Faculty Bibliography

BACKGROUND:Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. RESULTS:Two of these rules-one associated with eating disorder and the other with anxiety-remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. CONCLUSION/CONCLUSIONS:Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.

The genetic susceptibility to preeclampsia, a pregnancy-specific complication with significant maternal and fetal morbidity, has been poorly characterized. To identify maternal genes associated with preeclampsia risk, we assembled 498 cases and 1864 controls of European ancestry from preeclampsia case-control collections in 5 different US sites (with additional matched population controls), genotyped samples on a cardiovascular gene-centric array composed of variants from ≈2000 genes selected based on prior genetic studies of cardiovascular and metabolic diseases and performed case-control genetic association analysis on 27 429 variants passing quality control. In silico replication testing of 9 lead signals with P<10^-4 was performed in independent European samples from the SOPHIA (Study of Pregnancy Hypertension in Iowa) and Inova cohorts (212 cases, 456 controls). Multiethnic assessment of lead signals was then performed in samples of black (26 cases, 136 controls), Hispanic (132 cases, 468 controls), and East Asian (9 cases, 80 controls) ancestry. Multiethnic meta-analysis (877 cases, 3004 controls) revealed a study-wide statistically significant association of the rs9478812 variant in the pleiotropic PLEKHG1 gene (odds ratio, 1.40 [1.23-1.60]; P_meta=5.90×10^-7). The rs9478812 effect was even stronger in the subset of European cases with known early-onset preeclampsia (236 cases diagnosed <37 weeks, 1864 controls; odds ratio, 1.59 [1.27-1.98]; P=4.01×10^-5). PLEKHG1 variants have previously been implicated in genome-wide association studies of blood pressure, body weight, and neurological disorders. Although larger studies are required to further define maternal preeclampsia heritability, this study identifies a novel maternal risk locus for further investigation.

Although immunosuppressive drugs have enhanced patient outcomes in transplantation, the liver transplant community has made significant research efforts into the discovery of more accurate and precise methods of posttransplant monitoring and diagnosing. Current research in biomarkers reveals many promising approaches.

BACKGROUND:Genome wide association studies of patients with European descent have identified common variants associated with risk of reduced estimated glomerular filtration rate (eGFR). A panel of eight variants were selected to evaluate their association and prevalence in a Saudi Arabian patient cohort with chronic kidney disease (CKD). METHODS:) and effects were compared with results obtained from 133,413 participants in the CKD genetics consortium. Multivariable regression was used to evaluate the role of these eight variants in improving prediction of CKD development. RESULTS:All eight variants were present in Saudi populations with minor allele frequency ranging from 16 to 46%. The risk variant in all four genes demonstrated the same direction of effect as observed in European populations. One variant, rs4821480, in MYH9 was significantly associated with increased risk of development of CKD (OR = 1.69, 95% CI 1.22-2.36, P = 0.002), but the additional variants were not statistically significant given our modest sample size. CONCLUSIONS:CKD risk variants identified in European populations are present in Saudis. We did not find evidence to suggest heterogeneity of effect size compared to previously published estimates in European populations. Multivariable logistic regression analysis showed a statistically significant improvement in predicting the CKD using models with either FGF23 and vitamin D or FGF23, vitamin D level, and MYH9 genotypes (AUC = 0.93, 95% CI 0.90-0.95, P <  0.0001).

Homocysteine (Hcy) is a heritable biomarker for CVD, peripheral artery disease, stroke, and dementia. Little is known about genetic associations with Hcy in individuals of African ancestry. We performed a genome-wide association study for Hcy in 4927 AAs from the Jackson Heart Study (JHS), the Multi-Ethnic Study of Atherosclerosis (MESA), and the Coronary Artery Risk in Young Adults (CARDIA) study. Analyses were stratified by sex and results were meta-analyzed within and across sex. In the sex-combined meta-analysis, we observed genome-wide significant evidence (p < 5.0 × 10^-8) for the NOX4 locus (lead variant rs2289125, β = -0.15, p = 5.3 × 10¹¹). While the NOX4 locus was previously reported as associated with Hcy in European-American populations, rs2289125 remained genome-wide significant when conditioned on the previously reported lead variants. Previously reported genome-wide significant associations at NOX4, MTR, CBS, and MMACHC were also nominally (p < 0.050) replicated in AAs. Associations at the CPS1 locus, previously reported in females only, also was replicated specifically in females in this analysis, supporting sex-specific effects for this locus. These results suggest that there may be a combination of cross-population and population-specific genetic effects, as well as differences in genetic effects between males and females, in the regulation of Hcy levels.

While advances in patient care and immunosuppressive pharmacotherapies have increased the lifespan of heart allograft recipients, there are still significant comorbidities post-transplantation and 5-year survival rates are still significant, at approximately 70%. The last decade has seen massive strides in genomics and other omics fields, including transcriptomics, with many of these advances now starting to impact heart transplant clinical care. This review summarizes a number of the key advances in genomics which are relevant for heart transplant outcomes, and we highlight the translational potential that such knowledge may bring to patient care within the next decade.

Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors.