Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci

The Finnish population is enriched for genetic variants which are rare in other populations. Here, the authors find new genetic loci associated with 1391 circulating metabolites in 6136 Finnish men, demonstrating that metabolite genetic associations can help elucidate disease mechanisms. Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.Peer reviewe

Whole exome sequencing enhanced imputation identifies 85 metabolite associations in the Alpine CHRIS cohort

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with \u3e5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriche

Statistical Methods for Multi-Omics Data in Observational Studies

With the advent of high-throughput technology, the availability of various omics data types has presented an unprecedented opportunity to answer questions that were not previously possible. However, the data analytic techniques for analyzing omics data are lagging the requirements for high-dimensional data; there are few adequate statistical methods and software available to address the complexity and multimodality of omics data. Valid statistical toolboxes are essential for exploring and understanding the underlying biology, generating new hypotheses, and designing new experiments to deliver potentially new effective therapeutics. In Chapter 2, I develop a statistical method, termed DrFARM, to identify and infer pleiotropic genes and variants in multi-trait genomewide association studies (GWAS). In a standard analysis, pleiotropic variants are identified by post hoc combining results across separate GWASes. But such two-stage procedures may lead to spurious results. DrFARM employs a joint regression model for simultaneous analysis of high-dimensional genetic variants while incorporating multilevel dependencies. This joint modeling approach permits universal FDR control. DrFARM combines strengths of the debiasing technique and the Cauchy combination test, both being theoretically justified, to establish a valid post-selection inference on pleiotropic variants. Through extensive simulations, I show that DrFARM controls the overall FDR. Applying DrFARM to data on 1,031 metabolites measured on 6,135 men from the METSIM study, I find 288 new metabolite associations at loci that did not reach statistical significance in prior METSIM metabolite GWAS. In addition, I discover new pleiotropic loci for 16 metabolite pairs. Chapter 3 is devoted to the development of a statistical approach, termed CAMP, for differential abundance analysis of microbiome data. Microbiome data analysis faces the challenge of sparsity, with many entries being zeros. In differential abundance analysis, the presence of excessive zeros in data violates distributional assumptions and creates ties, leading to an increased risk of type I errors and reduced statistical power. To deal with these limitations, I introduce the concept of censoring normalization, which treats zeros as censored observations, transforms raw read counts into tie-free time-to-event-like data. The novel data transformation also enables the use of survival analysis techniques, such as the Cox proportional hazards model, for differential abundance analysis. Through extensive simulations, I demonstrate that CAMP achieves desirable statistical properties, such as proper type I error control and high power. The application of CAMP to a human gut microbiome dataset identifies 60 new differentially abundant taxa across geographic locations, showcasing its usefulness. Chapter 4 concerns the development of residual diagnostics to scrutinize two critical assumptions in the instrumental variable analysis methodology that is widely used in Mendelian randomization: exclusion restriction and exchangeability. Despite their prominence, there are no existing methods for testing the two assumptions. I develop two residual diagnostic plots, termed Y-Y plot and Y-X plot, which serve as visual aids in detecting potential departures from these assumptions, allowing for candidate instrument variable screening prior to downstream analysis. Furthermore, I propose a procedure for ranking multiple candidate instruments, providing researchers with a practical means of selecting the most promising instrumental variables for their analyses. Applying this diagnostic methodology to the METSIM dataset, I found that 128 / 317 (40.4%) of instrument candidates violate either the exclusion restriction or exchangeability assumptions, providing empirical confirmation for the majority valid assumption (at least 50% of instruments are valid) that is basis for the building of robust Mendelian randomization methods.PhDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/177907/1/lapsum_1.pd

Energy efficient online deadline scheduling

Abstract. This paper extends the study of online algorithms for energy-efficient deadline scheduling to the overloaded setting. Specifically, we consider a processor that can vary its speed between 0 and a maximum speed T to minimize its energy usage (of which the rate is roughly a cubic function of the speed). As the speed is upper bounded, the system may be overloaded with jobs and no scheduling algorithms can meet the deadlines of all jobs. An optimal schedule is expected to maximize the throughput, and furthermore, its energy usage should be the smallest among all schedules that achieve the maximum throughput. In designing a scheduling algorithm, one has to face the dilemma of selecting more jobs and being conservative in energy usage. Even if we ignore energy usage, the best possible online algorithm is 4-competitive on throughput [12]. On the other hand, existing work on energy-efficient scheduling focuses on minimizing the energy to complete all jobs on a processor with unbounded speed, giving several O(1)-competitive algorithms with respect to the energy usage [2,20]. This paper presents the first online algorithm for the more realistic setting where processor speed is bounded and the system may be overloaded; the algorithm is O(1)-competitive on both throughput and energy usage. If the maximum speed of the online scheduler is relaxed slightly to (1+ǫ)T for some ǫ&gt; 0, we can improve the competitive ratio on throughput to arbitrarily close to one, while maintaining O(1)-competitive on energy usage.

DataSheet_1_The association between altered intestinal microbiome, impaired systemic and ocular surface immunity, and impaired wound healing response after corneal alkaline-chemical injury in diabetic mice.docx

PurposeWe aim to investigate the effect of sustained hyperglycemia on corneal epithelial wound healing, ocular surface and systemic immune response, and microbiome indices in diabetic mice compared to controls after alkaline chemical injury of the eye.MethodsCorneal alkaline injury was induced in the right eye of Ins2Akita (Akita) mice and wild-type mice. The groups were observed at baseline and subsequently days 0, 3, and 7 after injury. Corneal re-epithelialization was observed under slit lamp with fluorescein staining using a cobalt blue light filter. Enucleated cornea specimens were compared at baseline and after injury for changes in cornea thickness under hematoxylin and eosin staining. Tear cytokine and growth factor levels were measured using protein microarray assay and compared between groups and time points. Flow cytometry was conducted on peripheral blood and ocular surface samples to determine CD3+CD4+ cell count. Fecal samples were collected, and gut microbiota composition and diversity pattern were measured using shotgun sequencing.ResultsAkita mice had significantly delayed corneal wound healing compared to controls. This was associated with a reduction in tear levels of vascular endothelial growth factor A, angiopoietin 2, and insulin growth factor 1 on days 0, 3, and 7 after injury. Furthermore, there was a distinct lack of upregulation of peripheral blood and ocular surface CD3+CD4+ cell counts in response to injury in Akita mice compared to controls. This was associated with a reduction in intestinal microbiome diversity indices in Akita mice compared to controls after injury. Specifically, there was a lower abundance of Firmicutes bacterium M10-2 in Akita mice compared to controls after injury.ConclusionIn diabetic mice, impaired cornea wound healing was associated with an inability to mount systemic and local immune response to ocular chemical injury. Baseline and post-injury differences in intestinal microbial diversity and abundance patterns between diabetic mice and controls may potentially play a role in this altered response.</p

Table_1_The association between altered intestinal microbiome, impaired systemic and ocular surface immunity, and impaired wound healing response after corneal alkaline-chemical injury in diabetic mice.docx

PurposeWe aim to investigate the effect of sustained hyperglycemia on corneal epithelial wound healing, ocular surface and systemic immune response, and microbiome indices in diabetic mice compared to controls after alkaline chemical injury of the eye.MethodsCorneal alkaline injury was induced in the right eye of Ins2Akita (Akita) mice and wild-type mice. The groups were observed at baseline and subsequently days 0, 3, and 7 after injury. Corneal re-epithelialization was observed under slit lamp with fluorescein staining using a cobalt blue light filter. Enucleated cornea specimens were compared at baseline and after injury for changes in cornea thickness under hematoxylin and eosin staining. Tear cytokine and growth factor levels were measured using protein microarray assay and compared between groups and time points. Flow cytometry was conducted on peripheral blood and ocular surface samples to determine CD3+CD4+ cell count. Fecal samples were collected, and gut microbiota composition and diversity pattern were measured using shotgun sequencing.ResultsAkita mice had significantly delayed corneal wound healing compared to controls. This was associated with a reduction in tear levels of vascular endothelial growth factor A, angiopoietin 2, and insulin growth factor 1 on days 0, 3, and 7 after injury. Furthermore, there was a distinct lack of upregulation of peripheral blood and ocular surface CD3+CD4+ cell counts in response to injury in Akita mice compared to controls. This was associated with a reduction in intestinal microbiome diversity indices in Akita mice compared to controls after injury. Specifically, there was a lower abundance of Firmicutes bacterium M10-2 in Akita mice compared to controls after injury.ConclusionIn diabetic mice, impaired cornea wound healing was associated with an inability to mount systemic and local immune response to ocular chemical injury. Baseline and post-injury differences in intestinal microbial diversity and abundance patterns between diabetic mice and controls may potentially play a role in this altered response.</p

Whole Exome Sequencing Enhanced Imputation Identifies 85 Metabolite Associations in the Alpine CHRIS Cohort

Metabolites are intermediates or end products of biochemical processes involved in both health and disease. Here, we take advantage of the well-characterized Cooperative Health Research in South Tyrol (CHRIS) study to perform an exome-wide association study (ExWAS) on absolute concentrations of 175 metabolites in 3294 individuals. To increase power, we imputed the identified variants into an additional 2211 genotyped individuals of CHRIS. In the resulting dataset of 5505 individuals, we identified 85 single-variant genetic associations, of which 39 have not been reported previously. Fifteen associations emerged at ten variants with >5-fold enrichment in CHRIS compared to non-Finnish Europeans reported in the gnomAD database. For example, the CHRIS-enriched ETFDH stop gain variant p.Trp286Ter (rs1235904433-hexanoylcarnitine) and the MCCC2 stop lost variant p.Ter564GlnextTer3 (rs751970792-carnitine) have been found in patients with glutaric acidemia type II and 3-methylcrotonylglycinuria, respectively, but the loci have not been associated with the respective metabolites in a genome-wide association study (GWAS) previously. We further identified three gene-trait associations, where multiple rare variants contribute to the signal. These results not only provide further evidence for previously described associations, but also describe novel genes and mechanisms for diseases and disease-related traits