Large genome-wide association studies have been performed to detect common genetic variants involved in common diseases, but most of the variants found this way account for only a small portion of the trait variance. Furthermore, candidate gene based resequencing suggests that many rare genetic variants contribute to the trait variance of common diseases. Here we propose two designs, sibpair and unrelated-case designs, to detect rare genetic variants in either a candidate gene based or genome-wide association analysis. First we show that we can detect and classify together rare risk haplotypes using a relatively small sample with either of these designs, and then have increased power to test association in a larger case-control sample. This method can also be applied to resequencing data. Next we apply the method to the Wellcome Trust Case Control Consortium (WTCCC) coronary artery disease and hypertension data, the latter being the only trait for which no genome-wide association evidence was reported in the original WTCCC study, and identify one interesting gene associated with hypertension and four associated with coronary artery disease at a genome-wide significance level of 5%. These results suggest that searching for rare genetic variants is feasible and can be fruitful in current genome-wide association studies, candidate gene studies or resequencing studies.

The Broad Antisocial Behavior Consortium entails the largest collaboration to date on the genetic architecture of ASB, and the first results suggest that ASB may be highly polygenic and has potential heterogeneous genetic effects across sex.

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Current extensive genetic research into common complex diseases, especially with the completion of genome-wide association studies, is bringing to light many novel genetic risk loci. These new discoveries, along with previously known genetic risk variants, offer an important opportunity for researchers to improve health care. We describe a method of quick evaluation of these new findings for potential clinical practice by designing a new predictive genetic test, estimating its classification accuracy, and determining the sample size required for the verification of this accuracy. The proposed predictive test is asymptotically more powerful than tests built on any other existing method and can be extended to scenarios where loci are linked or interact. We illustrate the approach for the case of type 2 diabetes. We incorporate recently discovered risk factors into the proposed test and find a potentially better predictive genetic test. The area under the receiver operating characteristic (ROC) curve (AUC) of the proposed test is estimated to be higher (AUC = 0.671) than for the existing test (AUC = 0.580).

Pre-eclampsia (PE) affects 5–7% of pregnancies in the US, and is a leading cause of maternal death and perinatal morbidity and mortality worldwide. To identify genes with a role in PE, we conducted a large-scale association study evaluating 775 SNPs in 190 candidate genes selected for a potential role in obstetrical complications. SNP discovery was performed by DNA sequencing, and genotyping was carried out in a high-throughput facility using the MassARRAY^TM System. Women with PE (n = 394) and their offspring (n = 324) were compared with control women (n = 602) and their offspring (n = 631) from the same hospital-based population. Haplotypes were estimated for each gene using the EM algorithm, and empirical p values were obtained for a logistic regression-based score test, adjusted for significant covariates. An interaction model between maternal and offspring genotypes was also evaluated. The most significant findings for association with PE were COL1A1 (p = 0.0011) and IL1A (p = 0.0014) for the maternal genotype, and PLAUR (p = 0.0008) for the offspring genotype. Common candidate genes for PE, including MTHFR and NOS3, were not significantly associated with PE. For the interaction model, SNPs within IGF1 (p = 0.0035) and IL4R (p = 0.0036) gave the most significant results. This study is one of the most comprehensive genetic association studies of PE to date, including an evaluation of offspring genotypes that have rarely been considered in previous studies. Although we did not identify statistically significant evidence of association for any of the candidate loci evaluated here after adjusting for multiple testing using the false discovery rate, additional compelling evidence exists, including multiple SNPs with nominally significant p values in COL1A1 and the IL1A region, and previous reports of association for IL1A, to support continued interest in these genes as candidates for PE. Identification of the genetic regulators of PE may have broader implications, since women with PE are at increased risk of death from cardiovascular diseases later in life.

Fisher [1925] was the first to suggest a method of combining the p-values obtained from several statistics and many other methods have been proposed since then. However, there is no agreement about what is the best method. Motivated by a situation that now often arises in genetic epidemiology, we consider the problem when it is possible to define a simple alternative hypothesis of interest for which the expected effect size of each test statistic is known and we determine the most powerful test for this simple alternative hypothesis. Based on the proposed method, we show that information about the effect sizes can be used to obtain the best weights for Liptak's method of combining p-values. We present extensive simulation results comparing methods of combining p-values and illustrate for a real example in genetic epidemiology how information about effect sizes can be deduced.

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Few studies have investigated gender differences in dietary intake. The objective of this cross-sectional study was to examine gender differences in dietary patterns and their association with the prevalence of metabolic syndrome. The food intakes of 3794 subjects enrolled by a two-stage cluster stratified sampling method were collected using a valid semi-quantitative food frequency questionnaire (FFQ). Metabolic syndrome (MetS) was defined according to the International Diabetes Federation (IDF) and its prevalence was 35.70% in the sample (37.67% in men and 24.67% in women). Dietary patterns were identified using factor analysis combined with cluster analysis and multiple group confirmatory factor analysis was used to assess the factorial invariance between gender groups. The dominating dietary pattern for men was the “balanced” dietary pattern (32.65%) and that for women was the “high-salt and energy” dietary pattern (34.42%). For men, the “animal and fried food” dietary pattern was related to higher risk of MetS (odds ratio: 1.27; 95% CI: 1.01–1.60), after adjustment for age, marital status, socioeconomic status and lifestyle factors. For women, the “high-salt and energy” dietary pattern was related to higher risk of MetS (odds ratio: 2.27; 95% CI: 1.24–4.14). We observed gender differences in dietary patterns and their association with the prevalence of MetS. For men, the “animal and fried food” dietary pattern was associated with enhancive likelihood of MetS. For women, it was the “high-salt and energy” dietary pattern.