@article {1345, title = {A phenomics-based strategy identifies loci on APOC1, BRAP, and PLCG1 associated with metabolic syndrome phenotype domains.}, journal = {PLoS Genet}, volume = {7}, year = {2011}, month = {2011 Oct}, pages = {e1002322}, abstract = {

Despite evidence of the clustering of metabolic syndrome components, current approaches for identifying unifying genetic mechanisms typically evaluate clinical categories that do not provide adequate etiological information. Here, we used data from 19,486 European American and 6,287 African American Candidate Gene Association Resource Consortium participants to identify loci associated with the clustering of metabolic phenotypes. Six phenotype domains (atherogenic dyslipidemia, vascular dysfunction, vascular inflammation, pro-thrombotic state, central obesity, and elevated plasma glucose) encompassing 19 quantitative traits were examined. Principal components analysis was used to reduce the dimension of each domain such that >55\% of the trait variance was represented within each domain. We then applied a statistically efficient and computational feasible multivariate approach that related eight principal components from the six domains to 250,000 imputed SNPs using an additive genetic model and including demographic covariates. In European Americans, we identified 606 genome-wide significant SNPs representing 19 loci. Many of these loci were associated with only one trait domain, were consistent with results in African Americans, and overlapped with published findings, for instance central obesity and FTO. However, our approach, which is applicable to any set of interval scale traits that is heritable and exhibits evidence of phenotypic clustering, identified three new loci in or near APOC1, BRAP, and PLCG1, which were associated with multiple phenotype domains. These pleiotropic loci may help characterize metabolic dysregulation and identify targets for intervention.

}, keywords = {African Americans, Apolipoprotein C-I, Blood Glucose, Dyslipidemias, European Continental Ancestry Group, Genetic Association Studies, Genetic Predisposition to Disease, Genome, Human, Humans, Metabolic Syndrome, Obesity, Abdominal, Phenotype, Phospholipase C gamma, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable, Ubiquitin-Protein Ligases, Vascular Diseases}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1002322}, author = {Avery, Christy L and He, Qianchuan and North, Kari E and Ambite, Jos{\'e} L and Boerwinkle, Eric and Fornage, Myriam and Hindorff, Lucia A and Kooperberg, Charles and Meigs, James B and Pankow, James S and Pendergrass, Sarah A and Psaty, Bruce M and Ritchie, Marylyn D and Rotter, Jerome I and Taylor, Kent D and Wilkens, Lynne R and Heiss, Gerardo and Lin, Dan Yu} } @article {6111, title = {Post-genome-wide association study challenges for lipid traits: describing age as a modifier of gene-lipid associations in the Population Architecture using Genomics and Epidemiology (PAGE) study.}, journal = {Ann Hum Genet}, volume = {77}, year = {2013}, month = {2013 Sep}, pages = {416-25}, abstract = {

Numerous common genetic variants that influence plasma high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride distributions have been identified via genome-wide association studies (GWAS). However, whether or not these associations are age-dependent has largely been overlooked. We conducted an association study and meta-analysis in more than 22,000 European Americans between 49 previously identified GWAS variants and the three lipid traits, stratified by age (males: <50 or >=50 years of age; females: pre- or postmenopausal). For each variant, a test of heterogeneity was performed between the two age strata and significant Phet values were used as evidence of age-specific genetic effects. We identified seven associations in females and eight in males that displayed suggestive heterogeneity by age (Phet < 0.05). The association between rs174547 (FADS1) and LDL-C in males displayed the most evidence for heterogeneity between age groups (Phet = 1.74E-03, I(2) = 89.8), with a significant association in older males (P = 1.39E-06) but not younger males (P = 0.99). However, none of the suggestive modifying effects survived adjustment for multiple testing, highlighting the challenges of identifying modifiers of modest SNP-trait associations despite large sample sizes.

}, keywords = {Adult, Aged, European Continental Ancestry Group, Female, Genetic Association Studies, Genome-Wide Association Study, Humans, Lipids, Male, Middle Aged, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Quantitative Trait, Heritable, Risk Factors}, issn = {1469-1809}, doi = {10.1111/ahg.12027}, author = {Dumitrescu, Logan and Carty, Cara L and Franceschini, Nora and Hindorff, Lucia A and Cole, Shelley A and B{\r u}zkov{\'a}, Petra and Schumacher, Fredrick R and Eaton, Charles B and Goodloe, Robert J and Duggan, David J and Haessler, Jeff and Cochran, Barbara and Henderson, Brian E and Cheng, Iona and Johnson, Karen C and Carlson, Chris S and Love, Shelly-Ann and Brown-Gentry, Kristin and Nato, Alejandro Q and Quibrera, Miguel and Anderson, Garnet and Shohet, Ralph V and Ambite, Jose Luis and Wilkens, Lynne R and Marchand, Loic Le and Haiman, Christopher A and Buyske, Steven and Kooperberg, Charles and North, Kari E and Fornage, Myriam and Crawford, Dana C} } @article {6220, title = {Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Feb}, pages = {403-12}, abstract = {

BACKGROUND AND PURPOSE: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.

METHODS: The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged >=55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke.

RESULTS: In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3{\texttimes}10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7{\texttimes}10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)).

CONCLUSIONS: The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Area Under Curve, Case-Control Studies, Cohort Studies, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Regression Analysis, Risk Factors, ROC Curve, Sex Factors, Stroke}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.113.003044}, author = {Ibrahim-Verbaas, Carla A and Fornage, Myriam and Bis, Joshua C and Choi, Seung Hoan and Psaty, Bruce M and Meigs, James B and Rao, Madhu and Nalls, Mike and Fontes, Jo{\~a}o D and O{\textquoteright}Donnell, Christopher J and Kathiresan, Sekar and Ehret, Georg B and Fox, Caroline S and Malik, Rainer and Dichgans, Martin and Schmidt, Helena and Lahti, Jari and Heckbert, Susan R and Lumley, Thomas and Rice, Kenneth and Rotter, Jerome I and Taylor, Kent D and Folsom, Aaron R and Boerwinkle, Eric and Rosamond, Wayne D and Shahar, Eyal and Gottesman, Rebecca F and Koudstaal, Peter J and Amin, Najaf and Wieberdink, Renske G and Dehghan, Abbas and Hofman, Albert and Uitterlinden, Andr{\'e} G and DeStefano, Anita L and Debette, Stephanie and Xue, Luting and Beiser, Alexa and Wolf, Philip A and DeCarli, Charles and Ikram, M Arfan and Seshadri, Sudha and Mosley, Thomas H and Longstreth, W T and van Duijn, Cornelia M and Launer, Lenore J} } @article {7139, title = {Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Jul 7}, pages = {40-55}, abstract = {

Platelet production, maintenance, and clearance are tightly controlled processes indicative of platelets{\textquoteright} important roles in hemostasis and thrombosis. Platelets are common targets for primary and secondary prevention of several conditions. They are monitored clinically by complete blood counts, specifically with measurements of platelet count (PLT) and mean platelet volume (MPV). Identifying genetic effects on PLT and MPV can provide mechanistic insights into platelet biology and their role in disease. Therefore, we formed the Blood Cell Consortium (BCX) to perform a large-scale meta-analysis of Exomechip association results for PLT and MPV in 157,293 and 57,617 individuals, respectively. Using the low-frequency/rare coding variant-enriched Exomechip genotyping array, we sought to identify genetic variants associated with PLT and MPV. In addition to confirming 47 known PLT and 20 known MPV associations, we identified 32 PLT and 18 MPV associations not previously observed in the literature across the allele frequency spectrum, including rare large effect (FCER1A), low-frequency (IQGAP2, MAP1A, LY75), and common (ZMIZ2, SMG6, PEAR1, ARFGAP3/PACSIN2) variants. Several variants associated with PLT/MPV (PEAR1, MRVI1, PTGES3) were also associated with platelet reactivity. In concurrent BCX analyses, there was overlap of platelet-associated variants with red (MAP1A, TMPRSS6, ZMIZ2) and white (PEAR1, ZMIZ2, LY75) blood cell traits, suggesting common regulatory pathways with shared genetic architecture among these hematopoietic lineages. Our large-scale Exomechip analyses identified previously undocumented associations with platelet traits and further indicate that several complex quantitative hematological, lipid, and cardiovascular traits share genetic factors.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.05.005}, author = {Eicher, John D and Chami, Nathalie and Kacprowski, Tim and Nomura, Akihiro and Chen, Ming-Huei and Yanek, Lisa R and Tajuddin, Salman M and Schick, Ursula M and Slater, Andrew J and Pankratz, Nathan and Polfus, Linda and Schurmann, Claudia and Giri, Ayush and Brody, Jennifer A and Lange, Leslie A and Manichaikul, Ani and Hill, W David and Pazoki, Raha and Elliot, Paul and Evangelou, Evangelos and Tzoulaki, Ioanna and Gao, He and Vergnaud, Anne-Claire and Mathias, Rasika A and Becker, Diane M and Becker, Lewis C and Burt, Amber and Crosslin, David R and Lyytik{\"a}inen, Leo-Pekka and Nikus, Kjell and Hernesniemi, Jussi and K{\"a}h{\"o}nen, Mika and Raitoharju, Emma and Mononen, Nina and Raitakari, Olli T and Lehtim{\"a}ki, Terho and Cushman, Mary and Zakai, Neil A and Nickerson, Deborah A and Raffield, Laura M and Quarells, Rakale and Willer, Cristen J and Peloso, Gina M and Abecasis, Goncalo R and Liu, Dajiang J and Deloukas, Panos and Samani, Nilesh J and Schunkert, Heribert and Erdmann, Jeanette and Fornage, Myriam and Richard, Melissa and Tardif, Jean-Claude and Rioux, John D and Dub{\'e}, Marie-Pierre and de Denus, Simon and Lu, Yingchang and Bottinger, Erwin P and Loos, Ruth J F and Smith, Albert Vernon and Harris, Tamara B and Launer, Lenore J and Gudnason, Vilmundur and Velez Edwards, Digna R and Torstenson, Eric S and Liu, Yongmei and Tracy, Russell P and Rotter, Jerome I and Rich, Stephen S and Highland, Heather M and Boerwinkle, Eric and Li, Jin and Lange, Ethan and Wilson, James G and Mihailov, Evelin and M{\"a}gi, Reedik and Hirschhorn, Joel and Metspalu, Andres and Esko, T{\~o}nu and Vacchi-Suzzi, Caterina and Nalls, Mike A and Zonderman, Alan B and Evans, Michele K and Engstr{\"o}m, Gunnar and Orho-Melander, Marju and Melander, Olle and O{\textquoteright}Donoghue, Michelle L and Waterworth, Dawn M and Wallentin, Lars and White, Harvey D and Floyd, James S and Bartz, Traci M and Rice, Kenneth M and Psaty, Bruce M and Starr, J M and Liewald, David C M and Hayward, Caroline and Deary, Ian J and Greinacher, Andreas and V{\"o}lker, Uwe and Thiele, Thomas and V{\"o}lzke, Henry and van Rooij, Frank J A and Uitterlinden, Andr{\'e} G and Franco, Oscar H and Dehghan, Abbas and Edwards, Todd L and Ganesh, Santhi K and Kathiresan, Sekar and Faraday, Nauder and Auer, Paul L and Reiner, Alex P and Lettre, Guillaume and Johnson, Andrew D} } @article {9037, title = {Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.}, journal = {Am J Hum Genet}, year = {2022}, month = {2022 Mar 31}, abstract = {

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1~s [FEV] and its ratio to forced vital capacity [FEV/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV and FEV/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR]~= 1.24 [95\% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with >= 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p~<~2.2~{\texttimes}~10 with portability gains ranging from 5\% to 28\%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2022.03.007}, author = {Hu, Xiaowei and Qiao, Dandi and Kim, Wonji and Moll, Matthew and Balte, Pallavi P and Lange, Leslie A and Bartz, Traci M and Kumar, Rajesh and Li, Xingnan and Yu, Bing and Cade, Brian E and Laurie, Cecelia A and Sofer, Tamar and Ruczinski, Ingo and Nickerson, Deborah A and Muzny, Donna M and Metcalf, Ginger A and Doddapaneni, Harshavardhan and Gabriel, Stacy and Gupta, Namrata and Dugan-Perez, Shannon and Cupples, L Adrienne and Loehr, Laura R and Jain, Deepti and Rotter, Jerome I and Wilson, James G and Psaty, Bruce M and Fornage, Myriam and Morrison, Alanna C and Vasan, Ramachandran S and Washko, George and Rich, Stephen S and O{\textquoteright}Connor, George T and Bleecker, Eugene and Kaplan, Robert C and Kalhan, Ravi and Redline, Susan and Gharib, Sina A and Meyers, Deborah and Ortega, Victor and Dupuis, Jos{\'e}e and London, Stephanie J and Lappalainen, Tuuli and Oelsner, Elizabeth C and Silverman, Edwin K and Barr, R Graham and Thornton, Timothy A and Wheeler, Heather E and Cho, Michael H and Im, Hae Kyung and Manichaikul, Ani} } @article {9781, title = {Plasma proteome-wide analysis of cerebral small vessel disease identifies novel biomarkers and disease pathways.}, journal = {medRxiv}, year = {2024}, month = {2024 Oct 08}, abstract = {

Cerebral small vessel disease (SVD), as defined by neuroimaging characteristics such as white matter hyperintensities (WMHs), cerebral microhemorrhages (CMHs), and lacunar infarcts, is highly prevalent and has been associated with dementia risk and other clinical sequelae. Although conditions such as hypertension are known to contribute to SVD, little is known about the diverse set of subclinical biological processes and molecular mediators that may also influence the development and progression of SVD. To better understand the mechanisms underlying SVD and to identify novel SVD biomarkers, we used a large-scale proteomic platform to relate 4,877 plasma proteins to MRI-defined SVD characteristics within 1,508 participants of the Atherosclerosis Risk in Communities (ARIC) Study cohort. Our proteome-wide analysis of older adults (mean age: 76) identified 13 WMH-associated plasma proteins involved in synaptic function, endothelial integrity, and angiogenesis, two of which remained associated with late-life WMH volume when measured nearly 20 years earlier, during midlife. We replicated the relationship between 9 candidate proteins and WMH volume in one or more external cohorts; we found that 11 of the 13 proteins were associated with risk for future dementia; and we leveraged publicly available proteomic data from brain tissue to demonstrate that a subset of WMH-associated proteins was differentially expressed in the context of cerebral atherosclerosis, pathologically-defined Alzheimer{\textquoteright}s disease, and cognitive decline. Bidirectional two-sample Mendelian randomization analyses examined the causal relationships between candidate proteins and WMH volume, while pathway and network analyses identified discrete biological processes (lipid/cholesterol metabolism, NF-kB signaling, hemostasis) associated with distinct forms of SVD. Finally, we synthesized these findings to identify two plasma proteins, oligodendrocyte myelin glycoprotein (OMG) and neuronal pentraxin receptor (NPTXR), as top candidate biomarkers for elevated WMH volume and its clinical manifestations.

}, doi = {10.1101/2024.10.07.24314972}, author = {Gomez, Gabriela T and Shi, Liu and Fohner, Alison E and Chen, Jingsha and Yang, Yunju and Fornage, Myriam and Duggan, Michael R and Peng, Zhongsheng and Daya, Gulzar N and Tin, Adrienne and Schlosser, Pascal and Longstreth, W T and Kalani, Rizwan and Sharma, Malveeka and Psaty, Bruce M and Nevado-Holgado, Alejo J and Buckley, Noel J and Gottesman, Rebecca F and Lutsey, Pamela L and Jack, Clifford R and Sullivan, Kevin J and Mosley, Thomas and Hughes, Timothy M and Coresh, Josef and Walker, Keenan A} } @article {9739, title = {Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease.}, journal = {Res Sq}, year = {2024}, month = {2024 Jul 02}, abstract = {

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific mechanism-based treatment. We used Mendelian randomization to combine a unique cerebrospinal fluid (CSF) and plasma pQTL resource with the latest European-ancestry GWAS of MRI-markers of cSVD (white matter hyperintensities, perivascular spaces). We describe a new biological fingerprint of 49 protein-cSVD associations, predominantly in the CSF. We implemented a multipronged follow-up, across fluids, platforms, and ancestries (Europeans and East-Asian), including testing associations of direct plasma protein measurements with MRI-cSVD. We highlight 16 proteins robustly associated in both CSF and plasma, with 24/4 proteins identified in CSF/plasma only. cSVD-proteins were enriched in extracellular matrix and immune response pathways, and in genes enriched in microglia and specific microglial states (integration with single-nucleus RNA sequencing). Immune-related proteins were associated with MRI-cSVD already at age twenty. Half of cSVD-proteins were associated with stroke, dementia, or both, and seven cSVD-proteins are targets for known drugs (used for other indications in directions compatible with beneficial therapeutic effects. This first cSVD proteogenomic signature opens new avenues for biomarker and therapeutic developments.

}, issn = {2693-5015}, doi = {10.21203/rs.3.rs-4535534/v1}, author = {Debette, Stephanie and Caro, Ilana and Western, Daniel and Namba, Shinichi and Sun, Na and Kawaguchi, Shuji and He, Yunye and Fujita, Masashi and Roshchupkin, Gennady and D{\textquoteright}Aoust, Tim and Duperron, Marie-Gabrielle and Sargurupremraj, Murali and Tsuchida, Ami and Koido, Masaru and Ahmadi, Marziehsadat and Yang, Chengran and Timsina, Jigyasha and Ibanez, Laura and Matsuda, Koichi and Suzuki, Yutaka and Oda, Yoshiya and Kanai, Akinori and Jandaghi, Pouria and Munter, Hans Markus and Auld, Dan and Astafeva, Iana and Puerta, Raquel and Rotter, Jerome and Psaty, Bruce and Bis, Joshua and Longstreth, Will and Couffinhal, Thierry and Garcia-Gonzalez, Pablo and Pytel, Vanesa and Marqui{\'e}, Marta and Cano, Amanda and Boada, Merce and Joliot, Marc and Lathrop, Mark and Le Grand, Quentin and Launer, Lenore and Wardlaw, Joanna and Heiman, Myriam and Ruiz, Agustin and Matthews, Paul and Seshadri, Sudha and Fornage, Myriam and Adams, Hieab and Mishra, Aniket and Tr{\'e}gou{\"e}t, David-Alexandre and Okada, Yukinori and Kellis, Manolis and De Jager, Philip and Tzourio, Christophe and Kamatani, Yoichiro and Matsuda, Fumihiko and Cruchaga, Carlos} }