Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes

Hindy, George; Dornbos, Peter; Chaffin, Mark; Liu, Dajiang; Wang, Minxian; Selvaraj, Margaret Sunitha; Zhang, David; Park, Joseph; Aguilar-Salinas, Carlos A.; Antonacci-Fulton, Lucinda; Ardissino, Diego; Arnett, Donna K.; Aslibekyan, Stella; Atzmon, Gil; Ballantyne, Christie M.; Barajas‐Olmos, Francisco; Barzilai, Nir; Becker, Lewis C.; Bielak, Lawrence F.; Bis, Joshua C.; Blangero, John; Boerwinkle, Eric; Bonnycastle, Lori L.; Böttinger, Erwin P.; Bowden, Donald W.; Bown, Matthew J.; Brody, Jennifer A.; Broome, Jai; Burtt, Noél P.; Cade, Brian E.; Centeno-Cruz, Federico; Chan, Edmund; Chang, Yi‐Cheng; Chen, Yii‐Der I.; Cheng, Ching-Yu; Choi, Won Jung; Chowdhury, Rajiv; Contreras-Cubas, Cecilia; Córdova, Emilio J.; Correa, Adolfo; Cupples, L. Adrienne; Curran, Joanne E.; Danesh, John; Vries, Paul S. de; DeFronzo, Ralph A.; Doddapaneni, Harshavardhan; Duggirala, Ravindranath; Dutcher, Susan K.; Ellinor, Patrick T.; Emery, Leslie S.; Florez, José C.; Fornage, Myriam; Freedman, Barry I.; Fuster, Valentín; Garay‐Sevilla, Ma. Eugenia; Garcia‐Ortíz, Humberto; Germer, Søren; Gibbs, Richard A.; Gieger, Christian; Gläser, Benjamin; González, Clicerio; González-Villalpando, María Elena; Graff, Mariaelisa; Graham, Sarah E.; Grarup, Niels; Groop, Leif; Guo, Xiuqing; Gupta, Namrata; Han, Sohee; Hanis, Craig L.; Hansen, Torben; He, Jiang; Heard‐Costa, Nancy L.; Hung, Yi‐Jen; Hwang, Mi Yeong; Irvin, Marguerite R.; Islas-Andrade, Sergio; Jarvik, Gail P.; Kang, Hyun Min; Kardia, Sharon L.R.; Kelly, Tanika N.; Kenny, Eimear E.; Khan, Alyna; Kim, Bong-Jo; Kim, Ryan W.; Kim, Young Jin; Koistinen, Heikki A.; Kooperberg, Charles; Kuusisto, Johanna; Kwak, Soo Heon; Laakso, Markku; Lange, Leslie A.; Lee, Jiwon; Lee, Juyoung; Lee, Seonwook; Lehman, Donna M.; Lemaître, Rozenn N.; Linneberg, Allan; Li, Jianjun; Loos, Ruth J.F.; Lubitz, Steven A.; Lyssenko, Valeriya; Ma, Rong; Martin, Lisa W.; Martínez‐Hernández, Angélica; Mathias, Rasika A.; McGarvey, Stephen T.; McPherson, Ruth; Meigs, James B.; Meitinger, Thomas; Melander, Olle; Mendoza-Caamal, Elvia; Metcalf, Ginger; Mi, Xuenan; Mohlke, Karen L.; Montasser, May E.; Moon, Jee-Young; Moreno-Macías, Hortensia; Morrison, Alanna C.; Muzny, Donna M.; Nelson, Sarah C.; Nilsson, Peter M.; O’Connell, Jeffrey R.; Orho‐Melander, Marju; Orozco, Lorena; Palmer, Colin N. A.; Palmer, Nicholette D.; Park, Cheol Joo; Park, Kyong Soo; Pedersen, Oluf; Peralta, Juan M.; Peyser, Patricia A.; Post, Wendy S.; Preuss, Michael; Psaty, Bruce M.; Qi, Qibin; Rao, D. C.; Redline, Susan; Reiner, Alexander P.; Rodríguez‐Padilla, Cristina; Rich, Stephen S.; Samani, Nilesh J.; Schunkert, Heribert; Schurmann, Claudia; Seo, Daekwan; Seo, Jeong‐Sun; Sim, Xueling; Sladek, Rob; Small, Kerrin S.; So, Wing Yee; Stilp, Adrienne M.; Tai, E Shyong; Tam, Claudia H.T.; Taylor, Kent D.; Teo, Yik Ying; Thameem, Farook; Tomlinson, Brian; Tsai, Michael Y.; Tuomi, Tiinamaija; Tuomilehto, Jaakko; Tusié-Luna, Teresa; Udler, Miriam S.; Dam, Rob M. van; Vasan, Ramachandran S.; Martinez, Karine A. Viaud; Wang, Feifei; Wang, Xuzhi; Watkins, Hugh; Weeks, Daniel E.; Wilson, James G.; Witte, Daniel R.; Wong, Tien‐Yin; Yanek, Lisa R.; Kathiresan, Sekar; Rader, Daniel J.; Rotter, Jerome I.; Boehnke, Michael; McCarthy, Mark; Willer, Cristen J.; Natarajan, Pradeep; Flannick, Jason; Khera, Amit V.; Peloso, Gina M.

doi:10.1016/j.ajhg.2021.11.021

Cited by 28 publications

(31 citation statements)

References 78 publications

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“…genes with rare-variant burden for lipid phenotypes in a recent multi-ancestry analysis 33 (30.82-fold enrichment, p ¼ 1.77 3 10 À16 , including APOB, LPL, LIPG, and ANGPTL4), confirming shared mechanisms of rare and common variation underlying lipid traits; 49 (4) genes implicated by cholesterol or lipidemia phenotypes in mouse knockouts (3.92-fold enrichment, p ¼ 2.18eÀ20), suggesting the shared genetic basis of lipid traits between human and mouse. 50 Colocalized genes also showed enrichment with genes implicated in TWAS (Table S4) run on the same GWAS and eQTL summary statistics (20.14-fold enrichment, p < 2.22eÀ308).…”

Section: Resultsmentioning

confidence: 99%

“…Second, we used 35 genes with rare-coding variants associated with lipid levels. 33 Third, we extracted 1,115 genes associated with ''cholesterol'' or ''lipidemia'' phenotypes in mouse knockouts from the Mouse Genome Informatics (MGI) database. 34 Fourth, we identified 4,008 genes from a transcriptome-wide association study (TWAS) on the same GWAS and GTEx v8 summary statistics using the S-PrediXcan software 35 default setup.…”

Section: Enrichment In Known Lipid-associated Genesmentioning

confidence: 99%

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A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Ramdas

Judd

Graham

et al. 2022

The American Journal of Human Genetics

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Section: Resultsmentioning

confidence: 99%

Section: Enrichment In Known Lipid-associated Genesmentioning

confidence: 99%

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Ramdas

Judd

Graham

et al. 2022

The American Journal of Human Genetics

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“…Some recent studies of the UK Biobank have also noted that PLIN1 is associated with higher HDL cholesterol ( 11 , 12 ), but not all have noted the direction of effect. Recently, Hindy and colleagues identified 35 genes associated with lipid levels ( 13 ). PLIN1 PTVs were associated with higher HDL cholesterol (3.9 mg/dL, P = 1 × 10 –5 ) and nominally with reduced triglycerides (–7%, P = .02).…”

Section: Discussionmentioning

confidence: 99%

PLIN1 Haploinsufficiency Causes a Favorable Metabolic Profile

Patel

Burman

Laver

et al. 2022

The Journal of Clinical Endocrinology &Amp; Metabolism

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Context PLIN1 encodes Perilipin-1 which coats lipid droplets in adipocytes and is involved in droplet formation, triglyceride storage and lipolysis. Rare PLIN1 frameshift variants that extend the translated protein have been described to cause lipodystrophy. Objective To test whether PLIN1 protein-truncating variants cause lipodystrophy in a large population-based cohort. Design We identified individuals with PLIN1 PTVs in individuals with exome data in UK Biobank. We performed gene-burden testing for individuals with PLIN1 PTVs. We replicated the associations using data from the T2D Knowledge portal. We performed a phenome-wide association study using publicly available association statistics. Setting A population-based cohort and a T2D case/control study. Participants 362,791 individuals in UK Biobank and 43,125 individuals in the T2D Knowledge portal. Main Outcome Measures: Twenty-two diseases and traits relevant to lipodystrophy. Results The 735 individuals with PLIN1 protein-truncating variants had a favourable metabolic profile. These individuals had increased HDL cholesterol (0.12mmol/L, 95% CI: 0.09, 0.14, P=2x10 -18), reduced triglycerides (-0.22 mmol/L 95% CI: -0.29, -0.14, P=3x10 -11), reduced waist hip ratio (-0.02, 95% CI: -0.02, -0.01, P=9x10 -12) and reduced systolic blood pressure (-1.67 mmHg, -3.25, -0.09, P=0.05). These associations were consistent in the smaller T2D knowledge portal cohort. In UK Biobank, PLIN1 PTVs were associated with reduced risk of myocardial infarction (OR=0.59, 95% CI: 0.35, 0.93, P=0.02) and hypertension (OR=0.85, 95% CI: 0.73, 0.98, P=0.03), but not Type 2 diabetes (OR=0.99 95% CI: 0.63,1.51, P=0.99). Conclusion Our study suggests that PLIN1 haploinsufficiency causes a favourable metabolic profile and may protect against cardiovascular disease.

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“…It is of note that we have previously used WES to search for low-frequency and rare variants for DKD in individuals with type 1 diabetes [ 11 , 12 ]. A recent exome sequencing of >170,000 individuals identified rare coding variants in 35 genes for total cholesterol, LDLC, HDLC, triglycerides, or their ratios [ 13 ]. Indeed, identification of rare loss-of-function variants may reveal genes that can be targeted to prevent disease, such as the LDLC-lowering loss-of-function variants in PCSK9 , the identification of which resulted in the PCSK9 inhibitors for preventing CVD [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, previous studies on PAVs for lipid traits were either limited to exome-focused genotyping arrays [ 8 ], individuals with suspected monogenic dyslipidemias [ 15 ], or simple clinical lipid measurements, e.g., total cholesterol, HDLC, and LDLC [ 9 , 13 , 16 ]. Lipidomic profiles consisting of more detailed lipid and lipoprotein subtypes can increase our understanding of the complex lipidomic regulatory networks and, occasionally, outperform the traditional lipid variables in risk prediction [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations

Sandholm

Hotakainen²,

Haukka³

et al. 2022

Genome Med

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Background Dyslipidemia is a major risk factor for cardiovascular disease, and diabetes impacts the lipid metabolism through multiple pathways. In addition to the standard lipid measurements, apolipoprotein concentrations provide added awareness of the burden of circulating lipoproteins. While common genetic variants modestly affect the serum lipid concentrations, rare genetic mutations can cause monogenic forms of hypercholesterolemia and other genetic disorders of lipid metabolism. We aimed to identify low-frequency protein-altering variants (PAVs) affecting lipoprotein and lipid traits. Methods We analyzed whole-exome (WES) and whole-genome sequencing (WGS) data of 481 and 474 individuals with type 1 diabetes, respectively. The phenotypic data consisted of 79 serum lipid and apolipoprotein phenotypes obtained with clinical laboratory measurements and nuclear magnetic resonance spectroscopy. Results The single-variant analysis identified an association between the LIPC p.Thr405Met (rs113298164) and serum apolipoprotein A1 concentrations (p=7.8×10−8). The burden of PAVs was significantly associated with lipid phenotypes in LIPC, RBM47, TRMT5, GTF3C5, MARCHF10, and RYR3 (p<2.9×10−6). The RBM47 gene is required for apolipoprotein B post-translational modifications, and in our data, the association between RBM47 and apolipoprotein C-III concentrations was due to a rare 21 base pair p.Ala496-Ala502 deletion; in replication, the burden of rare deleterious variants in RBM47 was associated with lower triglyceride concentrations in WES of >170,000 individuals from multiple ancestries (p=0.0013). Two PAVs in GTF3C5 were highly enriched in the Finnish population and associated with cardiovascular phenotypes in the general population. In the previously known APOB gene, we identified novel associations at two protein-truncating variants resulting in lower serum non-HDL cholesterol (p=4.8×10−4), apolipoprotein B (p=5.6×10−4), and LDL cholesterol (p=9.5×10−4) concentrations. Conclusions We identified lipid and apolipoprotein-associated variants in the previously known LIPC and APOB genes, as well as PAVs in GTF3C5 associated with LDLC, and in RBM47 associated with apolipoprotein C-III concentrations, implicated as an independent CVD risk factor. Identification of rare loss-of-function variants has previously revealed genes that can be targeted to prevent CVD, such as the LDL cholesterol-lowering loss-of-function variants in the PCSK9 gene. Thus, this study suggests novel putative therapeutic targets for the prevention of CVD.

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Rare coding variants in 35 genes associate with circulating lipid levels—A multi-ancestry analysis of 170,000 exomes

Cited by 28 publications

References 78 publications

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

PLIN1 Haploinsufficiency Causes a Favorable Metabolic Profile

Whole-exome sequencing identifies novel protein-altering variants associated with serum apolipoprotein and lipid concentrations

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