Effect of population stratification on case-control association studies. I. Elevation in false positive rates and comparison to confounding risk ratios (a simulation study)

Huntingtin gene CAG repeat size affects autism risk: Family-based and case-control association study

The Huntingtin (HTT) gene contains a CAG repeat in exon 1, whose expansion beyond 39 repeats consistently leads to Huntington's disease (HD), whereas normal-to-intermediate alleles seemingly modulate brain structure, function and behavior. The role of the CAG repeat in Autism Spectrum Disorder (ASD) was investigated applying both family-based and case-control association designs, with the SCA3 repeat as a negative control. Significant overtransmission of "long" CAG alleles (≥17 repeats) to autistic children and of "short" alleles (≤16 repeats) to their unaffected siblings (all p < 10^-5 ) was observed in 612 ASD families (548 simplex and 64 multiplex). Surprisingly, both 193 population controls and 1,188 neurological non-HD controls have significantly lower frequencies of "short" CAG alleles compared to 185 unaffected siblings and higher rates of "long" alleles compared to 548 ASD patients from the same families (p < .05-.001). The SCA3 CAG repeat displays no association. "Short" HTT alleles seemingly exert a protective effect from clinically overt autism in families carrying a genetic predisposition for ASD, while "long" alleles may enhance autism risk. Differential penetrance of autism-inducing genetic/epigenetic variants may imply atypical developmental trajectories linked to HTT functions, including excitation/inhibition imbalance, cortical neurogenesis and apoptosis, neuronal migration, synapse formation, connectivity and homeostasis.

Aquaporin locus (12q13.12) might contribute to susceptibility of temporomandibular joint disorder associated with periodontitis

Aquaporins (AQPs) are membrane channels that provide for transport of water and other small molecules across the lipid bilayer of cells. Their function is essential for physiologic processes such as cell volume regulation, chondrocyte hypertrophy during appendicular skeletal growth, water reabsorption in the kidney tubules, and water excretion by the salivary glands. The ten AQP isoforms show tissue specificity and are involved in different pathologies and inflammatory diseases. This study addresses the hypothesis that arthritis, periodontitis, and temporomandibular joint disorders (TMDs) can be influenced by variation in the AQP genes at 12q13.12 locus. Salivary samples of 688 individuals were obtained from the Dental Registry and DNA Repository project at the University of Pittsburgh. Ten polymorphisms in four AQP genes (AQP1, 2, 5, and 6) were genotyped and correlated to disease status as reported by patients. Associations were found between the single nucleotide polymorphism (SNP) rs467323 in AQP2 and TMD in both genotypic (p = 0.03) and recessive (p = 0.02) models, and between rs1996315 in AQP6 and periodontitis (p = 0.05). Combined analysis of TMD and periodontitis showed an association with rs3741559 in AQP2 (p = 0.02). When conducting haplotype analysis of rs467323 and rs10875989 in AQP2, the haplotype CT showed an association with the TMD phenotype (p = 0.007). Our results suggest that the aquaporin locus at 12q13.12 may contribute to the pathogenesis of inflammatory conditions such as periodontitis and TMD. Thus, oral and skeletal health are correlated and potential susceptibility screening strategies may be developed.

Carnitine palmitoyltransferase 1A P479L and infant death: policy implications of emerging data

Carnitine palmitoyltransferase 1 isoform A (CPT1A) is a crucial enzyme for the transport of long-chain fatty acids into the mitochondria. The CPT1A p.P479L variant is found in high frequencies among indigenous populations residing on the west and north coasts of Alaska and Canada and in northeast Siberia and Greenland. Epidemiological studies have reported a statistical association between P479L homozygosity and infant death in Alaska Native and Canadian Inuit populations. Here, we review the available evidence about the P479L variant and apply to these data the epidemiological criteria for assessing causal associations. We found insufficient evidence to support a causal association with infant death and, further, that if a causal association is present, then the genotype is likely to be only one of a complex set of factors contributing to an increased risk of infant death. We conclude that additional research is needed to clarify the observed association and to inform effective preventative measures for infant death. In light of these findings, we discuss the policy implications for public health efforts because policies based on the observed association between P479L homozygosity and infant death data are premature.Genet Med advance online publication 26 January 2017.

Genetics and psychotic disorders: A fresh look at consanguinity

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

N-Acetyltransferase 2 Genotypes Are Associated With Diisocyanate-Induced Asthma

OBJECTIVE

To investigate whether genetic variants of N-acetyltransferase (NAT) genes are associated with diisocyanate asthma (DA).

METHODS

The study population consisted of 354 diisocyanate-exposed workers. Genotyping was performed using a 5'-nuclease polymerase chain reaction assay.

RESULTS

The NAT2 rs2410556 and NAT2 rs4271002 variants were significantly associated with DA in the univariate analysis. In the first logistic regression model comparing DA+ and asymptomatic worker groups, the rs2410556 (P = 0.004) and rs4271002 (P < 0.001) single nucleotide polymorphisms and the genotype combination, NAT2 rs4271002*NAT1 rs11777998, showed associations with DA risk (P = 0.014). In the second model comparing DA+ and DA- groups, NAT2 rs4271002 variant and the combined genotype, NAT1 rs8190845*NAT2 rs13277605, were significantly associated with DA risk (P = 0.022, P = 0.036, respectively).

CONCLUSIONS

These findings suggest that variations in the NAT2 gene and their interactions contribute to DA susceptibility.

Genetic variants in TNFα, TGFB1, PTGS1 and PTGS2 genes are associated with diisocyanate-induced asthma

Diisocyanates are the most common cause of occupational asthma, but risk factors are not well defined. A case-control study was conducted to investigate whether genetic variants in inflammatory response genes (TNFα, IL1α, IL1β, IL1RN, IL10, TGFB1, ADAM33, ALOX-5, PTGS1, PTGS2 and NAG-1/GDF15) are associated with increased susceptibility to diisocyanate asthma (DA). These genes were selected based on their role in asthmatic inflammatory processes and previously reported associations with asthma phenotypes. The main study population consisted of 237 Caucasian French Canadians from among a larger sample of 280 diisocyanate-exposed workers in two groups: workers with specific inhalation challenge (SIC) confirmed DA (DA(+), n = 95) and asymptomatic exposed workers (AW, n = 142). Genotyping was performed on genomic DNA, using a 5' nuclease PCR assay. After adjusting for potentially confounding variables of age, smoking status and duration of exposure, the PTGS1 rs5788 and TGFB1 rs1800469 single nucleotide polymorphisms (SNP) showed a protective effect under a dominant model (OR = 0.38; 95% CI = 0.17, 0.89 and OR = 0.38; 95% CI = 0.18, 0.74, respectively) while the TNFα rs1800629 SNP was associated with an increased risk of DA (OR = 2.08; 95% CI = 1.03, 4.17). Additionally, the PTGS2 rs20417 variant showed an association with increased risk of DA in a recessive genetic model (OR = 6.40; 95% CI = 1.06, 38.75). These results suggest that genetic variations in TNFα, TGFB1, PTGS1 and PTGS2 genes contribute to DA susceptibility.

Challenges of SNP genotyping and genetic variation: its future role in diagnosis and treatment of cancer

Thorough annotation of common germline genetic variation in the human genome has generated a foundation for the investigation of the contribution of genetics to the etiology and pathogenesis of cancer. For many malignancies, it has become increasingly apparent that numerous alleles, with small-to-moderate effects, additively contribute to cancer susceptibility. The most common genetic variant in the genome, the single nucleotide polymorphism, is of special interest for the study of susceptibility to and protection from cancer. Similarly, intense effort has focused on genetic variants that can predict either response or toxicity to therapeutic interventions. This review discusses the challenges and prospects of genetic association studies in cancer research. On the basis of recent changes in genomics and high-throughput genotyping platforms, future genetic findings of association studies could impact clinical care and public health screening.

Genetic variants in antioxidant genes are associated with diisocyanate-induced asthma

Diisocyanates are a common cause of occupational asthma, but risk factors are not well defined. A case-control study was conducted to investigate whether genetic variants of antioxidant defense genes, glutathione S-transferases (GSTM1, GSTT1, GSTM3, GSTP1), manganese superoxide dismutase (SOD2), and microsomal epoxide hydrolase (EPHX1) are associated with increased susceptibility to diisocyanate-induced asthma (DA). The main study population consisted of 353 Caucasian French-Canadians from among a larger sample of 410 diisocyanate-exposed workers in three groups: workers with specific inhalation challenge (SIC) confirmed DA (DA(+), n = 95); symptomatic diisocyanate workers with a negative SIC (DA(-), n = 116); and asymptomatic exposed workers (AW, n = 142). Genotyping was performed on genomic DNA, using a 5'-nuclease PCR assay. The SOD2 rs4880, GSTP1 rs1695, and EPHX1 rs2740171 variants were significantly associated with DA in both univariate and multivariate analyses. In the first logistic regression model comparing DA(+) and DA(-) groups, SOD2 rs4880, GSTM1 (null), GSTP1 rs762803, and EPHX1 rs2854450 variants were associated with DA (p = 0.004, p = 0.047, p = 0.021, p <0.001, respectively). Genotype combinations GSTT1*GSTP1 rs762803, GSTM1*EPHX1 rs2854450, EPHX1 rs2740168*EPHX1 rs1051741, and GSTP1 rs762803*EPHX1 rs2740168 were also associated with DA in this model (p = 0.027, p = 0.002, p = 0.045, p = 0.044, respectively). The GSTP1 rs1695 and EPHX1 rs1051741 and rs2740171 variants showed an association with DA in the second model comparing DA(+) and AW groups (p = 0.040, p = 0.019, p = 0.002, respectively). The GSTM3 rs110913*EPHX1 rs1051741 genotype combination was also associated with DA under this model (p = 0.042). The results suggest that variations in SOD2, GST, and EPHX1 genes and their interactions contribute to DA susceptibility.

Computer simulation is an undervalued tool for genetic analysis: a historical view and presentation of SHIMSHON--a Web-based genetic simulation package

Computer simulation methods are under-used tools in genetic analysis because simulation approaches have been portrayed as inferior to analytic methods. Even when simulation is used, its advantages are not fully exploited. Here, I present SHIMSHON, our package of genetic simulation programs that have been developed, tested, used for research, and used to generated data for Genetic Analysis Workshops (GAW). These simulation programs, now web-accessible, can be used by anyone to answer questions about designing and analyzing genetic disease studies for locus identification. This work has three foci: (1) the historical context of SHIMSHON's development, suggesting why simulation has not been more widely used so far. (2) Advantages of simulation: computer simulation helps us to understand how genetic analysis methods work. It has advantages for understanding disease inheritance and methods for gene searches. Furthermore, simulation methods can be used to answer fundamental questions that either cannot be answered by analytical approaches or cannot even be defined until the problems are identified and studied, using simulation. (3) I argue that, because simulation was not accepted, there was a failure to grasp the meaning of some simulation-based studies of linkage. This may have contributed to perceived weaknesses in linkage analysis; weaknesses that did not, in fact, exist.

Accounting for population stratification in practice: a comparison of the main strategies dedicated to genome-wide association studies

Genome-Wide Association Studies are powerful tools to detect genetic variants associated with diseases. Their results have, however, been questioned, in part because of the bias induced by population stratification. This is a consequence of systematic differences in allele frequencies due to the difference in sample ancestries that can lead to both false positive or false negative findings. Many strategies are available to account for stratification but their performances differ, for instance according to the type of population structure, the disease susceptibility locus minor allele frequency, the degree of sampling imbalanced, or the sample size. We focus on the type of population structure and propose a comparison of the most commonly used methods to deal with stratification that are the Genomic Control, Principal Component based methods such as implemented in Eigenstrat, adjusted Regressions and Meta-Analyses strategies. Our assessment of the methods is based on a large simulation study, involving several scenarios corresponding to many types of population structures. We focused on both false positive rate and power to determine which methods perform the best. Our analysis showed that if there is no population structure, none of the tests led to a bias nor decreased the power except for the Meta-Analyses. When the population is stratified, adjusted Logistic Regressions and Eigenstrat are the best solutions to account for stratification even though only the Logistic Regressions are able to constantly maintain correct false positive rates. This study provides more details about these methods. Their advantages and limitations in different stratification scenarios are highlighted in order to propose practical guidelines to account for population stratification in Genome-Wide Association Studies.

Differential serum cytokine levels are associated with cytokine gene polymorphisms in north Indians with active pulmonary tuberculosis

Globally only 5-10% of people encountering Mycobacterium tuberculosis have a lifetime risk of active disease indicating a strong host genetic bias towards development of tuberculosis. In the current study we investigated genotype variants pertaining to five cytokine genes namely IFNG, TNFA, IL4, IL10 and IL12 in the north Indian population with active pulmonary tuberculosis (APTB) and correlated the serum cytokine levels with the corresponding genotypes. Twenty five single nucleotide polymorphisms (SNPs) including six loci examined for the first time in tuberculosis were selected for genotyping in 108 patients with APTB from north India and 48 healthy regional controls (HC). Applying exclusion criteria 12 SNPs passed all the filters and were analysed further. The serum cytokine concentrations were measured by ELISA. Compared to HC mean serum IFN-γ, IL-12, IL-4, and IL-10 levels were higher in APTB (p = 0.3661, p = 0.0186, p = 0.003, p = 0.7, respectively). In contrast the mean serum TNF-α level was higher in HC (p = 0.007). Comparison of genotypes and serum levels of the corresponding cytokine genes reveal that though IFN-γ and IL-4 levels were higher in APTB the genotype variants showed no difference between HC and APTB. In contrast the genotypes of the selected rsIDs in the TNFA, IL12 and IL10 genes showed significant association with the varying serum levels of corresponding cytokines. The variant of the TNFA gene at rs3093662, the IL12 gene at rs3213094 and rs3212220 and the IL10 gene at rs3024498 did show a strong indication to be of relevance to the immunity to tuberculosis. To our knowledge this is the first report from this region relating genotypes and serum cytokine levels in north Indian population.

Influence of population stratification on population-based marker-disease association analysis

Population-based genetic association analysis may suffer from the failure to control for confounders such as population stratification (PS). There has been extensive study on the influence of PS on candidate gene-disease association analysis, but much less attention has been paid to its influence on marker-disease association analysis. In this paper, we focus on the Pearson chi(2) test and the trend test for marker-disease association analysis. The mean and variance of the test statistics are derived under presence of PS, so that the power and inflated type I error rate can be evaluated. It is shown that the bias and the variance distortion are not zero in the presence of both PS and penetrance heterogeneity (PH). Unlike candidate gene-disease association analysis, when PS is present, the bias is not zero no matter whether PH is present or not. This work generalises the published results, where only the fully recessive penetrance model is considered and only the bias is calculated. It is shown that candidate gene-disease association analysis can be treated as a special case of marker-disease association analysis. Consequently, our results extend previous studies on candidate gene-disease association analysis. A simulation study confirms the theoretical findings.

Evolution in health and medicine Sackler colloquium: Consanguinity, human evolution, and complex diseases

There is little information on inbreeding during the critical early years of human existence. However, given the small founding group sizes and restricted mate choices it seems inevitable that intrafamilial reproduction occurred and the resultant levels of inbreeding would have been substantial. Currently, couples related as second cousins or closer (F >or= 0.0156) and their progeny account for an estimated 10.4% of the global population. The highest rates of consanguineous marriage occur in north and sub-Saharan Africa, the Middle East, and west, central, and south Asia. In these regions even couples who regard themselves as unrelated may exhibit high levels of homozygosity, because marriage within clan, tribe, caste, or biraderi boundaries has been a long-established tradition. Mortality in first-cousin progeny is approximately 3.5% higher than in nonconsanguineous offspring, although demographic, social, and economic factors can significantly influence the outcome. Improving socioeconomic conditions and better access to health care will impact the effects of consanguinity, with a shift from infant and childhood mortality to extended morbidity. At the same time, a range of primarily social factors, including urbanization, improved female education, and smaller family sizes indicate that the global prevalence of consanguineous unions will decline. This shift in marriage patterns will initially result in decreased homozygosity, accompanied by a reduction in the expression of recessive single-gene disorders. Although the roles of common and rare gene variants in the etiology of complex disease remain contentious, it would be expected that declining consanguinity would also be reflected in reduced prevalence of complex diseases, especially in population isolates.

Measurement of admixture proportions and description of admixture structure in different U.S. populations

Variation in individual admixture proportions leads to heterogeneity within populations. Though novel methods and marker panels have been developed to quantify individual admixture, empirical data describing individual admixture distributions are limited. We investigated variation in individual admixture in four U.S. populations (European American [EA], African American [AA], Hispanics from Connecticut [East Coast, or EC], and Hispanics from California [West Coast, or WC]) assuming three-way intermixture among Europeans, Africans, and Indigenous Americans. Admixture estimates were inferred using a panel of 36 microsatellites and one SNP, which have significant allele frequency differences between ancestral populations, and by using both a maximum likelihood (ML)-based method and a Bayesian method implemented in the program STRUCTURE. Simulation studies showed that estimates obtained with this marker panel are within 96% of expected values. EAs had the lowest non-European admixture with both methods, but showed greater homogeneity with STRUCTURE than with ML. All other samples showed a high degree of variation in admixture estimates with both methods, were highly concordant, and showed evidence of admixture stratification. With both methods, AA subjects had on average, 16% European and <10% Indigenous American admixture. EC Hispanics had higher mean African admixture and the WC Hispanics had higher mean Indigenous American admixture, possibly reflecting their different continental origins.

Genetic ancestry and risk of breast cancer among U.S. Latinas

U.S. Latinas have a lower incidence of breast cancer compared with non-Latina White women. This difference is partially explained by differences in the prevalence of known risk factors. Genetic factors may also contribute to this difference in incidence. Latinas are an admixed population with most of their genetic ancestry from Europeans and Indigenous Americans. We used genetic markers to estimate the ancestry of Latina breast cancer cases and controls and assessed the association with genetic ancestry, adjusting for reproductive and other risk factors. We typed a set of 106 ancestry informative markers in 440 Latina women with breast cancer and 597 Latina controls from the San Francisco Bay area and estimated genetic ancestry using a maximum likelihood method. Odds ratios (OR) and 95% confidence intervals (95% CI) for ancestry modeled as a continuous variable were estimated using logistic regression with known risk factors included as covariates. Higher European ancestry was associated with increased breast cancer risk. The OR for a 25% increase in European ancestry was 1.79 (95% CI, 1.28-2.79; P<0.001). When known risk factors and place of birth were adjusted for, the association with European ancestry was attenuated but remained statistically significant (OR, 1.39; 95% CI, 1.06-2.11; P=0.013). Further work is needed to determine if the association is due to genetic differences between populations or possibly due to environmental factors not measured.

Genomewide association for schizophrenia in the CATIE study: results of stage 1

Little is known for certain about the genetics of schizophrenia. The advent of genomewide association has been widely anticipated as a promising means to identify reproducible DNA sequence variation associated with this important and debilitating disorder. A total of 738 cases with DSM-IV schizophrenia (all participants in the CATIE study) and 733 group-matched controls were genotyped for 492,900 single-nucleotide polymorphisms (SNPs) using the Affymetrix 500K two-chip genotyping platform plus a custom 164K fill-in chip. Following multiple quality control steps for both subjects and SNPs, logistic regression analyses were used to assess the evidence for association of all SNPs with schizophrenia. We identified a number of promising SNPs for follow-up studies, although no SNP or multimarker combination of SNPs achieved genomewide statistical significance. Although a few signals coincided with genomic regions previously implicated in schizophrenia, chance could not be excluded. These data do not provide evidence for the involvement of any genomic region with schizophrenia detectable with moderate sample size. However, a planned genomewide association study for response phenotypes and inclusion of individual phenotype and genotype data from this study in meta-analyses hold promise for eventual identification of susceptibility and protective variants.

Association of RGS2 and RGS5 variants with schizophrenia symptom severity

BACKGROUND

Several lines of evidence indicate that Regulator of G Protein Signaling 4 (RGS4) contributes to schizophrenia vulnerability. RGS4 is one of a family of molecules that modulate signaling via G-protein coupled receptors. Five genes encoding members of this family (RGS2, RGS4, RGS5, RGS8 and RGS16) map to chromosome 1q23.3-1q31. Due to overlapping cellular functions and chromosomal proximity, we hypothesized that multiple RGS genes may contribute to schizophrenia severity and treatment responsiveness.

METHODS

Subjects were 750 individuals with schizophrenia who participated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Inferred ancestries were: 221 (30%) 'Africa only', 422 (56%) 'Europe only' and 107 (14%) 'Other'. Fifty-nine single nucleotide polymorphisms (SNPs) in or near the RGS5, RGS16, RGS8 and RGS2 genes were genotyped. Multiple linear regression was used to analyze association of markers with Positive and Negative Symptoms Scale (PANSS) total scores at baseline and throughout antipsychotic treatment.

RESULTS

RGS5 marker rs10799902 was associated with altered baseline PANSS total score in both the Africa only (P=0.0440) and Europe only (P=0.0143) strata, although neither association survived multiple comparisons correction. A common five-marker haplotype of the RGS2 gene was associated with more severe baseline PANSS total score in the Europe only strata (global P=0.0254; haplotype-specific P=0.0196). In contrast to RGS4, none of the markers showed association with antipsychotic treatment response.

CONCLUSIONS

RGS2 and RGS5 genotypes predicted severity of baseline symptoms in schizophrenia. Although these analyses are exploratory and replication is required, these data suggest a possible role for multiple RGS proteins in schizophrenia.

Ethnic stratification of the association of RGS4 variants with antipsychotic treatment response in schizophrenia

BACKGROUND

Genetic association studies, including a large meta-analysis, report association of regulator of G protein signaling 4 (RGS4) with schizophrenia in the context of heterogeneity. The central role of RGS4 in regulating signaling via Gi/o coupled neurotransmitter receptors led us to hypothesize that there may be RGS4 genotypes predictive of specific disease phenotypes and antipsychotic treatment responses.

METHODS

Subjects were 678 individuals with schizophrenia who participated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE). Among the 678 subjects, the inferred ancestries were 198 (29%) "Africa only," 397 (59%) "Europe only," and 83 (12%) "Other." Eight single nucleotide polymorphisms (SNPs) spanning RGS4 were genotyped. Multiple linear regression was used to analyze association of RGS4 markers with Positive and Negative Symptoms Scale (PANSS) scores at baseline and throughout antipsychotic treatment.

RESULTS

Two consecutive markers within RGS4, rs2661319 and rs2842030, were associated with more severe baseline PANSS total score. Treatment with perphenazine was more effective than treatment with quetiapine (p = .010) or ziprasidone (p = .002) in individuals of inferred African ancestry and homozygous for the rs951439 C allele.

CONCLUSIONS

RGS4 genotypes predicted both the severity of baseline symptoms and relative responsiveness to antipsychotic treatment. Although these analyses are exploratory and replication is required, these data provide support for RGS4 in schizophrenia pathogenesis and suggest a functional role for RGS4 in differential antipsychotic treatment efficacy of schizophrenia.

Common single nucleotide polymorphisms in Japanese patients with essential hypertension: aldehyde dehydrogenase 2 gene as a risk factor independent of alcohol consumption

Essential hypertension (EH) is a multifactorial disorder determined by the interaction of environmental and genetic factors. EH patients' responses to these factors may vary, depending on differences in their genes that determine the physiological systems that mediate the response. The purpose of this investigation was to clarify the contributions of genetic background and lifestyle to EH through an association study using some common single nucleotide polymorphisms (SNPs) that should have functional effects on EH phenotypes. We studied the associations between common SNPs of some causal genes related to EH and lifestyle in a Japanese population. The variants of the causal genes were selected based on their functions, including: obesity (adrenergic, beta-3-, receptor: ADRB3), alcohol consumption (aldehyde dehydrogenase 2: ALDH2), water-electrolyte metabolism (guanine nucleotide binding protein [G protein], beta polypeptide 3: GNB3), glycometabolism (peroxisome proliferator-activated receptor gamma: PPARG), lipometabolism (cholesteryl ester transfer protein, plasma: CETP), atherosclerosis (5,10-methylenetetrahydrofolate reductase [NADPH]: MTHFR), and cellular behavior (gap junction protein, alpha 4, 37 kD: GJA4). Case-control association analysis showed a significant association between EH and both the ALDH2 (Lys487Glu) and GNB3 (C825T) variants. Logistic regression analysis indicated that body mass index (BMI) is an important risk factor for EH, and that the GG (Glu/Glu) genotype of ALDH2 was an independent risk factor for EH overall and especially for EH in males. There was no interaction between the ALDH2 genotype and alcohol consumption overall or in male subjects. Our results suggest that the ALDH2 genotype is associated with EH independently of alcohol consumption.

Non-replication of association studies: "pseudo-failures" to replicate?

Recently, serious doubts have been cast on the usefulness of association studies as a means to genetically dissect complex diseases because most initial findings fail to replicate in subsequent studies. The reasons usually invoked are population stratification, genetic heterogeneity, and inflated Type I errors. In this article, we argue that, even when these problems are addressed, the scientific community usually has unreasonably high expectations on replication success, based on initial low P values, a phenomenon known as the replication fallacy. We present a modified formula that gives the replication power of a second association study based on the P value of an initial study. When both studies have similar sample sizes, this formula shows that: (1) a P value only slightly lower than the nominal alpha results in only approximately 50% replication power; (2) very low P values are required to achieve a replication power of at least 80% (e.g., at alpha = 0.05, a P value of <0.005 is required). Because many initially significant findings result in low replication power, replication failure should not be surprising or be interpreted as necessarily refuting the initial findings. We refer to replication failures for which the replication power is low as "pseudo-failures."

Genetic admixture among Hispanics and candidate gene polymorphisms: potential for confounding in a breast cancer study?

Hispanics in the U.S. Southwest have genetic ancestry from Europeans and from American Indians, two groups with markedly different breast cancer incidence rates. Genetic admixture may therefore bias estimates of associations between candidate cancer susceptibility genes and breast cancer in Hispanics. We estimated genetic admixture using 15 ancestry-informative markers for 1,239 Hispanics and 2,505 non-Hispanic Whites in a breast cancer case-control study in the Southwest, the Four Corners Study. Confounding risk ratios (CRR) were calculated to quantify potential bias due to admixture. Genetic admixture was strongly related to self-reported race and ethnic background (P < 0.0001). Among Hispanic controls, admixture was significantly associated with allele frequency for 5 of 11 candidate gene single nucleotide polymorphisms (SNP) examined. Hispanics in the highest versus the lowest quintile of American Indian admixture had higher mean body mass index at age 30 years (25.4 versus 23.6 kg/m(2); P = 0.003), shorter mean height (1.56 versus 1.58 m; P = 0.01), higher prevalence of diabetes (14.8% versus 7.2%; P = 0.04), and a larger proportion with less than a high school education (38.5% versus 23.2%; P = 0.001). Admixture was not associated with breast cancer risk among Hispanics (P = 0.65). CRRs for potential bias to candidate SNP-breast cancer risk ratios ranged from 0.99 to 1.01. Thus, although genetic admixture in Hispanics was associated with exposures, confounding by admixture was negligible due to the null association between admixture and breast cancer. CRRs from simulated scenarios indicated that appreciable confounding by admixture would occur only when within-group candidate SNP allele frequency differences are much larger than any that we observed.

Non-Replication and Inconsistency in the Genome-Wide Association Setting

Non-replication and inconsistency had been common features in the search for common variants of candidate genes affecting the risk of complex diseases. They may continue to require attention in the current era, when massive hypothesis-free testing of genetic variants is feasible. An empirical evaluation of the early experience with genome-wide association (GWA) studies suggests several examples where proposed associations have failed to be replicated by subsequent investigations. Non-replication and inconsistency is defined here in the framework of cumulative meta-analysis. Ideally, associations exist, GWA finds them, and subsequent investigations should replicate them. However, a number of other possibilities need to be considered. No common genetic variants may associate with the phenotype of interest and GWA may find nothing; or associations may exist, but GWA may miss them. Associations that do not exist may be falsely selected by the GWA and subsequent studies may appropriately refute them or falsely replicate them. Finally, GWA may find true associations that are nevertheless falsely non-replicated in the subsequent studies; or associations may be genuinely inconsistent across study populations. A list of options is presented for consideration in each of these scenarios.

A unified approach for quantifying, testing and correcting population stratification in case-control association studies

The HapMap project has given case-control association studies a unique opportunity to uncover the genetic basis of complex diseases. However, persistent issues in such studies remain the proper quantification of, testing for, and correction for population stratification (PS). In this paper, we present the first unified paradigm that addresses all three fundamental issues within one statistical framework. Our unified approach makes use of an omnibus quantity (delta), which can be estimated in a case-control study from suitable null loci. We show how this estimated value can be used to quantify PS, to statistically test for PS, and to correct for PS, all in the context of case-control studies. Moreover, we provide guidelines for interpreting values of delta in association studies (e.g., at alpha = 0.05, a delta of size 0.416 is small, a delta of size 0.653 is medium, and a delta of size 1.115 is large). A novel feature of our testing procedure is its ability to test for either strictly any PS or only 'practically important' PS. We also performed simulations to compare our correction procedure with Genomic Control (GC). Our results show that, unlike GC, it maintains good Type I error rates and power across all levels of PS.

NCAM1 and neurocognition in schizophrenia

BACKGROUND

Alterations in neurocognition may be fundamental to schizophrenia and may be endophenotypes. Neural cell adhesion molecule 1 (NCAM1, aliases NCAM and CD56) may be a candidate gene for schizophrenia or for neurocognition in schizophrenia as supported by linkage and functional findings.

METHODS

Subjects were 641 patients with schizophrenia who participated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) clinical trial. Neurocognition was assessed at study baseline. Nine NCAM1 single nucleotide polymorphisms (SNPs) were blindly genotyped. Analysis of covariance was used to test for single SNP associations and haplotype regression for multilocus associations.

RESULTS

As there were suggestions of population stratification, all analyses were conducted stratified by inferred ancestry. In the "Europe only" stratum, there were nominally significant associations with five contiguous SNPs (rs1943620, rs1836796, rs1821693, rs686050, rs584427) with the strongest association at rs1836796 (p = .007). Via permutation testing, the probability of obtaining five consecutive statistically significant SNPs with p-values <or= .05 was p = .0044. These results were robust to examination of model assumptions. Haplotype analyses did not identify significant haplotype associations.

CONCLUSIONS

Although it is essential to see if these findings replicate in additional samples, we suggest that NCAM1 deserves further scrutiny for its relevance to clinical and etiological aspects of schizophrenia.

AKT1 and neurocognition in schizophrenia

OBJECTIVE

Previous research has shown conflicting results for the significance of five v-akt murine thymoma viral oncogene homolog 1 (AKT1) single-nucleotide polymorphisms (SNPs) to the aetiology of schizophrenia. Neurocognition is a plausible endophenotype for schizophrenia and it was reasoned that the lack of agreement might be due to variability in neurocognition across studies. Therefore, the association of genetic variation in AKT1 with neurocognition was investigated in patients with schizophrenia.

METHODS

The same five SNPs used in previous studies of the etiology of schizophrenia (rs2494732, rs2498799, rs3730358, rs1130214, [corrected] and rs3803300) were genotyped in 641 individuals with schizophrenia who had participated in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) project. The primary dependent variable was a neurocognitive composite score and exploratory analyses investigated five domain scores (processing speed, reasoning, verbal memory, working memory, and vigilance).

RESULTS

There were no significant asymptotic or empirical associations between any SNP and the neurocognitive composite score. The authors also investigated the association of five-SNP haplotypes with the neurocognitive composite score. A marginally significant association was observed for the neurocognitive composite score with one of the five-SNP haplotypes (global score statistic 19.51, df = 9, permutation p = 0.02). Exploratory analyses of five domain scores (processing speed, reasoning, verbal memory, working memory, and vigilance) were non-significant for all five SNPs.

CONCLUSION

Results published to date for an association between genetic variation in AKT1 with schizophrenia are inconsistent. The results suggest that the AKT1 markers studied are not associated with neurocognition in schizophrenia, and do not support unassessed variation in neurocognitive scores as a reason for this discrepancy.

An evaluation of power and type I error of single-nucleotide polymorphism transmission/disequilibrium-based statistical methods under different family structures, missing parental data, and population stratification

Researchers conducting family-based association studies have a wide variety of transmission/disequilibrium (TD)-based methods to choose from, but few guidelines exist in the selection of a particular method to apply to available data. Using a simulation study design, we compared the power and type I error of eight popular TD-based methods under different family structures, frequencies of missing parental data, genetic models, and population stratifications. No method was uniformly most powerful under all conditions, but type I error was appropriate for nearly every test statistic under all conditions. Power varied widely across methods, with a 46.5% difference in power observed between the most powerful and the least powerful method when 50% of families consisted of an affected sib pair and one parent genotyped under an additive genetic model and a 35.2% difference when 50% of families consisted of a single affection-discordant sibling pair without parental genotypes available under an additive genetic model. Methods were generally robust to population stratification, although some slightly less so than others. The choice of a TD-based test statistic should be dependent on the predominant family structure ascertained, the frequency of missing parental genotypes, and the assumed genetic model.

Centralizing the non-central chi-square: A new method to correct for population stratification in genetic case-control association studies

We present a new method, the delta-centralization (DC) method, to correct for population stratification (PS) in case-control association studies. DC works well even when there is a lot of confounding due to PS. The latter causes overdispersion in the usual chi-square statistics which then have non-central chi-square distributions. Other methods approach the noncentrality indirectly, but we deal with it directly, by estimating the non-centrality parameter tau itself. Specifically: (1) We define a quantity delta, a function of the relevant subpopulation parameters. We show that, for relatively large samples, delta exactly predicts the elevation of the false positive rate due to PS, when there is no true association between marker genotype and disease. (This quantity delta is quite different from Wright's F(ST) and can be large even when F(ST) is small.) (2) We show how to estimate delta, using a panel of unlinked "neutral" loci. (3) We then show that delta2 corresponds to tau the noncentrality parameter of the chi-square distribution. Thus, we can centralize the chi-square using our estimate of 6; this is the DC method. (4) We demonstrate, via computer simulations, that DC works well with as few as 25-30 unlinked markers, where the markers are chosen to have allele frequencies reasonably close (within +/- .1) to those at the test locus. (5) We compare DC with genomic control and show that where as the latter becomes overconservative when there is considerable confounding due to PS (i.e. when delta is large), DC performs well for all values of delta.

Evaluating bias due to population stratification in epidemiologic studies of gene-gene or gene-environment interactions

Confounding by ethnicity (i.e. population stratification) can result in bias and incorrect inferences in genotype-disease association studies, but the effect of population stratification in gene-gene or gene-environment interaction studies has not been addressed. We used logistic regression models to fit multiplicative interactions between two dichotomous variables that represented genetic and/or environmental factors for a binary disease outcome in a hypothetical cohort of multiple ethnicities. Biases in main effects and interactions due to population stratification were evaluated by comparing regression coefficients in mis-specified models that ignored ethnicities with their counterparts in models that accounted for ethnicities. We showed that biases in main effects and interactions were constrained by the differences in disease risks across the ethnicities. Therefore, large biases due to population stratification are not possible when baseline disease risk differences among ethnicities are small or moderate. Numerical examples of biases in genotype-genotype and/or genotype-environment interactions suggested that biases due to population stratification for main effects were generally small but could become large for studies of interactions, particularly when strong linkage disequilibrium between genes or large correlations between genetic and environmental factors existed. However, when linkage disequilibrium among genes or correlations among genes and environments were small, biases to main effects or interaction odds ratios were small to nonexistent.

A general population-genetic model for the production by population structure of spurious genotype-phenotype associations in discrete, admixed or spatially distributed populations

In linkage disequilibrium mapping of genetic variants causally associated with phenotypes, spurious associations can potentially be generated by any of a variety of types of population structure. However, mathematical theory of the production of spurious associations has largely been restricted to population structure models that involve the sampling of individuals from a collection of discrete subpopulations. Here, we introduce a general model of spurious association in structured populations, appropriate whether the population structure involves discrete groups, admixture among such groups, or continuous variation across space. Under the assumptions of the model, we find that a single common principle--applicable to both the discrete and admixed settings as well as to spatial populations--gives a necessary and sufficient condition for the occurrence of spurious associations. Using a mathematical connection between the discrete and admixed cases, we show that in admixed populations, spurious associations are less severe than in corresponding mixtures of discrete subpopulations, especially when the variance of admixture across individuals is small. This observation, together with the results of simulations that examine the relative influences of various model parameters, has important implications for the design and analysis of genetic association studies in structured populations.

Effect of population stratification on case-control association studies. II. False-positive rates and their limiting behavior as number of subpopulations increases

There has been considerable debate in the literature concerning bias in case-control association mapping studies due to population stratification. In this paper, we perform a theoretical analysis of the effects of population stratification by measuring the inflation in the test's type I error (or false-positive rate). Using a model of stratified sampling, we derive an exact expression for the type I error as a function of population parameters and sample size. We give necessary and sufficient conditions for the bias to vanish when there is no statistical association between disease and marker genotype in each of the subpopulations making up the total population. We also investigate the variation of bias with increasing subpopulations and show, both theoretically and by using simulations, that the bias can sometimes be quite substantial even with a very large number of subpopulations. In a companion simulation-based paper (Heiman et al., Part I, this issue), we have focused on the CRR (confounding risk ratio) and its relationship to the type I error in the case of two subpopulations, and have also quantified the magnitude of the type I error that can occur with relatively low CRR values.