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. 2013 Aug;45(8):899-901.
doi: 10.1038/ng.2671. Epub 2013 Jun 16.

Whole-genome sequence-based analysis of high-density lipoprotein cholesterol

Alanna C Morrison  1 , Arend Voorman, Andrew D Johnson, Xiaoming Liu, Jin Yu, Alexander Li, Donna Muzny, Fuli Yu, Kenneth Rice, Chengsong Zhu, Joshua Bis, Gerardo Heiss, Christopher J O'Donnell, Bruce M Psaty, L Adrienne Cupples, Richard Gibbs, Eric Boerwinkle; Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium
Collaborators, Affiliations

Whole-genome sequence-based analysis of high-density lipoprotein cholesterol

Alanna C Morrison et al. Nat Genet. 2013 Aug.

Abstract

We describe initial steps for interrogating whole-genome sequence data to characterize the genetic architecture of a complex trait, levels of high-density lipoprotein cholesterol (HDL-C). We report whole-genome sequencing and analysis of 962 individuals from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) studies. From this analysis, we estimate that common variation contributes more to heritability of HDL-C levels than rare variation, and screening for mendelian variants for dyslipidemia identified individuals with extreme HDL-C levels. Whole-genome sequencing analyses highlight the value of regulatory and non-protein-coding regions of the genome in addition to protein-coding regions.

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Figures

Figure 1
Figure 1
HDL-C distribution for carriers of identified Mendelian variation. HDL-C levels are adjusted for age, sex, body mass index, and cohort field center. Dotted lines indicate the 10th, 50th, and 90th percentiles.
Figure 2
Figure 2
Survey of the genomic landscape using Lachesis. Global assessment of the genome is accomplished by interrogating common variation in a comprehensive genome-wide association study (super-GWAS)(black dots in Figure 2). Additionally, a sliding window of physical distance (4 kb) was used to evaluate the aggregate contribution of rare (T1, light green line) and all (SKAT, dark green line) variation across the entire genome. Annotation-based analyses at the gene level contrast the contribution to phenotypic variation from protein-coding variation (exon only, orange lines) and the addition of gene-based regulatory regions (exon +, red lines). Non-coding regions, such as miRNAs and lincRNAs, and annotated regulatory regions (ORegAnno) throughout the genome were also evaluated for phenotypic association with the aggregate contribution of rare and common variation in these regulatory domains. Other annotations can be added to these analyses as they emerge.
Figure 2
Figure 2
Survey of the genomic landscape using Lachesis. Global assessment of the genome is accomplished by interrogating common variation in a comprehensive genome-wide association study (super-GWAS)(black dots in Figure 2). Additionally, a sliding window of physical distance (4 kb) was used to evaluate the aggregate contribution of rare (T1, light green line) and all (SKAT, dark green line) variation across the entire genome. Annotation-based analyses at the gene level contrast the contribution to phenotypic variation from protein-coding variation (exon only, orange lines) and the addition of gene-based regulatory regions (exon +, red lines). Non-coding regions, such as miRNAs and lincRNAs, and annotated regulatory regions (ORegAnno) throughout the genome were also evaluated for phenotypic association with the aggregate contribution of rare and common variation in these regulatory domains. Other annotations can be added to these analyses as they emerge.
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