@article {6938, title = {FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals.}, journal = {Hum Mol Genet}, volume = {23}, year = {2014}, month = {2014 Dec 20}, pages = {6961-72}, abstract = {

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 {\texttimes} 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 {\texttimes} 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] \%, P = 2.4 {\texttimes} 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] \%, P = 0.004). The associations with protein (P = 7.5 {\texttimes} 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

}, keywords = {Adult, African Americans, Aged, Alleles, Asian Continental Ancestry Group, Body Mass Index, Dietary Carbohydrates, Dietary Fats, Dietary Proteins, Energy Intake, European Continental Ancestry Group, Female, Gene Frequency, Humans, Male, Middle Aged, Obesity, Polymorphism, Single Nucleotide, Proteins}, issn = {1460-2083}, doi = {10.1093/hmg/ddu411}, author = {Qi, Qibin and Kilpel{\"a}inen, Tuomas O and Downer, Mary K and Tanaka, Toshiko and Smith, Caren E and Sluijs, Ivonne and Sonestedt, Emily and Chu, Audrey Y and Renstrom, Frida and Lin, Xiaochen and {\"A}ngquist, Lars H and Huang, Jinyan and Liu, Zhonghua and Li, Yanping and Asif Ali, Muhammad and Xu, Min and Ahluwalia, Tarunveer Singh and Boer, Jolanda M A and Chen, Peng and Daimon, Makoto and Eriksson, Johan and Perola, Markus and Friedlander, Yechiel and Gao, Yu-Tang and Heppe, Denise H M and Holloway, John W and Houston, Denise K and Kanoni, Stavroula and Kim, Yu-Mi and Laaksonen, Maarit A and J{\"a}{\"a}skel{\"a}inen, Tiina and Lee, Nanette R and Lehtim{\"a}ki, Terho and Lemaitre, Rozenn N and Lu, Wei and Luben, Robert N and Manichaikul, Ani and M{\"a}nnist{\"o}, Satu and Marques-Vidal, Pedro and Monda, Keri L and Ngwa, Julius S and Perusse, Louis and van Rooij, Frank J A and Xiang, Yong-Bing and Wen, Wanqing and Wojczynski, Mary K and Zhu, Jingwen and Borecki, Ingrid B and Bouchard, Claude and Cai, Qiuyin and Cooper, Cyrus and Dedoussis, George V and Deloukas, Panos and Ferrucci, Luigi and Forouhi, Nita G and Hansen, Torben and Christiansen, Lene and Hofman, Albert and Johansson, Ingegerd and J{\o}rgensen, Torben and Karasawa, Shigeru and Khaw, Kay-Tee and Kim, Mi-Kyung and Kristiansson, Kati and Li, Huaixing and Lin, Xu and Liu, Yongmei and Lohman, Kurt K and Long, Jirong and Mikkil{\"a}, Vera and Mozaffarian, Dariush and North, Kari and Pedersen, Oluf and Raitakari, Olli and Rissanen, Harri and Tuomilehto, Jaakko and van der Schouw, Yvonne T and Uitterlinden, Andr{\'e} G and Zillikens, M Carola and Franco, Oscar H and Shyong Tai, E and Ou Shu, Xiao and Siscovick, David S and Toft, Ulla and Verschuren, W M Monique and Vollenweider, Peter and Wareham, Nicholas J and Witteman, Jacqueline C M and Zheng, Wei and Ridker, Paul M and Kang, Jae H and Liang, Liming and Jensen, Majken K and Curhan, Gary C and Pasquale, Louis R and Hunter, David J and Mohlke, Karen L and Uusitupa, Matti and Cupples, L Adrienne and Rankinen, Tuomo and Orho-Melander, Marju and Wang, Tao and Chasman, Daniel I and Franks, Paul W and S{\o}rensen, Thorkild I A and Hu, Frank B and Loos, Ruth J F and Nettleton, Jennifer A and Qi, Lu} } @article {9543, title = {A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.}, journal = {bioRxiv}, year = {2023}, month = {2023 Nov 02}, abstract = {

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 (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of and an intergenic region on chromosome 1.

}, doi = {10.1101/2023.10.30.564764}, 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 Arnett, Donna K 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 and Konigsberg, Iain and Kooperberg, Charles and Kral, Brian G and Li, Changwei and Loos, Ruth J F and Mahaney, Michael C and Martin, Lisa W and Mathias, Rasika A and Minster, Ryan L and Mitchell, Braxton D and Montasser, May E and Morrison, Alanna C and Palmer, Nicholette D and Peyser, Patricia A and Psaty, Bruce M and Raffield, Laura 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 and Vasan, Ramachandran S and Wang, Zhe and Yanek, Lisa R and Yu, Bing and Rice, Kenneth M and Rotter, Jerome I and Peloso, Gina M and Natarajan, Pradeep and Li, Zilin and Liu, Zhonghua and Lin, Xihong} } @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 = {

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.

}, 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} }