPs in 944 cases of lymphoma, including 282 familial cases, and 4,044 public shared controls, followed by genotyping of 50 SNPs in 1,245 cases and 2,596 controls. A novel region on 11q12.1 showed association with combined lymphoma (LYM) subtypes. SNPs in this region included rs12289961 near LPXN, (P(LYM) = 3.89×10(-8), OR = 1.29) and rs948562 (P(LYM) = 5.85×10(-7), OR = 1.29). A SNP in a novel non-HLA region on 6p23 (rs707824, P(NHL) = 5.72×10(-7)) was suggestive of an association conferring susceptibility to lymphoma. Four SNPs, all in a previously reported HLA region, 6p21.32, showed genome-wide significant associations with follicular lymphoma. The most significant association with follicular lymphoma was for rs4530903 (P(FL) = 2.69×10(-12), OR = 1.93). Three novel SNPs near the HLA locus, rs9268853, rs2647046, and rs2621416, demonstrated additional variation contributing toward genetic susceptibility to FL associated with this region. Genes implicated by GWAS were also found to be cis-eQTLs in lymphoblastoid cell lines; candidate genes in these regions have been implicated in hematopoiesis and immune function. These results, showing novel susceptibility regions and allelic heterogeneity, point to the existence of pathways of susceptibility to both shared as well as specific subtypes of lymphoid malignancy.

Boosting signals in gene-based association studies via efficient SNP selection

Set-based association studies based on genes or pathways have shown great promise in interpreting association signals associated with complex diseases. These approaches are particularly useful when variants in a set have moderate effects and are difficult to be detected with single marker analysis, especially when variants function jointly in a complicated manner. The set-based analyses use a summary statistic such as the maximum or average of individual signal (e.g. a chi-square statistic) over all variants in a set, or consider their joint distribution to assess the significance of the set. The signal obtained with this treatment, however, could be potentially diluted when noisy variants are not taken good care of, leading to either inflated false negatives or false positives. Thus, the selection of disease informative single-nucleotide polymorphism (diSNPs) plays a crucial role in improving the power of the set-based association study. In this work, we propose an efficient diSNP selection method based on the information theory. We select diSNP variants by considering their relative information contribution to a disease status, which is different from the usual tag SNP selection. The relative merit of pre-selecting diSNPs in a set-based association analysis is demonstrated through extensive simulation studies and real data analysis.

Genome-wide association analyses identify multiple loci associated with central corneal thickness and keratoconus

Central corneal thickness (CCT) is associated with eye conditions including keratoconus and glaucoma. We performed a meta-analysis on >20,000 individuals in European and Asian populations that identified 16 new loci associated with CCT at genome-wide significance (P < 5 × 10(-8)). We further showed that 2 CCT-associated loci, FOXO1 and FNDC3B, conferred relatively large risks for keratoconus in 2 cohorts with 874 cases and 6,085 controls (rs2721051 near FOXO1 had odds ratio (OR) = 1.62, 95% confidence interval (CI) = 1.4-1.88, P = 2.7 × 10(-10), and rs4894535 in FNDC3B had OR = 1.47, 95% CI = 1.29-1.68, P = 4.9 × 10(-9)). FNDC3B was also associated with primary open-angle glaucoma (P = 5.6 × 10(-4); tested in 3 cohorts with 2,979 cases and 7,399 controls). Further analyses implicate the collagen and extracellular matrix pathways in the regulation of CCT.

Association between functional polymorphisms in genes involved in the MAPK signaling pathways and cutaneous melanoma risk

Genome-wide association studies (GWASs) have mainly focused on top significant single nucleotide polymorphisms (SNPs), most of which did not have clear biological functions but were just surrogates for unknown causal variants. Studying SNPs with modest association and putative functions in biologically plausible pathways has become one complementary approach to GWASs. To unravel the key roles of mitogen-activated protein kinase (MAPK) pathways in cutaneous melanoma (CM) risk, we re-evaluated the associations between 47 818 SNPs in 280 MAPK genes and CM risk using our published GWAS dataset with 1804 CM cases and 1026 controls. We initially found 105 SNPs with P ≤ 0.001, more than expected by chance, 26 of which were predicted to be putatively functional SNPs. The risk associations with 16 SNPs around DUSP14 (rs1051849) and a previous reported melanoma locus MAFF/PLA2G6 (proxy SNP rs4608623) were replicated in the GenoMEL dataset (P < 0.01) but failed in the Australian dataset. Meta-analysis showed that rs1051849 in the 3' untranslated regions of DUSP14 was associated with a reduced risk of melanoma (odds ratio = 0.89, 95% confidence interval: 0.82-0.96, P = 0.003, false discovery rate = 0.056). Further genotype-phenotype correlation analysis using the 90 HapMap lymphoblastoid cell lines from Caucasians showed significant correlations between two SNPs (rs1051849 and rs4608623) and messenger RNA expression levels of DUSP14 and MAFF (P = 0.025 and P = 0.010, respectively). Gene-based tests also revealed significant SNPs were over-represented in MAFF, PLA2G6, DUSP14 and other 16 genes. Our results suggest that functional SNPs in MAPK pathways may contribute to CM risk. Further studies are warranted to validate our findings.

Large scale association analysis for drug addiction: results from SNP to gene

Many genetic association studies used single nucleotide polymorphisms (SNPs) data to identify genetic variants for complex diseases. Although SNP-based associations are most common in genome-wide association studies (GWAS), gene-based association analysis has received increasing attention in understanding genetic etiologies for complex diseases. While both methods have been used to analyze the same data, few genome-wide association studies compare the results or observe the connection between them. We performed a comprehensive analysis of the data from the Study of Addiction: Genetics and Environment (SAGE) and compared the results from the SNP-based and gene-based analyses. Our results suggest that the gene-based method complements the individual SNP-based analysis, and conceptually they are closely related. In terms of gene findings, our results validate many genes that were either reported from the analysis of the same dataset or based on animal studies for substance dependence.

RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia

BACKGROUND

While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways.

RESULTS

We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis.

CONCLUSIONS

Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia.

Genome-wide association meta-analysis identifies new endometriosis risk loci

We conducted a genome-wide association meta-analysis of 4,604 endometriosis cases and 9,393 controls of Japanese and European ancestry. We show that rs12700667 on chromosome 7p15.2, previously found to associate with disease in Europeans, replicates in Japanese (P = 3.6 × 10(-3)), and we confirm association of rs7521902 at 1p36.12 near WNT4. In addition, we establish an association of rs13394619 in GREB1 at 2p25.1 with endometriosis and identify a newly associated locus at 12q22 near VEZT (rs10859871). Excluding cases of European ancestry of minimal or unknown severity, we identified additional previously unknown loci at 2p14 (rs4141819), 6p22.3 (rs7739264) and 9p21.3 (rs1537377). All seven SNP effects were replicated in an independent cohort and associated at P <5 × 10(-8) in a combined analysis. Finally, we found a significant overlap in polygenic risk for endometriosis between the genome-wide association cohorts of European and Japanese descent (P = 8.8 × 10(-11)), indicating that many weakly associated SNPs represent true endometriosis risk loci and that risk prediction and future targeted disease therapy may be transferred across these populations.

Cancer pharmacogenomics: strategies and challenges

Genetic variation influences the response of an individual to drug treatments. Understanding this variation has the potential to make therapy safer and more effective by determining selection and dosing of drugs for an individual patient. In the context of cancer, tumours may have specific disease-defining mutations, but a patient's germline genetic variation will also affect drug response (both efficacy and toxicity), and here we focus on how to study this variation. Advances in sequencing technologies, statistical genetics analysis methods and clinical trial designs have shown promise for the discovery of variants associated with drug response. We discuss the application of germline genetics analysis methods to cancer pharmacogenomics with a focus on the special considerations for study design.

Gene size matters

In this work we show that in genome-wide association studies (GWAS) there is a strong bias favoring of genes covered by larger numbers of SNPs. Thus, we state here that there is a need for correction for such bias when performing downstream gene-level analysis, e.g. pathway analysis and gene-set analysis. We investigate several methods of obtaining gene level statistical significance in GWAS, and compare their effectiveness in correcting such bias. We also propose a simple algorithm based on first order statistic that corrects such bias.

Population-based case-control association studies

This unit provides an overview of the design and analysis of population-based case-control studies of genetic risk factors for complex disease. Considerations specific to genetic studies are emphasized. The unit reviews basic study designs differentiating case-control studies from others, presents different genetic association strategies (candidate gene, genome-wide association, and high-throughput sequencing), introduces basic methods of statistical analysis for case-control data and approaches to combining case-control studies, and discusses measures of association and impact. Admixed populations, controlling for confounding (including population stratification), consideration of multiple loci and environmental risk factors, and complementary analyses of haplotypes, genes, and pathways are briefly discussed. Readers are referred to basic texts on epidemiology for more details on general conduct of case-control studies.

A multi-SNP locus-association method reveals a substantial fraction of the missing heritability

There are many known examples of multiple semi-independent associations at individual loci; such associations might arise either because of true allelic heterogeneity or because of imperfect tagging of an unobserved causal variant. This phenomenon is of great importance in monogenic traits but has not yet been systematically investigated and quantified in complex-trait genome-wide association studies (GWASs). Here, we describe a multi-SNP association method that estimates the effect of loci harboring multiple association signals by using GWAS summary statistics. Applying the method to a large anthropometric GWAS meta-analysis (from the Genetic Investigation of Anthropometric Traits consortium study), we show that for height, body mass index (BMI), and waist-to-hip ratio (WHR), 3%, 2%, and 1%, respectively, of additional phenotypic variance can be explained on top of the previously reported 10% (height), 1.5% (BMI), and 1% (WHR). The method also permitted a substantial increase (by up to 50%) in the number of loci that replicate in a discovery-validation design. Specifically, we identified 74 loci at which the multi-SNP, a linear combination of SNPs, explains significantly more variance than does the best individual SNP. A detailed analysis of multi-SNPs shows that most of the additional variability explained is derived from SNPs that are not in linkage disequilibrium with the lead SNP, suggesting a major contribution of allelic heterogeneity to the missing heritability.

Searching joint association signals in CATIE schizophrenia genome-wide association studies through a refined integrative network approach

Background

Genome-wide association studies (GWAS) have generated a wealth of valuable genotyping data for complex diseases/traits. A large proportion of these data are embedded with many weakly associated markers that have been missed in traditional single marker analyses, but they may provide valuable insights in dissecting the genetic components of diseases. Gene set analysis (GSA) augmented by protein-protein interaction network data provides a promising way to examine GWAS data by analyzing the combined effects of multiple genes/markers, each of which may have only individually weak to moderate association effects. A critical issue in GSA of GWAS data is the definition of gene-wise P values based on multiple SNPs mapped to a gene.

Results

In this study, we proposed an alternative restricted search approach based on our previously developed dense module search algorithm, and we demonstrated it in the CATIE GWAS dataset for schizophrenia. Specifically, we explored three ways of computing gene-wise P values and examined their effects on the resultant module genes. These methods calculate gene-wise P values based on all the SNPs, the top ranked SNPs, or the most significant SNP among all the SNPs mapped to a gene. We applied the restricted search approach and identified a module gene set for each of the gene-wise P value data set. In our evaluation using an independent method, ALIGATOR, we showed that although each of these input datasets generated a unique set of module genes, all of them were significant in the GWAS dataset. Further functional enrichment analysis of these module genes showed that at the pathway level, they were all consistently related to neuro- and immune-related pathways. Finally, we compared our method with a previously reported method.

Conclusion

Our results showed that the approaches to computing gene-wise P values in GWAS data are critical in GSA. This work is useful for evaluating key factors in GSA of GWAS data.

Genome-wide association study to identify the genetic determinants of otitis media susceptibility in childhood

Background

Otitis media (OM) is a common childhood disease characterised by middle ear inflammation and effusion. Susceptibility to recurrent acute OM (rAOM; ≥3 episodes of AOM in 6 months) and chronic OM with effusion (COME; MEE ≥3 months) is 40–70% heritable. Few underlying genes have been identified to date, and no genome-wide association study (GWAS) of OM has been reported.

Methods and Findings

Data for 2,524,817 single nucleotide polymorphisms (SNPs; 535,544 quality-controlled SNPs genotyped by Illumina 660W-Quad; 1,989,273 by imputation) were analysed for association with OM in 416 cases and 1,075 controls from the Western Australian Pregnancy Cohort (Raine) Study. Logistic regression analyses under an additive model undertaken in GenABEL/ProbABEL adjusting for population substructure using principal components identified SNPs at CAPN14 (rs6755194: OR = 1.90; 95%CI 1.47–2.45; P_adj-PCA = 8.3×10⁻⁷) on chromosome 2p23.1 as the top hit, with independent effects (rs1862981: OR = 1.60; 95%CI 1.29–1.99; P_adj-PCA = 2.2×10⁻⁵) observed at the adjacent GALNT14 gene. In a gene-based analysis in VEGAS, BPIFA3 (P_Gene = 2×10⁻⁵) and BPIFA1 (P_Gene = 1.07×10⁻⁴) in the BPIFA gene cluster on chromosome 20q11.21 were the top hits. In all, 32 genomic regions show evidence of association (P_adj-PCA<10⁻⁵) in this GWAS, with pathway analysis showing a connection between top candidates and the TGFβ pathway. However, top and tag-SNP analysis for seven selected candidate genes in this pathway did not replicate in 645 families (793 affected individuals) from the Western Australian Family Study of Otitis Media (WAFSOM). Lack of replication may be explained by sample size, difference in OM disease severity between primary and replication cohorts or due to type I error in the primary GWAS.

Conclusions

This first discovery GWAS for an OM phenotype has identified CAPN14 and GALNT14 on chromosome 2p23.1 and the BPIFA gene cluster on chromosome 20q11.21 as novel candidate genes which warrant further analysis in cohorts matched more precisely for clinical phenotypes.

Unraveling the genetic etiology of adult antisocial behavior: a genome-wide association study

Crime poses a major burden for society. The heterogeneous nature of criminal behavior makes it difficult to unravel its causes. Relatively little research has been conducted on the genetic influences of criminal behavior. The few twin and adoption studies that have been undertaken suggest that about half of the variance in antisocial behavior can be explained by genetic factors. In order to identify the specific common genetic variants underlying this behavior, we conduct the first genome-wide association study (GWAS) on adult antisocial behavior. Our sample comprised a community sample of 4816 individuals who had completed a self-report questionnaire. No genetic polymorphisms reached genome-wide significance for association with adult antisocial behavior. In addition, none of the traditional candidate genes can be confirmed in our study. While not genome-wide significant, the gene with the strongest association (p-value = 8.7×10(-5)) was DYRK1A, a gene previously related to abnormal brain development and mental retardation. Future studies should use larger, more homogeneous samples to disentangle the etiology of antisocial behavior. Biosocial criminological research allows a more empirically grounded understanding of criminal behavior, which could ultimately inform and improve current treatment strategies.

Behavioral variant frontotemporal lobar degeneration with amyotrophic lateral sclerosis with a chromosome 9p21 hexanucleotide repeat

To determine the genetic basis of familial frontotemporal lobar degeneration (FTLD) with amyotrophic lateral sclerosis (ALS) we performed a clinical and genetic analysis of an affected family. A 51-year-old man with behavioral variant FTLD with ALS had a family history of the disease suggestive of autosomal dominant inheritance with incomplete penetrance. Genetic studies in this patient demonstrated the presence of an amplified hexanucleotide repeat (>30) polymorphism in the chromosome 9 open reading frame 72 (C9ORF72) gene which was previously identified as a cause of FTLD. Five others unaffected from the family were negative (all had less than 11 repeats). Because of the clinical and pathological overlap between FTLD and AD we performed a larger genome-wide association study and did not find association of single nucleotide polymorphisms (SNPs) in the C9ORF72 gene with Alzheimer's disease (AD) risk. Bioinformatic analysis of C9ORF72 using the Gene Expression Omnibus database showed expression differences in patients with muscular dystrophy, neural tube defects, and schizophrenia. We also report analysis of gene expression in brain regions using the Allen Human Brain Atlas. Defects in this recently reported gene are now believed to be the most common cause of inherited ALS and an important cause of inherited FTLD. Our work suggests that the gene may also be important in other neurological and psychiatric conditions.

Replication of genome-wide association studies on asthma and allergic diseases in Korean adult population

Allergic diseases such as asthma, allergic rhinitis, and atopic dermatitis are heterogeneous diseases characterized by multiple symptoms and phenotypes. Recent advancements in genetic study enabled us to identify disease associated genetic factors. Numerous genome-wide association studies (GWAS) have revealed multiple associated loci for allergic diseases. However, the majority of previous studies have been conducted in populations of European ancestry. Moreover, the associations of single nucleotide polymorphisms (SNPs) with allergic diseases have not been studied amongst the large-scale general Korean population. Herein, we performed the replication study to validate the previous variants, known to be associated with allergic diseases, in the Korean population. In this study, we categorized three allergic related phenotypes, one allergy and two asthma related phenotypes, based on self-reports of physician diagnosis and their symptoms from 8,842 samples. As a result, we found nominally significant associations of 6 SNPs with at least one allergic related phenotype in the Korean population.

SNP set association analysis for familial data

Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

HYST: a hybrid set-based test for genome-wide association studies, with application to protein-protein interaction-based association analysis

The extended Simes' test (known as GATES) and scaled chi-square test were proposed to combine a set of dependent genome-wide association signals at multiple single-nucleotide polymorphisms (SNPs) for assessing the overall significance of association at the gene or pathway levels. The two tests use different strategies to combine association p values and can outperform each other when the number of and linkage disequilibrium between SNPs vary. In this paper, we introduce a hybrid set-based test (HYST) combining the two tests for genome-wide association studies (GWASs). We describe how HYST can be used to evaluate statistical significance for association at the protein-protein interaction (PPI) level in order to increase power for detecting disease-susceptibility genes of moderate effect size. Computer simulations demonstrated that HYST had a reasonable type 1 error rate and was generally more powerful than its parents and other alternative tests to detect a PPI pair where both genes are associated with the disease of interest. We applied the method to three complex disease GWAS data sets in the public domain; the method detected a number of highly connected significant PPI pairs involving multiple confirmed disease-susceptibility genes not found in the SNP- and gene-based association analyses. These results indicate that HYST can be effectively used to examine a collection of predefined SNP sets based on prior biological knowledge for revealing additional disease-predisposing genes of modest effects in GWASs.

Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways

Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have increased the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin concentration showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional analysis of these newly discovered loci will further improve our understanding of glycemic control.

George-Hyslop P, Seaman MN, Farrer LA. Identification of Alzheimer disease-associated variants in genes that regulate retromer function

The proteolytic processing of amyloid precursor protein (APP) to generate the neurotoxic amyloid β (Aβ) peptide is central to the pathogenesis of Alzheimer disease (AD). The endocytic system mediates the processing of APP by controlling its access to secretases that cleave APP. A key mediator of APP localization is SorL1-a membrane protein that has been genetically linked to AD. The retromer complex is a conserved protein complex required for endosome-to-Golgi retrieval of a number of physiologically important membrane proteins including SorL1. Based on the prior suggestion that endocytosis and retromer sorting pathways might be involved, we hypothesized that variants in other genes in this pathway might also modulate AD risk. Genetic association of AD with 451 polymorphisms in 15 genes encoding retromer or retromer-associated proteins was tested in a Caucasian sample of 8309 AD cases and 7366 cognitively normal elders using individual single nucleotide polymorphism (SNP)- and gene-based tests. We obtained significant evidence of association with KIAA1033 (VEGAS p = 0.025), SNX1 (VEGAS p = 0.035), SNX3 (p = 0.0057), and RAB7A (VEGAS p = 0.018). Ten KIAA1033 SNPs were also significantly associated with AD in a group of African Americans (513 AD cases, 504 control subjects). Findings with four significant SNX3 SNPs in the discovery sample were replicated in a community-based sample of Israeli-Arabs (124 AD cases, 142 control subjects). We show that Snx3 and Rab7A proteins interact with the cargo-selective retromer complex through independent mechanisms to regulate the membrane association of retromer and thereby are key mediators of retromer function. These data implicate additional AD risk genes in the retromer pathway and formally demonstrate a direct link between the activity of the retromer complex and the pathogenesis of AD.

Genome-wide association uncovers shared genetic effects among personality traits and mood states

Measures of personality and psychological distress are correlated and exhibit genetic covariance. We conducted univariate genome-wide SNP (~2.5 million) and gene-based association analyses of these traits and examined the overlap in results across traits, including a prediction analysis of mood states using genetic polygenic scores for personality. Measures of neuroticism, extraversion, and symptoms of anxiety, depression, and general psychological distress were collected in eight European cohorts (n ranged 546-1,338; maximum total n = 6,268) whose mean age ranged from 55 to 79 years. Meta-analysis of the cohort results was performed, with follow-up associations of the top SNPs and genes investigated in independent cohorts (n = 527-6,032). Suggestive association (P = 8 × 10(-8)) of rs1079196 in the FHIT gene was observed with symptoms of anxiety. Other notable associations (P < 6.09 × 10(-6)) included SNPs in five genes for neuroticism (LCE3C, POLR3A, LMAN1L, ULK3, SCAMP2), KIAA0802 for extraversion, and NOS1 for general psychological distress. An association between symptoms of depression and rs7582472 (near to MGAT5 and NCKAP5) was replicated in two independent samples, but other replication findings were less consistent. Gene-based tests identified a significant locus on chromosome 15 (spanning five genes) associated with neuroticism which replicated (P < 0.05) in an independent cohort. Support for common genetic effects among personality and mood (particularly neuroticism and depressive symptoms) was found in terms of SNP association overlap and polygenic score prediction. The variance explained by individual SNPs was very small (up to 1%) confirming that there are no moderate/large effects of common SNPs on personality and related traits.

Evaluating SKI as a candidate gene for non-syndromic cleft lip with or without cleft palate

Non-syndromic cleft lip with or without cleft palate (NSCL/P) is one of the most common of all congenital malformations and has a multifactorial etiology. Findings in mice suggest that the v-ski sarcoma viral oncogene homolog (SKI) gene is a candidate gene for orofacial clefting. In humans, a significant association between rs2843159 within SKI and NSCL/P has been reported in patients from the Philippines and South America. In the South American patients, the association was driven by the subgroup of patients with non-syndromic cleft lip only (NSCLO). Here we investigated the association with rs2843159 in a Mayan Mesoamerican population (172 NSCL/P patients and 366 controls). In addition, we analyzed the phenotypic subgroups NSCLO and non-syndromic cleft of lip and palate (NSCLP). A trend towards association between rs2843159 and NSCL/P was observed in the Mayan cohort (P = 0.097), and we found a stronger association in the NSCLP subgroup (P = 0.072) despite a limited sample size. To investigate whether other common variants within the SKI gene contribute to NSCL/P susceptibility in European and Asian populations, we also analyzed genotypic data from two recent genome-wide association studies using set-based statistical approaches. These analyses detected a trend toward association in the European population. Our data provide limited support for the hypothesis that common SKI variants are susceptibility factors for NSCL/P.

Common genetic variants and gene-expression changes associated with bipolar disorder are over-represented in brain signaling pathway genes

BACKGROUND

Despite high heritability, the genetic variants influencing bipolar disorder (BD) susceptibility remain largely unknown. Low statistical power to detect the small effect-size alleles believed to underlie much of the genetic risk and possible heterogeneity between cohorts are an increasing concern. Integrative biology approaches might offer advantages over genetic analysis alone by combining different genomic datasets at the higher level of biological processes rather than the level of specific genetic variants or genes. We employed this strategy to identify biological processes involved in BD etiopathology.

METHOD

Three genome-wide association studies and a brain gene-expression study were combined with the Human Protein Reference Database protein-protein interaction network data. We used bioinformatic analysis to search for biological networks with evidence of association on the basis of enrichment among both genetic and differential-expression associations with BD.

RESULTS

We identified association with gene networks involved in transmission of nerve impulse, Wnt, and Notch signaling. Three features stand out among these genes: 1) they localized to the human postsynaptic density, which is crucial for neuronal function; 2) their mouse knockouts present altered behavioral phenotypes; and 3) some are known targets of the pharmacological treatments for BD.

CONCLUSIONS

Genetic and gene-expression associations of BD cluster in discrete regions of the protein-protein interaction network. We found replicated evidence for association for networks involving several interlinked signaling pathways. These genes are promising candidates to generate animal models and pharmacological interventions. Our results demonstrate the potential advantage of integrative biology analyses of BD datasets.

Genome-wide gene-environment interaction analysis for asbestos exposure in lung cancer susceptibility

Asbestos exposure is a known risk factor for lung cancer. Although recent genome-wide association studies (GWASs) have identified some novel loci for lung cancer risk, few addressed genome-wide gene-environment interactions. To determine gene-asbestos interactions in lung cancer risk, we conducted genome-wide gene-environment interaction analyses at levels of single nucleotide polymorphisms (SNPs), genes and pathways, using our published Texas lung cancer GWAS dataset. This dataset included 317 498 SNPs from 1154 lung cancer cases and 1137 cancer-free controls. The initial SNP-level P-values for interactions between genetic variants and self-reported asbestos exposure were estimated by unconditional logistic regression models with adjustment for age, sex, smoking status and pack-years. The P-value for the most significant SNP rs13383928 was 2.17×10(-6), which did not reach the genome-wide statistical significance. Using a versatile gene-based test approach, we found that the top significant gene was C7orf54, located on 7q32.1 (P = 8.90×10(-5)). Interestingly, most of the other significant genes were located on 11q13. When we used an improved gene-set-enrichment analysis approach, we found that the Fas signaling pathway and the antigen processing and presentation pathway were most significant (nominal P < 0.001; false discovery rate < 0.05) among 250 pathways containing 17 572 genes. We believe that our analysis is a pilot study that first describes the gene-asbestos interaction in lung cancer risk at levels of SNPs, genes and pathways. Our findings suggest that immune function regulation-related pathways may be mechanistically involved in asbestos-associated lung cancer risk.

Epistasis network centrality analysis yields pathway replication across two GWAS cohorts for bipolar disorder

Most pathway and gene-set enrichment methods prioritize genes by their main effect and do not account for variation due to interactions in the pathway. A portion of the presumed missing heritability in genome-wide association studies (GWAS) may be accounted for through gene-gene interactions and additive genetic variability. In this study, we prioritize genes for pathway enrichment in GWAS of bipolar disorder (BD) by aggregating gene-gene interaction information with main effect associations through a machine learning (evaporative cooling) feature selection and epistasis network centrality analysis. We validate this approach in a two-stage (discovery/replication) pathway analysis of GWAS of BD. The discovery cohort comes from the Wellcome Trust Case Control Consortium (WTCCC) GWAS of BD, and the replication cohort comes from the National Institute of Mental Health (NIMH) GWAS of BD in European Ancestry individuals. Epistasis network centrality yields replicated enrichment of Cadherin signaling pathway, whose genes have been hypothesized to have an important role in BD pathophysiology but have not demonstrated enrichment in previous analysis. Other enriched pathways include Wnt signaling, circadian rhythm pathway, axon guidance and neuroactive ligand-receptor interaction. In addition to pathway enrichment, the collective network approach elevates the importance of ANK3, DGKH and ODZ4 for BD susceptibility in the WTCCC GWAS, despite their weak single-locus effect in the data. These results provide evidence that numerous small interactions among common alleles may contribute to the diathesis for BD and demonstrate the importance of including information from the network of gene-gene interactions as well as main effects when prioritizing genes for pathway analysis.

A Bayesian method for comparing and combining binary classifiers in the absence of a gold standard

Background

Many problems in bioinformatics involve classification based on features such as sequence, structure or morphology. Given multiple classifiers, two crucial questions arise: how does their performance compare, and how can they best be combined to produce a better classifier? A classifier can be evaluated in terms of sensitivity and specificity using benchmark, or gold standard, data, that is, data for which the true classification is known. However, a gold standard is not always available. Here we demonstrate that a Bayesian model for comparing medical diagnostics without a gold standard can be successfully applied in the bioinformatics domain, to genomic scale data sets. We present a new implementation, which unlike previous implementations is applicable to any number of classifiers. We apply this model, for the first time, to the problem of finding the globally optimal logical combination of classifiers.

Results

We compared three classifiers of protein subcellular localisation, and evaluated our estimates of sensitivity and specificity against estimates obtained using a gold standard. The method overestimated sensitivity and specificity with only a small discrepancy, and correctly ranked the classifiers. Diagnostic tests for swine flu were then compared on a small data set. Lastly, classifiers for a genome-wide association study of macular degeneration with 541094 SNPs were analysed. In all cases, run times were feasible, and results precise. The optimal logical combination of classifiers was also determined for all three data sets. Code and data are available from http://bioinformatics.monash.edu.au/downloads/.

Conclusions

The examples demonstrate the methods are suitable for both small and large data sets, applicable to the wide range of bioinformatics classification problems, and robust to dependence between classifiers. In all three test cases, the globally optimal logical combination of the classifiers was found to be their union, according to three out of four ranking criteria. We propose as a general rule of thumb that the union of classifiers will be close to optimal.

The genetics of addiction-a translational perspective

Addictions are serious and common psychiatric disorders, and are among the leading contributors to preventable death. This selective review outlines and highlights the need for a multi-method translational approach to genetic studies of these important conditions, including both licit (alcohol, nicotine) and illicit (cannabis, cocaine, opiates) drug addictions and the behavioral addiction of disordered gambling. First, we review existing knowledge from twin studies that indicates both the substantial heritability of substance-specific addictions and the genetic overlap across addiction to different substances. Next, we discuss the limited number of candidate genes which have shown consistent replication, and the implications of emerging genomewide association findings for the genetic architecture of addictions. Finally, we review the utility of extensions to existing methods such as novel phenotyping, including the use of endophenotypes, biomarkers and neuroimaging outcomes; emerging methods for identifying alternative sources of genetic variation and accompanying statistical methodologies to interpret them; the role of gene-environment interplay; and importantly, the potential role of genetic variation in suggesting new alternatives for treatment of addictions.

Genetic associations for activated partial thromboplastin time and prothrombin time, their gene expression profiles, and risk of coronary artery disease

Activated partial thromboplastin time (aPTT) and prothrombin time (PT) are clinical tests commonly used to screen for coagulation-factor deficiencies. One genome-wide association study (GWAS) has been reported previously for aPTT, but no GWAS has been reported for PT. We conducted a GWAS and meta-analysis to identify genetic loci for aPTT and PT. The GWAS for aPTT was conducted in 9,240 individuals of European ancestry from the Atherosclerosis Risk in Communities (ARIC) study, and the GWAS for PT was conducted in 2,583 participants from the Genetic Study of Three Population Microisolates in South Tyrol (MICROS) and the Lothian Birth Cohorts (LBC) of 1921 and 1936. Replication was assessed in 1,041 to 3,467 individuals. For aPTT, previously reported associations with KNG1, HRG, F11, F12, and ABO were confirmed. A second independent association in ABO was identified and replicated (rs8176704, p = 4.26 × 10(-24)). Pooling the ARIC and replication data yielded two additional loci in F5 (rs6028, p = 3.22 × 10(-9)) and AGBL1 (rs2469184, p = 3.61 × 10(-8)). For PT, significant associations were identified and confirmed in F7 (rs561241, p = 3.71 × 10(-56)) and PROCR/EDEM2 (rs2295888, p = 5.25 × 10(-13)). Assessment of existing gene expression and coronary artery disease (CAD) databases identified associations of five of the GWAS loci with altered gene expression and two with CAD. In summary, eight genetic loci that account for ∼29% of the variance in aPTT and two loci that account for ∼14% of the variance in PT were detected and supported by functional data.

A genome-wide association study of caffeine-related sleep disturbance: confirmation of a role for a common variant in the adenosine receptor

OBJECTIVES

To identify common genetic variants that predispose to caffeine-induced insomnia and to test whether genes whose expression changes in the presence of caffeine are enriched for association with caffeine-induced insomnia.

DESIGN

A hypothesis-free, genome-wide association study.

SETTING

Community-based sample of Australian twins from the Australian Twin Registry.

PARTICIPANTS

After removal of individuals who said that they do not drink coffee, a total of 2,402 individuals from 1,470 families in the Australian Twin Registry provided both phenotype and genotype information.

MEASUREMENTS AND RESULTS

A dichotomized scale based on whether participants reported ever or never experiencing caffeine-induced insomnia. A factor score based on responses to a number of questions regarding normal sleep habits was included as a covariate in the analysis. More than 2 million common single nucleotide polymorphisms (SNPs) were tested for association with caffeine-induced insomnia. No SNPs reached the genome-wide significance threshold. In the analysis that did not include the insomnia factor score as a covariate, the most significant SNP identified was an intronic SNP in the PRIMA1 gene (P = 1.4 × 10⁻⁶, odds ratio = 0.68 [0.53 - 0.89]). An intergenic SNP near the GBP4 gene on chromosome 1 was the most significant upon inclusion of the insomnia factor score into the model (P = 1.9 × 10⁻⁶, odds ratio = 0.70 [0.62 - 0.78]). A previously identified association with a polymorphism in the ADORA2A gene was replicated.

CONCLUSIONS

Several genes have been identified in the study as potentially influencing caffeine-induced insomnia. They will require replication in another sample. The results may have implications for understanding the biologic mechanisms underlying insomnia.

GWAS reveals new recessive loci associated with non-syndromic facial clefting

We have applied a GWAS to 40 consanguineous families segregating cases of non-syndromic cleft lip with or without cleft palate (NS CL/P) (a total of 160 affected and unaffected individuals) in order to trace potential recessive loci that confer susceptibility to this common facial malformation. Pedigree-based association test (PBAT) analyses reported nominal evidence of association and linkage over SNP markers located at 11q25 (rs4937877, P = 2.7 × 10(-6)), 19p12 (rs4324267, P = 1.6 × 10(-5)), 5q14.1 (rs4588572, P-value = 3.36 × 10(-5)), and 15q21.1 (rs4774497, P = 1.08 × 10(-4)). Using the Versatile Gene-Based Association Study to complement the PBAT results, we found clusters of markers located at chromosomes 19p12, 11q25, and 8p23.2 overcome the threshold for GWAS significance (P < 1 × 10(-7)). From this study, new recessive loci implicated in NS CL/P include: B3GAT1, GLB1L2, ZNF431, ZNF714, and CSMD1, even though the functional association with the genesis of NS CL/P remains to be elucidated. These results emphasize the importance of using homogeneous populations, phenotypes, and family structures for GWAS combined with gene-based association analyses, and should encourage. other researchers to evaluate these genes on independent patient samples affected by NS CL/P.

Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation

The ability to predict how an individual patient will respond to a particular treatment is the ambitious goal of personalized medicine. The genetic make up of an individual has been shown to play a role in drug response. For pharmacogenomic studies, human lymphoblastoid cell lines (LCLs) comprise a useful model system for identifying genetic variants associated with pharmacologic phenotypes. The availability of extensive genotype data for many panels of LCLs derived from individuals of diverse ancestry allows for the study of genetic variants contributing to interethnic and interindividual variation in susceptibility to drugs. Many genome-wide association studies for drug-induced phenotypes have been performed in LCLs, often incorporating gene-expression data. LCLs are also being used in follow-up studies to clinical findings to determine how an associated variant functions to affect phenotype. This review describes the most recent pharmacogenomic findings made in LCLs, including the translation of some findings to clinical cohorts.

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

BACKGROUND

Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.

RESULTS

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.

CONCLUSIONS

Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

The polymorphisms and haplotypes of WWOX gene are associated with the risk of lung cancer in southern and eastern Chinese populations

The WW domain-containing oxidoreductase (WWOX) gene is an identified tumor suppressor gene, of which several single nucleotide polymorphisms have been reported to contribute to cancer susceptibility. We hypothesized that genetic variations in WWOX are associated with lung cancer risk. In two independent case-control studies conducted in southern and eastern Chinese, we genotyped five tagSNPs of WWOX gene (rs10220974C > T, rs3764340C > G, rs12918952G > A, rs383362G > T, and rs12828G > A) in 1,559 lung cancer cases and 1,679 controls. Logistic regression analysis showed that two tagSNPs (rs3764340C > G; rs383362G > T) were significantly associated with lung cancer risk in dominant model (rs3764340C > G, GC/GG vs. CC: adjust OR = 1.35, 95% CI = 1.11-1.65; rs383362G > T, TG + TT vs. GG: adjust OR = 1.33, 95% CI = 1.14-1.55). The haplotype analysis further shown that the haplotype "G-T" was associated with the highest increased risk of lung cancer (OR = 2.20; 95% CI = 1.43-3.37). After combined these two loci, the number of the risk genotypes was associated with increased cancer risk in a dose-response manner (Ptrend  = 3.16 × 10(-6) ). In addition, a gene-based association analysis by using VEGAS software suggested the WWOX as a susceptible gene for lung cancer (P = 0.009). However, for rs10220974C > T, rs12918952G > A, and rs12828G > A, no significant association was observed for lung cancer risk. Taken together, our data suggested that genetic variants in WWOX may be genetic biomarkers for susceptibility to lung cancer.

Adrenergic alpha-1 pathway is associated with hypertension among Nigerians in a pathway-focused analysis

Background

The pathway-focused association approach offers a hypothesis driven alternative to the agnostic genome-wide association study. Here we apply the pathway-focused approach to an association study of hypertension, systolic blood pressure (SBP), and diastolic blood pressure (DBP) in 1614 Nigerians with genome-wide data.

Methods and Results

Testing of 28 pathways with biological relevance to hypertension, selected a priori, containing a total of 101 unique genes and 4,349 unique single-nucleotide polymorphisms (SNPs) showed an association for the adrenergic alpha 1 (ADRA1) receptor pathway with hypertension (p<0.0009) and diastolic blood pressure (p<0.0007). Within the ADRA1 pathway, the genes PNMT (hypertension P_gene<0.004, DBP P_gene<0.004, and SBP P_gene<0.009, and ADRA1B (hypertension P_gene<0.005, DBP P_gene<0.02, and SBP P_gene<0.02) displayed the strongest associations. Neither ADRA1B nor PNMT could be the sole mediator of the observed pathway association as the ADRA1 pathway remained significant after removing ADRA1B, and other pathways involving PNMT did not reach pathway significance.

Conclusions

We conclude that multiple variants in several genes in the ADRA1 pathway led to associations with hypertension and DBP. SNPs in ADRA1B and PNMT have not previously been linked to hypertension in a genome-wide association study, but both genes have shown associations with hypertension through linkage or model organism studies. The identification of moderately significant (10⁻²>p>10⁻⁵) SNPs offers a novel method for detecting the “missing heritability” of hypertension. These findings warrant further studies in similar and other populations to assess the generalizability of our results, and illustrate the potential of the pathway-focused approach to investigate genetic variation in hypertension.

Systematic association analysis of microRNA machinery genes with schizophrenia informs further study

microRNAs (miRNAs) play a vital role in development via the post-transcriptional regulation of most genes. Variation in the miRNA machinery pathway proteins which mediate the biogenesis, maturation, transportation, and functioning of miRNAs might be relevant to human traits. In this work, we explored the role of 59 miRNA machinery genes in schizophrenia (SZ). Association analysis of 967 single nucleotide polymorphisms within these genes detected that an intronic polymorphism of EIF4ENIF1, rs7289941, was significantly associated with SZ (P=4.10E-5). We failed to replicate this result in a validation sample comprising 1027 healthy controls and 1012 SZ cases, and the combined data yielded nominal significance (P=0.013). We conducted a gene-based association analysis using VEGAS and SKAT, and found seven associated genes in total, including EIF4ENIF1, PIWIL2, and DGCR8, but none survived correction for multiple testing. Taken together, our data do not provide strong support for the association of common variants within miRNA machinery genes with SZ in the Han Chinese population, but implicate several promising candidate genes for further research.

No association of candidate genes with cannabis use in a large sample of Australian twin families

While there is solid evidence that cannabis use is heritable, attempts to identify genetic influences at the molecular level have yielded mixed results. Here, a large twin family sample (n = 7452) was used to test for association between 10 previously reported candidate genes and lifetime frequency of cannabis use using a gene-based association test. None of the candidate genes reached even nominal significance (P < 0.05). The lack of replication may point to our limited understanding of the neurobiology of cannabis involvement and also to potential publication bias and false-positive findings in previous studies.