Statistical Methods for Detecting Rare Variant Associations in Family-Based Designs

Rare variants are hypothesized to explain some genetic contribution to complex traits. However, using conventional case-control designs to identify rare variants associated with traits has low statistical power. Family designs can substantially increase power for these studies, especially for rare variants. In this dissertation, we present innovative statistical methods that can efficiently use family information to improve power. In Chapter 2, we present TRAFIC, a rare-variant association test using affected sibpairs. For rare risk variants, two affected siblings would share the variant on their shared identity-by-descent (IBD) chromosomes. We thus test the distribution of rare variants on IBD chromosomes and non-IBD chromosomes. TRAFIC is robust to population stratification as “cases” and “controls” are matched within each sibpair. We show that TRAFIC has significant power gain over the population case-control design for variants with summed allele frequency < 5%. Considering allelic heterogeneity, where risk variants have different effect sizes, TRAFIC can double the power of a case-control study. In Chapter 3, we present TRAP for testing the association between rare variants and a binary trait using extended families. Since affected family members are more likely to share risk variants, we propose to test if rare variants are shared more than expected given the known inheritance vector and the founder genotypes. TRAP is applicable from small to large pedigrees with multiple generations, including families with missing founders. Using simulations, we show that TRAP is more powerful than the conventional case-control design and existing family-based approaches, especially for rare variants. In Chapter 4, we present testing for rare variants associated with a continuous trait in extended families (TRACE). Given that a rare variant increases the trait value, conditional on inheritance vectors and founder genotypes, we test if family members with high trait values are more likely to share the variant. Under non-ascertained scenarios, TRACE can be more powerful than the existing family-based methods for large pedigrees; for ascertained scenarios, TRACE outperforms the existing approaches throughout considered pedigree structures. In sum, we present three family-based methods efficiently using the sharing of variants to increase the power for detecting rare variant associations.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137108/1/khlin_1.pd

Racial and Ethnic Differences in Diabetes Mellitus among People with and without Psychiatric Disorders: Results from the National Epidemiologic Survey on Alcohol and Related Conditions

Objective: This study examined racial/ethnic differences in the prevalence of diabetes mellitus in a nationally representative sample of adults with and without common psychiatric disorders. Method: Data were drawn from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (N= 34,653). Logistic regression models adjusting for sociodemographic variables and diabetes risk factors were used to examine racial/ethnic differences in 12-month prevalence rates of diabetes by psychiatric status. Results: Among people without psychiatric disorders, African Americans, Hispanics, and American Indians/Alaska Natives, but not Asians/Pacific Islanders, had significantly higher rates of diabetes than non-Hispanic whites even after adjusting for socio-demographic variables and diabetes risk factors. In the presence of psychiatric disorders, these health disparities persisted for African Americans and Hispanics, but not for American Indians/Alaska Natives. No significant interactions between race/ethnicity and psychiatric disorders in the odds of diabetes were found across any group. Conclusion: Policies and services that support culturally appropriate prevention and treatment strategies are needed to reduce racial/ethnic disparities in diabetes among people with and without psychiatric disabilities

Risk factors for in-hospital mortality after total arch procedure in patients with acute type A aortic dissection

ObjectKnowledge about the risk factors of in-hospital mortality for acute type A aortic dissection (ATAAD) patients who received total arch procedure is limited. This study aims to investigate preoperative and intraoperative risk factors of in-hospital mortality of these patients.MethodsFrom May 2014 to June 2018, 372 ATAAD patients received the total arch procedure in our institution. These patients were divided into survival and death groups, and patients` in-hospital data were retrospectively collected. Receiver operating characteristic curve analysis was adopted to determine the optimal cut-off value of continuous variables. Univariate and multivariable logistic regression analyses were used to detect independent risk factors for in-hospital mortality.ResultsA total of 321 patients were included in the survival group and 51 in the death group. Preoperative details showed that patients in the death group were older (55.4 ± 11.7 vs. 49.3 ± 12.6, P = 0.001), had more renal dysfunction (29.4% vs. 10.9%, P = 0.001) and coronary ostia dissection (29.4% vs. 12.2%, P = 0.001), and decreased left ventricular ejection fraction (LVEF) (57.5 ± 7.9% vs. 59.8 ± 7.3%, P = 0.032). Intraoperative results showed that more patients in the death group experienced concomitant coronary artery bypass grafting (35.3% vs. 15.3%, P = 0.001) with increased cardiopulmonary bypass (CPB) time (165.7 ± 39.0 vs. 149.4 ± 35.8 min, P = 0.003), cross-clamp time (98.4 ± 24.5 vs. 90.2 ± 26.9 min, P = 0.044), and red blood cell transfusion (913.7 ± 629.0 vs. 709.7 ± 686.6 ml, P = 0.047). Logistic regression analysis showed that age &gt;55 years, renal dysfunction, CPB time &gt;144 min, and RBC transfusion &gt;1,300 ml were independent risk factors for in-hospital mortality in patients with ATAAD.ConclusionIn the present study, we identified that older age, preoperative renal dysfunction, long CPB time, and intraoperative massive transfusion were risk factors for in-hospital mortality in ATAAD patients with the total arch procedure

A Potential Solution to Minimally Invasive Device for Oral Surgery: Evaluation of Surgical Outcomes in Rat

The objective of the present research was to investigate the thermal injury in the brain after minimally invasive electrosurgery using instruments with copper-doped diamond-like carbon (DLC-Cu) surface coating. The surface morphologies of DLC-Cu thin films were characterized using scanning electron microscopy and atomic force microscopy. Three-dimensional brain models were reconstructed using magnetic resonance imaging to simulate the electrosurgical operation. In adult rats, a monopolar electrosurgical instrument coated with the DLC-Cu thin film was used to generate lesions in the brain. Animals were sacrificed for evaluations on postoperative days 0, 2, 7, and 28. Data indicated that the temperature decreased significantly when minimally invasive electrosurgical instruments with nanostructure DLC-Cu thin films were used and continued to decrease with increasing film thickness. On the other hand, the DLC-Cu-treated device created a relatively small thermal injury area and lateral thermal effect in the brain tissues. These results indicated that the DLC-Cu thin film minimized excessive thermal injury and uniformly distributed the temperature in the brain. Taken together, our study results suggest that the DLC-Cu film on copper electrode substrates is an effective means for improving the performance of electrosurgical instruments

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

Author Correction: Discovery of 42 genome-wide significant loci associated with dyslexia

Correction to: Nature Genetics https://doi.org/10.1038/s41588-022-01192-y. Published online 20 October 2022. In the version of this article originally published, a paragraph was omitted in the Methods section, reading “Genomic control. Top SNPs are reported from the more conservative GWAS results adjusted for genomic control (Fig. 1, Extended Data Figs. 1–4, and Supplementary Tables 1, 2, 9 and 10), whereas downstream analyses (including gene-set analysis, enrichment and heritability partitioning, genetic correlations, polygenic prediction, candidate gene replication) are based on GWAS results without genomic control.” The paragraph has now been included in the HTML and PDF versions of the article

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P &lt; 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Bayesian approach to support vector machines

Ph.DDOCTOR OF PHILOSOPH