Craniosynostosis of the Metopic Suture in a Patient With CADASIL/Lehman Syndrome.

ABSTRACT: A 3-month-old patient presented for evaluation by plastic surgery with marked trigonocephaly and was subsequently diagnosed with metopic craniosynostosis. During presurgical evaluation, the patient was found to have two variants of the NOTCH3 gene, resulting in the diagnosis of lateral meningocele (Lehman) syndrome. Due to the increased possibility of stroke associated with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, the patient underwent only anterior calvarial vault remodeling without fronto-orbital advancement for correction of her craniosynostosis. This unique constellation of symptoms, and its impact on operative management, has not been previously described in the literature.

Transferability of tag SNPs in genetic association studies in multiple populations.

A general question for linkage disequilibrium-based association studies is how power to detect an association is compromised when tag SNPs are chosen from data in one population sample and then deployed in another sample. Specifically, it is important to know how well tags picked from the HapMap DNA samples capture the variation in other samples. To address this, we collected dense data uniformly across the four HapMap population samples and eleven other population samples. We picked tag SNPs using genotype data we collected in the HapMap samples and then evaluated the effective coverage of these tags in comparison to the entire set of common variants observed in the other samples. We simulated case-control association studies in the non-HapMap samples under a disease model of modest risk, and we observed little loss in power. These results demonstrate that the HapMap DNA samples can be used to select tags for genome-wide association studies in many samples around the world.

Demonstrating stratification in a European American population.

Population stratification occurs in case-control association studies when allele frequencies differ between cases and controls because of ancestry. Stratification may lead to false positive associations, although this issue remains controversial. Empirical studies have found little evidence of stratification in European-derived populations, but potentially significant levels of stratification could not be ruled out. We studied a European American panel discordant for height, a heritable trait that varies widely across Europe. Genotyping 178 SNPs and applying standard analytical methods yielded no evidence of stratification. But a SNP in the gene LCT that varies widely in frequency across Europe was strongly associated with height (P < 10(-6)). This apparent association was largely or completely due to stratification; rematching individuals on the basis of European ancestry greatly reduced the apparent association, and no association was observed in Polish or Scandinavian individuals. The failure of standard methods to detect this stratification indicates that new methods may be required.

Genomic screening and replication using the same data set in family-based association testing.

The Human Genome Project and its spin-offs are making it increasingly feasible to determine the genetic basis of complex traits using genome-wide association studies. The statistical challenge of analyzing such studies stems from the severe multiple-comparison problem resulting from the analysis of thousands of SNPs. Our methodology for genome-wide family-based association studies, using single SNPs or haplotypes, can identify associations that achieve genome-wide significance. In relation to developing guidelines for our screening tools, we determined lower bounds for the estimated power to detect the gene underlying the disease-susceptibility locus, which hold regardless of the linkage disequilibrium structure present in the data. We also assessed the power of our approach in the presence of multiple disease-susceptibility loci. Our screening tools accommodate genomic control and use the concept of haplotype-tagging SNPs. Our methods use the entire sample and do not require separate screening and validation samples to establish genome-wide significance, as population-based designs do.

Association of genetic variation with systolic and diastolic blood pressure among African Americans: the Candidate Gene Association Resource study.

The prevalence of hypertension in African Americans (AAs) is higher than in other US groups; yet, few have performed genome-wide association studies (GWASs) in AA. Among people of European descent, GWASs have identified genetic variants at 13 loci that are associated with blood pressure. It is unknown if these variants confer susceptibility in people of African ancestry. Here, we examined genome-wide and candidate gene associations with systolic blood pressure (SBP) and diastolic blood pressure (DBP) using the Candidate Gene Association Resource (CARe) consortium consisting of 8591 AAs. Genotypes included genome-wide single-nucleotide polymorphism (SNP) data utilizing the Affymetrix 6.0 array with imputation to 2.5 million HapMap SNPs and candidate gene SNP data utilizing a 50K cardiovascular gene-centric array (ITMAT-Broad-CARe [IBC] array). For Affymetrix data, the strongest signal for DBP was rs10474346 (P= 3.6 × 10(-8)) located near GPR98 and ARRDC3. For SBP, the strongest signal was rs2258119 in C21orf91 (P= 4.7 × 10(-8)). The top IBC association for SBP was rs2012318 (P= 6.4 × 10(-6)) near SLC25A42 and for DBP was rs2523586 (P= 1.3 × 10(-6)) near HLA-B. None of the top variants replicated in additional AA (n = 11 882) or European-American (n = 69 899) cohorts. We replicated previously reported European-American blood pressure SNPs in our AA samples (SH2B3, P= 0.009; TBX3-TBX5, P= 0.03; and CSK-ULK3, P= 0.0004). These genetic loci represent the best evidence of genetic influences on SBP and DBP in AAs to date. More broadly, this work supports that notion that blood pressure among AAs is a trait with genetic underpinnings but also with significant complexity.

Replication and fine mapping of asthma-associated loci in individuals of African ancestry.

Asthma originates from genetic and environmental factors with about half the risk of disease attributable to heritable causes. Genome-wide association studies, mostly in populations of European ancestry, have identified numerous asthma-associated single nucleotide polymorphisms (SNPs). Studies in populations with diverse ancestries allow both for identification of robust associations that replicate across ethnic groups and for improved resolution of associated loci due to different patterns of linkage disequilibrium between ethnic groups. Here we report on an analysis of 745 African-American subjects with asthma and 3,238 African-American control subjects from the Candidate Gene Association Resource (CARe) Consortium, including analysis of SNPs imputed using 1,000 Genomes reference panels and adjustment for local ancestry. We show strong evidence that variation near RAD50/IL13, implicated in studies of European ancestry individuals, replicates in individuals largely of African ancestry. Fine mapping in African ancestry populations also refined the variants of interest for this association. We also provide strong or nominal evidence of replication at loci near ORMDL3/GSDMB, IL1RL1/IL18R1, and 10p14, all previously associated with asthma in European or Japanese populations, but not at the PYHIN1 locus previously reported in studies of African-American samples. These results improve the understanding of asthma genetics and further demonstrate the utility of genetic studies in populations other than those of largely European ancestry.

Rapid assessment of genetic ancestry in populations of unknown origin by genome-wide genotyping of pooled samples.

As we move forward from the current generation of genome-wide association (GWA) studies, additional cohorts of different ancestries will be studied to increase power, fine map association signals, and generalize association results to additional populations. Knowledge of genetic ancestry as well as population substructure will become increasingly important for GWA studies in populations of unknown ancestry. Here we propose genotyping pooled DNA samples using genome-wide SNP arrays as a viable option to efficiently and inexpensively estimate admixture proportion and identify ancestry informative markers (AIMs) in populations of unknown origin. We constructed DNA pools from African American, Native Hawaiian, Latina, and Jamaican samples and genotyped them using the Affymetrix 6.0 array. Aided by individual genotype data from the African American cohort, we established quality control filters to remove poorly performing SNPs and estimated allele frequencies for the remaining SNPs in each panel. We then applied a regression-based method to estimate the proportion of admixture in each cohort using the allele frequencies estimated from pooling and populations from the International HapMap Consortium as reference panels, and identified AIMs unique to each population. In this study, we demonstrated that genotyping pooled DNA samples yields estimates of admixture proportion that are both consistent with our knowledge of population history and similar to those obtained by genotyping known AIMs. Furthermore, through validation by individual genotyping, we demonstrated that pooling is quite effective for identifying SNPs with large allele frequency differences (i.e., AIMs) and that these AIMs are able to differentiate two closely related populations (HapMap JPT and CHB).

Fine mapping of the association with obesity at the FTO locus in African-derived populations.

Genome-wide association studies have identified many common genetic variants that are associated with polygenic traits, and have typically been performed with individuals of recent European ancestry. In these populations, many common variants are tightly correlated, with the perfect or near-perfect proxies for the functional or true variant showing equivalent evidence of association, considerably limiting the resolution of fine mapping. Populations with recent African ancestry often have less extensive and/or different patterns of linkage disequilibrium (LD), and have been proposed to be useful in fine-mapping studies. Here, we strongly replicate and fine map in populations of predominantly African ancestry the association between variation at the FTO locus and body mass index (BMI) that is well established in populations of European ancestry. We genotyped single nucleotide polymorphisms that are correlated with the signal of association in individuals of European ancestry but that have varying degrees of correlation in African-derived individuals. Most of the variants, including one previously proposed as functionally important, have no significant association with BMI, but two variants, rs3751812 and rs9941349, show strong evidence of association (P = 2.58 x 10(-6) and 3.61 x 10(-6) in a meta-analysis of 9881 individuals). Thus, we have both strongly replicated this association in African-ancestry populations and narrowed the list of potentially causal variants to those that are correlated with rs3751812 and rs9941349 in African-derived populations. This study illustrates the potential of using populations with different LD patterns to fine map associations and helps pave the way for genetically guided functional studies at the FTO locus.

Genome-wide association of anthropometric traits in African- and African-derived populations.

Genome-wide association (GWA) studies have identified common variants that are associated with a variety of traits and diseases, but most studies have been performed in European-derived populations. Here, we describe the first genome-wide analyses of imputed genotype and copy number variants (CNVs) for anthropometric measures in African-derived populations: 1188 Nigerians from Igbo-Ora and Ibadan, Nigeria, and 743 African-Americans from Maywood, IL. To improve the reach of our study, we used imputation to estimate genotypes at approximately 2.1 million single-nucleotide polymorphisms (SNPs) and also tested CNVs for association. No SNPs or common CNVs reached a genome-wide significance level for association with height or body mass index (BMI), and the best signals from a meta-analysis of the two cohorts did not replicate in approximately 3700 African-Americans and Jamaicans. However, several loci previously confirmed in European populations showed evidence of replication in our GWA panel of African-derived populations, including variants near IHH and DLEU7 for height and MC4R for BMI. Analysis of global burden of rare CNVs suggested that lean individuals possess greater total burden of CNVs, but this finding was not supported in an independent European population. Our results suggest that there are not multiple loci with strong effects on anthropometric traits in African-derived populations and that sample sizes comparable to those needed in European GWA studies will be required to identify replicable associations. Meta-analysis of this data set with additional studies in African-ancestry populations will be helpful to improve power to detect novel associations.

Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution.

To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist-hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9x10(-11)) and MSRA (WC, P = 8.9x10(-9)). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6x10(-8)). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.

Meta-analysis of the INSIG2 association with obesity including 74,345 individuals: does heterogeneity of estimates relate to study design?

The INSIG2 rs7566605 polymorphism was identified for obesity (BMI> or =30 kg/m(2)) in one of the first genome-wide association studies, but replications were inconsistent. We collected statistics from 34 studies (n = 74,345), including general population (GP) studies, population-based studies with subjects selected for conditions related to a better health status ('healthy population', HP), and obesity studies (OB). We tested five hypotheses to explore potential sources of heterogeneity. The meta-analysis of 27 studies on Caucasian adults (n = 66,213) combining the different study designs did not support overall association of the CC-genotype with obesity, yielding an odds ratio (OR) of 1.05 (p-value = 0.27). The I(2) measure of 41% (p-value = 0.015) indicated between-study heterogeneity. Restricting to GP studies resulted in a declined I(2) measure of 11% (p-value = 0.33) and an OR of 1.10 (p-value = 0.015). Regarding the five hypotheses, our data showed (a) some difference between GP and HP studies (p-value = 0.012) and (b) an association in extreme comparisons (BMI> or =32.5, 35.0, 37.5, 40.0 kg/m(2) versus BMI<25 kg/m(2)) yielding ORs of 1.16, 1.18, 1.22, or 1.27 (p-values 0.001 to 0.003), which was also underscored by significantly increased CC-genotype frequencies across BMI categories (10.4% to 12.5%, p-value for trend = 0.0002). We did not find evidence for differential ORs (c) among studies with higher than average obesity prevalence compared to lower, (d) among studies with BMI assessment after the year 2000 compared to those before, or (e) among studies from older populations compared to younger. Analysis of non-Caucasian adults (n = 4889) or children (n = 3243) yielded ORs of 1.01 (p-value = 0.94) or 1.15 (p-value = 0.22), respectively. There was no evidence for overall association of the rs7566605 polymorphism with obesity. Our data suggested an association with extreme degrees of obesity, and consequently heterogeneous effects from different study designs may mask an underlying association when unaccounted for. The importance of study design might be under-recognized in gene discovery and association replication so far.

The efficacy of detecting variants with small effects on the Affymetrix 6.0 platform using pooled DNA.

Genome-wide genotyping of a cohort using pools rather than individual samples has long been proposed as a cost-saving alternative for performing genome-wide association (GWA) studies. However, successful disease gene mapping using pooled genotyping has thus far been limited to detecting common variants with large effect sizes, which tend not to exist for many complex common diseases or traits. Therefore, for DNA pooling to be a viable strategy for conducting GWA studies, it is important to determine whether commonly used genome-wide SNP array platforms such as the Affymetrix 6.0 array can reliably detect common variants of small effect sizes using pooled DNA. Taking obesity and age at menarche as examples of human complex traits, we assessed the feasibility of genome-wide genotyping of pooled DNA as a single-stage design for phenotype association. By individually genotyping the top associations identified by pooling, we obtained a 14- to 16-fold enrichment of SNPs nominally associated with the phenotype, but we likely missed the top true associations. In addition, we assessed whether genotyping pooled DNA can serve as an inexpensive screen as the second stage of a multi-stage design with a large number of samples by comparing the most cost-effective 3-stage designs with 80% power to detect common variants with genotypic relative risk of 1.1, with and without pooling. Given the current state of the specific technology we employed and the associated genotyping costs, we showed through simulation that a design involving pooling would be 1.07 times more expensive than a design without pooling. Thus, while a significant amount of information exists within the data from pooled DNA, our analysis does not support genotyping pooled DNA as a means to efficiently identify common variants contributing small effects to phenotypes of interest. While our conclusions were based on the specific technology and study design we employed, the approach presented here will be useful for evaluating the utility of other or future genome-wide genotyping platforms in pooled DNA studies.

On the replication of genetic associations: timing can be everything!

The failure of researchers to replicate genetic-association findings is most commonly attributed to insufficient statistical power, population stratification, or various forms of between-study heterogeneity or environmental influences.(1) Here, we illustrate another potential cause for nonreplications that has so far not received much attention in the literature. We illustrate that the strength of a genetic effect can vary by age, causing "age-varying associations." If not taken into account during the design and the analysis of a study, age-varying genetic associations can cause nonreplication. By using the 100K SNP scan of the Framingham Heart Study, we identified an age-varying association between a SNP in ROBO1 and obesity and hypothesized an age-gene interaction. This finding was followed up in eight independent samples comprising 13,584 individuals. The association was replicated in five of the eight studies, showing an age-dependent relationship (one-sided combined p = 3.92 x 10(-9), combined p value from pediatric cohorts = 2.21 x 10(-8), combined p value from adult cohorts = 0.00422). Furthermore, this study illustrates that it is difficult for cross-sectional study designs to detect age-varying associations. If the specifics of age- or time-varying genetic effects are not considered in the selection of both the follow-up samples and in the statistical analysis, important genetic associations may be missed.

Interrogating local population structure for fine mapping in genome-wide association studies.

MOTIVATION: Adjustment for population structure is necessary to avoid bias in genetic association studies of susceptibility variants for complex diseases. Population structure may differ from one genomic region to another due to the variability of individual ancestry associated with migration, random genetic drift or natural selection. Current association methods for correcting population stratification usually involve adjustment of global ancestry between study subjects. RESULTS: We suggest interrogating local population structure for fine mapping to more accurately locate true casual genes by better adjusting the confounding effect due to local ancestry. By extensive simulations on genome-wide datasets, we show that adjusting global ancestry may lead to false positives when local population structure is an important confounding factor. In contrast, adjusting local ancestry can effectively prevent false positives due to local population structure and thus can improve fine mapping for disease gene localization. We applied the local and global adjustments to the analysis of datasets from three genome-wide association studies, including European Americans, African Americans and Nigerians. Both European Americans and African Americans demonstrate greater variability in local ancestry than Nigerians. Adjusting local ancestry successfully eliminated the known spurious association between SNPs in the LCT gene and height due to the population structure existed in European Americans. CONTACT: xiaofeng.zhu@case.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer direct causal effects.

In genetic association studies, different complex phenotypes are often associated with the same marker. Such associations can be indicative of pleiotropy (i.e. common genetic causes), of indirect genetic effects via one of these phenotypes, or can be solely attributable to non-genetic/environmental links between the traits. To identify the phenotypes with the inducing genetic association, statistical methodology is needed that is able to distinguish between the different causes of the genetic associations. Here, we propose a simple, general adjustment principle that can be incorporated into many standard genetic association tests which are then able to infer whether an SNP has a direct biological influence on a given trait other than through the SNP's influence on another correlated phenotype. Using simulation studies, we show that, in the presence of a non-marker related link between phenotypes, standard association tests without the proposed adjustment can be biased. In contrast to that, the proposed methodology remains unbiased. Its achieved power levels are identical to those of standard adjustment methods, making the adjustment principle universally applicable in genetic association studies. The principle is illustrated by an application to three genome-wide association analyses.

Obesity and diabetes genetic variants associated with gestational weight gain.

OBJECTIVE: We sought to determine whether genetic variants associated with diabetes and obesity predict gestational weight gain. STUDY DESIGN: A total of 960 participants in the Pregnancy, Infection, and Nutrition cohorts were genotyped for 27 single-nucleotide polymorphisms (SNPs) associated with diabetes and obesity. RESULTS: Among Caucasian and African American women (n = 960), KCNQ1 risk allele carriage was directly associated with weight gain (P < .01). In Bayesian hierarchical models among Caucasian women (n = 628), we found posterior odds ratios >3 for inclusion of TCF2 and THADA SNPs in our models. Among African American women (n = 332), we found associations between risk allele carriage and weight gain for the THADA and INSIG2 SNPs. In Bayesian variable selection models, we found an interaction between the TSPAN8 risk allele and pregravid obesity, with lower weight gain among obese risk allele carriers. CONCLUSION: We found evidence that diabetes and obesity risk alleles interact with maternal pregravid body mass index to predict gestational weight gain.

The association of a SNP upstream of INSIG2 with body mass index is reproduced in several but not all cohorts.

A SNP upstream of the INSIG2 gene, rs7566605, was recently found to be associated with obesity as measured by body mass index (BMI) by Herbert and colleagues. The association between increased BMI and homozygosity for the minor allele was first observed in data from a genome-wide association scan of 86,604 SNPs in 923 related individuals from the Framingham Heart Study offspring cohort. The association was reproduced in four additional cohorts, but was not seen in a fifth cohort. To further assess the general reproducibility of this association, we genotyped rs7566605 in nine large cohorts from eight populations across multiple ethnicities (total n = 16,969). We tested this variant for association with BMI in each sample under a recessive model using family-based, population-based, and case-control designs. We observed a significant (p < 0.05) association in five cohorts but saw no association in three other cohorts. There was variability in the strength of association evidence across examination cycles in longitudinal data from unrelated individuals in the Framingham Heart Study Offspring cohort. A combined analysis revealed significant independent validation of this association in both unrelated (p = 0.046) and family-based (p = 0.004) samples. The estimated risk conferred by this allele is small, and could easily be masked by small sample size, population stratification, or other confounders. These validation studies suggest that the original association is less likely to be spurious, but the failure to observe an association in every data set suggests that the effect of SNP rs7566605 on BMI may be heterogeneous across population samples.

Peroxisome proliferator-activated receptor-gamma (PPARgamma) Pro12Ala polymorphism and risk for pediatric obesity.

BACKGROUND: Variation in the peroxisome-proliferator-activated receptor gamma (PPARgamma) gene has been reported to alter the risk for adiposity in adults. METHODS: We investigated the gender related association between the Pro12Ala variant (rs1801282) in obesity and insulin resistance traits in 794 peri-adolescent children aged 10-12 years of Greek origin from the Gene and Diet Attica Investigation (GENDAI) cohort. RESULTS: Gender stratified analysis suggested that in peri-adolescent boys, Ala carriers exhibited lower measures of skinfold (triceps: 16.9+/-6.9 vs. 19.4+/-7.9 mm, p=0.014; subscapular: 9.6+/-4.5 vs. 11.2+/-5.4 mm, p=0.016) and lower adiponectin concentrations (3.9+/-1.3 vs. 4.7+/-2.4 microg/mL, p=0.05). In peri-adolescent girls, Ala carriers had lower insulin concentrations (7.3+/-3.7 vs. 8.5+/-4.4 microU/mL, p=0.026) and lower values of homeostasis model assessment of insulin resistance (HOMA-IR) (1.5+/-0.8 vs. 1.8+/-0.96, p=0.019). Linear regression analysis revealed that the presence of the Ala allele in boys was a nominally significant predictor of obesity indices, including skin-folds (triceps: beta+/-SE: -2.3+/-1.1, p=0.032; subscapular: beta+/-SE: -2.3+/-1.1, p=0.04) and adiponectin concentrations (beta+/-SE: -0.7+/-0.4, p=0.05) after adjusting for potential covariates. In girls, the Ala allele was a predictor of insulin concentrations (beta+/-SE: -1.2+/-0.6, p=0.037) and HOMA-IR (beta+/-SE: -0.24+/-0.13, p=0.037). CONCLUSIONS: Our results suggest that adiposity in children is influenced by the Pro12Ala polymorphism in a gender specific manner.

Common variants in the ENPP1 gene are not reproducibly associated with diabetes or obesity.

The common missense single nucleotide polymorphism (SNP) K121Q in the ectoenzyme nucleotide pyrophosphate phosphodiesterase (ENPP1) gene has recently been associated with type 2 diabetes in Italian, U.S., and South-Asian populations. A three-SNP haplotype, including K121Q, has also been associated with obesity and type 2 diabetes in French and Austrian populations. We set out to confirm these findings in several large samples. We genotyped the haplotype K121Q (rs1044498), rs1799774, and rs7754561 in 8,676 individuals of European ancestry with and without type 2 diabetes, in 1,900 obese and 930 lean individuals of European ancestry from the U.S. and Poland, and in 1,101 African-American individuals. Neither the K121Q missense polymorphism nor the putative risk haplotype were significantly associated with type 2 diabetes or BMI. Two SNPs showed suggestive evidence of association in a meta-analysis of our European ancestry samples. These SNPs were rs7754561 with type 2 diabetes (odds ratio for the G-allele, 0.85 [95% CI 0.78-0.92], P = 0.00003) and rs1799774 with BMI (homozygotes of the delT-allele, 0.6 [0.42-0.88], P = 0.007). However, these findings are not supported by other studies. We did not observe a reproducible association between these three ENPP1 variants and BMI or type 2 diabetes.

Genetics of common forms of obesity: a brief overview.

The obesity epidemic is attributable to dietary and behavioral trends acting on a person's genetic makeup to determine body mass and susceptibility to obesity-related disease. Common forms of obesity have a strong hereditary component, yet genetic pathways that contribute to obesity have not yet been elucidated. Many genetic association studies have been reported, but few have been successfully replicated. New research tools and large studies will lead to an understanding of genes and their interaction to cause obesity, which may help guide successful interventions and treatments.