@article {9911,
	title = {Methylome-wide association analyses of lipids and modifying effects of behavioral factors in diverse race and ethnicity participants.},
	journal = {Clin Epigenetics},
	volume = {17},
	year = {2025},
	month = {2025 Apr 02},
	pages = {54},
	abstract = {<p>Circulating lipid concentrations are clinically associated with cardiometabolic diseases. The phenotypic variance explained by identified genetic variants remains limited, highlighting the importance of searching for additional factors beyond genetic sequence variants. DNA methylation has been linked to lipid concentrations in previous studies, although most of the studies harbored moderate sample sizes and exhibited underrepresentation of non-European ancestry populations. In addition, knowledge of nongenetic factors on lipid profiles is extremely limited. In the Population Architecture Using Genomics and Epidemiology (PAGE) Study, we performed methylome-wide association analysis on 9,561 participants from diverse race and ethnicity backgrounds for HDL-c, LDL-c, TC, and TG levels, and also tested interactions between smoking or alcohol intake and methylation in their association with lipid levels. We identified novel CpG sites at 16 loci (P < 1.18E-7) with successful replication on 3,215 participants. One additional novel locus was identified in the self-reported White participants (P = 4.66E-8). Although no additional CpG sites were identified in the genome-wide interaction analysis, 13 reported CpG sites showed significant heterogeneous association across smoking or alcohol intake strata. By mapping novel and reported CpG sites to genes, we identified enriched pathways directly linked to lipid metabolism as well as ones spanning various biological functions. These findings provide new insights into the regulation of lipid concentrations.</p>},
	keywords = {Adult, Aged, Alcohol Drinking, CpG Islands, DNA Methylation, Ethnicity, Female, Genome-Wide Association Study, Humans, Lipids, Male, Middle Aged, Racial Groups, Smoking, White People},
	issn = {1868-7083},
	doi = {10.1186/s13148-025-01859-3},
	author = {Hu, Yao and Haessler, Jeff and Lundin, Jessica I and Darst, Burcu F and Whitsel, Eric A and Grove, Megan and Guan, Weihua and Xia, Rui and Szeto, Mindy and Raffield, Laura M and Ratliff, Scott and Wang, Yuxuan and Wang, Xuzhi and Fohner, Alison E and Lynch, Megan T and Patel, Yesha M and Lani Park, S and Xu, Huichun and Mitchell, Braxton D and Bis, Joshua C and Sotoodehnia, Nona and Brody, Jennifer A and Psaty, Bruce M and Peloso, Gina M and Tsai, Michael Y and Rich, Stephen S and Rotter, Jerome I and Smith, Jennifer A and Kardia, Sharon L R and Reiner, Alex P and Lange, Leslie and Fornage, Myriam and Pankow, James S and Graff, Mariaelisa and North, Kari E and Kooperberg, Charles and Peters, Ulrike}
}
@article {9899,
	title = {A statistical framework for multi-trait rare variant analysis in large-scale whole-genome sequencing studies.},
	journal = {Nat Comput Sci},
	volume = {5},
	year = {2025},
	month = {2025 Feb},
	pages = {125-143},
	abstract = {<p>Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally scalable analytical pipeline for functionally informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits in 61,838 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered and replicated new associations with lipid traits missed by single-trait analysis.</p>},
	keywords = {Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Lipids, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Whole Genome Sequencing},
	issn = {2662-8457},
	doi = {10.1038/s43588-024-00764-8},
	author = {Li, Xihao and Chen, Han and Selvaraj, Margaret Sunitha and Van Buren, Eric and Zhou, Hufeng and Wang, Yuxuan and Sun, Ryan and McCaw, Zachary R and Yu, Zhi and Jiang, Min-Zhi and DiCorpo, Daniel and Gaynor, Sheila M and Dey, Rounak and Arnett, Donna K and Benjamin, Emelia J and Bis, Joshua C and Blangero, John and Boerwinkle, Eric and Bowden, Donald W and Brody, Jennifer A and Cade, Brian E and Carson, April P and Carlson, Jenna C and Chami, Nathalie and Chen, Yii-Der Ida and Curran, Joanne E and de Vries, Paul S and Fornage, Myriam and Franceschini, Nora and Freedman, Barry I and Gu, Charles and Heard-Costa, Nancy L and He, Jiang and Hou, Lifang and Hung, Yi-Jen and Irvin, Marguerite R and Kaplan, Robert C and Kardia, Sharon L R and Kelly, Tanika N and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Li, Yun and Lin, Honghuang and Liu, Ching-Ti and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Naseri, Take and North, Kari E and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Redline, Susan and Reiner, Alexander P and Rich, Stephen S and Sitlani, Colleen M and Smith, Jennifer A and Taylor, Kent D and Tiwari, Hemant K and Vasan, Ramachandran S and Viali, Satupa{\textquoteright}itea and Wang, Zhe and Wessel, Jennifer and Yanek, Lisa R and Yu, Bing and Dupuis, Jos{\'e}e and Meigs, James B and Auer, Paul L and Raffield, Laura M and Manning, Alisa K and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong}
}
