Download Online Only Supplement Supplementary Methods A. Individual level

Online Only Supplement
Supplementary Methods
A. Individual level data from UK Biobank
Individual level genetic data was available from 112 338 individuals in UK Biobank,
after excluding related samples, individuals whose genetic sex did not match selfreported sex and extreme outliers. Of these individuals, 38 557 were genotyped using the
Affymetrix UK BiLEVE array and 73 781 were genotyped using the Affymetrix UK
Biobank Axiom Array. Phasing and imputation were performed centrally, by UK
Biobank, using a reference panel combining UK10k and 1000 Genomes samples. 73 355
667 variants were imputed. As recommended by UK Biobank, we excluded any samples
with an information measure < 0.3.
To adjust for the presence of antihypertensive medication, we added 15 mm Hg to
systolic blood pressure and 10 mm Hg to diastolic blood pressure of individuals on
antihypertensive medication at baseline, as in the International Consortium for Blood
Pressure GWAS.(1) Type 2 diabetes and coronary heart disease (CHD) were both
ascertained at baseline by self-report, followed by a verbal interview with a trained nurse
to confirm the diagnosis. Type 2 diabetes was defined as report of type 2 diabetes, report
of type 2 diabetes unspecified, or current use of insulin medication. CHD was defined as
report of previous myocardial infarction or diagnosis of angina or hospitalization for
myocardial infarction (ICD codes I21-I23). Definitions for all outcomes in UK Biobank
are provided in Supp. Table 3.
Of the 112 338 individuals analysed in UK Biobank, 53 327 (47.5%) were men. The
mean age was 56.9 (SD 7.9), the mean systolic blood pressure 143.6 mm Hg (SD 21.8)
and the mean diastolic blood pressure 84.5 mm Hg (SD 11.8), after adjustment for use of
antihypertensive medication. The mean body mass index (BMI) was 27.5 (SD 4.8). 7680
individuals did not have blood pressure recorded at baseline; they were excluded in the
blood pressure analysis. Similarly, 311 individuals did not have BMI recorded, 157 did
not have waist circumference recorded, 165 did not have hip circumference recorded, and
352 did not have waist-to-hip ratio recorded; these individuals were excluded in their
respective analyses.
To combine cardiometabolic trait data from both genome wide association studies and
UK Biobank (for analysis of waist-to-hip ratio (WHR), BMI, waist circumference and hip
circumference), we used inverse variance weighted fixed effects meta-analysis to pool
estimates from UK Biobank with genome wide association study estimates (estimates
from GIANT for WHR, BMI, waist circumference and hip circumference). For WHR,
BMI, waist circumference and hip circumference, estimates were inverse normalized
separately by sex, with adjustment for age, to allow for direct comparison to the GIANT
consortium.
B. Summary level data from genome wide association studies
An overview of the included genome wide association studies is provided in Supp. Table
2.
For lipids (LDL cholesterol, HDL cholesterol, triglycerides and total cholesterol), we
used data from the Global Lipids Genetics Consortium, a meta-analysis of 188 587
individuals of European descent.(2) This GWAS included 37 studies genotyped using the
Illumina Metabochip array as well as an additional 23 studies genotyped using a variety
of arrays.
For glycaemic traits, we used data from the Meta-Analyses of Glucose and
Insulin-related traits Consortium (MAGIC), which included 133 010 individuals of
European ancestry without diabetes.(3) This included studies genotyped using the
Metabochip as well as studies genotyped using various arrays who were imputed to 2.5
million SNPs using the HapMap reference panel. SNPs included on the Metabochip were
then meta-analyzed across studies.
For BMI, WHR, waist circumference and hip circumference, we used data from the
Genetic Investigation of ANthropometric Traits (GIANT) consortium. (4, 5) For WHR,
waist circumference and hip circumference, data from 210,088 individuals of European
ancestry were included. For BMI, data for 322,154 individuals of European ancestry were
included. Individuals were genotyped using various arrays and imputed with the HapMap
reference panel to 2.5 million SNPs.
For estimated glomerular filtration rate (eGFR) and chronic kidney disease, we used data
from the Chronic Kidney Disease Genetics consortium (CKDGen), a meta-analysis of
133 413 individuals of European descent from 49 studies.(6) Individuals were genotyped
using various arrays and imputed with the HapMap reference panel to 2.5 million SNPs.
For coronary heart disease, we used data from the CARDIoGRAM Exome Consortium, a
study of 42 335 patients with coronary heart disease and 78240 controls.(7) Individuals
were predominantly of European ancestry (4710 were of South Asian ancestry). Samples
were genotyped on the Illumina HumanExome BeadChip array or the Illumina
OmniExome array, as previously described. (7)
For one variant (rs10455872) which was not available in the CARDIoGRAM Exome
Consortium, we used an estimate from the CARDIoGRAMplusC4D Consortium, a metaanalysis of up to 184 305 individuals (60 801 CHD cases and 123 504 controls), the
majority (77%) of whom were of European ancestry with remain individuals
predominantly of South Asian (13%) and East Asian descent (6%).(8) Remaining
individuals were of African-American and Hispanic ancestry. This meta-analysis
included 48 studies genotyped using the Illumina Metabochip array and imputed to 38
million variants using a reference panel from the 1000 Genomes Project.
For diabetes, we used data from the DIAbetes Genetics Replication and Meta-analysis
(DIAGRAM) Consortium, a meta-analysis of 34 840 diabetes cases and 114 981 controls,
overwhelmingly of European descent.(9) This meta-analysis included 12 studies
genotyped using a range of arrays and imputed with the HapMap reference panel to 2.5
million SNPs. These studies were meta-analysed with 26 studies genotyped using the
Illumina Metabochip array.
For heart failure, we used data from the Cohorts for Heart and Aging Research in
Genomic Epidemiology Heart Failure (CHARGE-HF) collaboration.(10) This
collaboration included 2526 incident heart failure events among 20 926 European
ancestry participants. This meta-analysis included four studies genotyped using different
arrays and imputed to 2.5 million SNPs using the HapMap reference panel.
Standardization
While GIANT and GLGC reported effect estimates of variants in units of standard
deviations, the MAGIC consortium did not. We wished to standardize betas from this
consortium so that the effect of genetically elevated lipoprotein(a) on cardiometabolic
traits could be uniformly expressed in terms of standard deviations of log-transformed
lipoprotein(a). Therefore, to standardize betas for HbA1c, fasting glucose and two-hour
glucose from the MAGIC consortium, one SD was assumed to correspond to 0.53%, 0.73
mM and 0.56 mM, the pooled standard deviation of studies included in a previous report
from the MAGIC consortium.(11) As a pooled SD for log-transformed fasting insulin
was not available from the MAGIC consortium, we used an estimate of 0.44 from
Framingham.(12)
To express increases in lipid levels in absolute terms, which may be easier for clinicians
to interpret, we calculated population level SDs in lipid levels using the National Health
and Nutrition Examination Survey (NHANES) from 2005-2012.(13) We calculated that
one SD in total cholesterol was 39.8 mg/dl and a one SD in LDL cholesterol was 34.8
mg/dl. To express estimated glomerular filtration rate in absolute terms, we used data
from white individuals in NHANES, assuming that one SD in eGFR corresponds to 49.1
mL/min. (14)
C. Effect of variants on lipoprotein(a) using the Atherosclerosis Risk in Communities
Study
To estimate the effect of each variant on lipoprotein(a) levels, we used individual-level
data from the Atherosclerosis Risk in Communities (ARIC) Study.(15) Phenotype and
genotype data were retrieved from the National Center for Biotechnology Information
dbGAP server (accession: phs000090.v3.p1 and phs000572.v6.p4). ARIC is a community
based study of 15792 white and black participants, aged 45 to 64 years. Participants were
first enrolled in 1987.(16) Lipoprotein(a) levels from visit 1, measured in the ARIC
central lipid laboratory, were analysed.(16)
The cohort used for derivation of the effect of LPA variants on lipoprotein(a) levels was
5772 ARIC participants in whom exome sequencing was performed. These exome
sequences were generated from three independent sequencing efforts, the NHLBI Exome
Sequencing Project, the Alzheimer’s Disease Sequencing Project and the CHARGE
consortium, as previously described.(17) We excluded 2965 individuals who were not of
European ancestry. When then excluded an additional 207 individuals for whom
lipoprotein(a) levels were not measured. As a result, our analysis was restricted to 2758
participants who were of European ancestry and had lipoprotein(a) levels measured. The
lipoprotein(a) assay in ARIC measure lipoprotein(a) protein levels, which represents
approximately one third of total lipoprotein(a) mass.(18) Therefore, to estimate total
lipoprotein(a) mass for each individual, we multiplied the measured lipoprotein(a) protein
levels in mg/dL by 3.(18)
We first calculated the natural logarithm of lipoprotein(a) levels for each participant and
standardized them to units of SD from the mean. We then estimated the effect of each
variant (rs3798220, rs41272114 and rs143431368) on standardized lipoprotein(a) levels,
adjusting for age, sex and five principal components of ancestry (Table 1).
One variant was not available in the exome sequence data (rs10455872, an intronic
variant), nor was it available in the genotyped data for ARIC. To estimate the effect of
this variant on lipoprotein(a) levels, we used an estimate from the PROCARDIS study
(1.18 increase in log-transformed Lp(a) levels), which was also conducted in individuals
of European ancestry. As the effect of LPA variants on log-transformed lp(a) levels differ
slightly between studies due to differences in the distribution of lp(a) levels(19), we
estimated the effect of the variant in units of SD using rs3798220 as a reference. To
estimate the effect of rs10455872 on lp(a) levels in ARIC, we multiplied the effect of
rs3798220 on standardized lp(a) levels in ARIC by the ratio of the effect of rs3798220 in
PROCARDIS to rs10455872 in PROCARDIS: 0.98*(1.18/1.27)=0.91 (Table 1).
D. Myocardial Infarction Genetics Consortium Sequencing and Analysis
The Myocardial Infarction Genetics (MIGen) Consortium exome sequencing was
performed as previously described.(20, 21) The Burrows–Wheeler Aligner algorithm was
used to align reads from participants to the reference genome (hg19). The GATK
HaploTypeCaller was used to jointly call variants. Metrics including Variant Quality
Score Recalibration (VQSR), quality over depth, and strand bias were then used to filter
variants. We excluded samples which were related to other samples, which had high
ratios of heterozygous to non-reference homozygous genotypes, which had high missing
genotypes, which had a discordant genetic gender relative to reports gender, and samples
which were discordant relative to genotype data.
After variant calling and quality controls, the Variant Effect Predictor(22, 23) was used to
annotate variants which were predicted to be loss of function variants: (1) nonsense
mutations that resulted in early termination of the apolipoprotein(a) protein; (2)
frameshift mutations due to insertions or deletions of DNA; or (3) splice-site mutations
which result in an incorrectly spliced protein. The Variant Effect Predictor was also used
to annotate genotyped or imputed loss of function variants in UK Biobank. Loss of
function variants analysed are provided (Supp. Tables 2-3).
We analyzed rare (<1%) and common (> 1%) loss of function variants separately. For
analysis of rare loss of function variants, we pooled rare loss of function variants in
MIGen, testing for the association of a loss of function variant with coronary heart
disease in logistic regression after adjustment for age, sex, cohort and five principle
components. As the common loss of function variant rs41272114 was included in
CARDIOGRAM Exome Consortium, which overlaps with MIGen, we used the effect
size from CARDIOGRAM Exome Consortium in place of MIGen for this variant due to
the larger sample size of CARDIOGRAM Exome Consortium.
Supplementary Tables
Supp. Table 1. Characteristics of participants in UK Biobank
N Individuals
112338
Age ± SD, years
56.9 + 7.9
Male, n (%)
53327 (47.5%)
UK BiLEVE Array, n (%)
38557 (34.3%)
SBP + SD, mm Hg
143.6 + 21.8
DBP + SD, mm Hg
84.5 + 11.8
BMI + SD, kg/m2
27.5 + 4.8
Waist-to-Hip Ratio + SD
0.88 + 0.09
5741 (5.1%)
Diabetes Mellitus, n (%)
Coronary Heart Disease, n (%) 4461 (4.0%)
Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; BP,
blood pressure; SD, standard deviation; BMI, body mass index.
Supp. Table 2. Definitions of outcomes
Outcome
Gastric reflux
Definition (UK Biobank unless otherwise specified)
Inverse variance weighted fixed effects meta-analysis of CARDIOGRAM
Exome Consortium(7) outcome (coronary heart disease) and UK Biobank
outcome:
(1) Myocardial infarction (MI), coronary artery bypass grafting, or
coronary artery angioplasty documented in medical history at time of
enrollment by a trained nurse or
(2) Hospitalization for ICD-10 code for acute myocardial infarction (I21.0,
I21.1, I21.2, I21.4, I21.9) or
(3) Hospitalization for OPCS-4 coded procedure: coronary artery bypass
grafting (K40.1-40.4, K41.1-41.4, K45.1-45.5) or
(4) Hospitalization for OPCS-4 coded procedure: coronary angioplasty ±
stenting (K49.1-49.2, K49.8-49.9, K50.2, K75.1-75.4, K75.8-75.9)
History of atrial fibrillation or flutter during verbal interview with trained
nurse or hospitalization for ICD code I48
History of heart failure during verbal interview with trained nurse or
hospitalization for ICD code I50
History of stroke, ischaemic stroke, or subarachnoid hemorrhage during
verbal interview with trained nurse or hospitalization for ICD codes I60I64
History of peripheral vascular disease or intermittent claudication during
verbal interview with trained nurse or hospitalization for ICD code I74 or
I1739
History of venous thromboembolism, deep vein thrombosis or pulmonary
embolism during verbal interview with trained nurse or hospitalization for
ICD code I26 or I80-I82
History of aortic stenosis during verbal interview with trained nurse or
hospitalization for ICD code I350
Inverse variance weighted fixed effects meta-analysis of DIAGRAM Exome
Consortium outcome (type 2 diabetes) and UK Biobank (history of
diabetes unspecified, type 2 diabetes during verbal interview with trained
nurse or current use of insulin medication)
Chronic Kidney Disease Genetics Consortium outcome(6) (creatinine
estimated glomerular filtration rate <60 ml/min)
History of inflammatory bowel disease, ulcerative colitis or Crohn’s
disease during verbal interview with trained nurse
History of gastric reflux during verbal interview with trained nurse
Gallstone
History of gallstones during verbal interview with trained nurse
Irritable bowel syndrome
Hyperthyroidisim
History of irritable bowel syndrome during verbal interview with trained
nurse
History of hyperthyroidism during verbal interview with trained nurse
Hypothyroidism
History of hypothyroidism during verbal interview with trained nurse
Gout
History of gout during verbal interview with trained nurse
Enlarged prostate
History of enlarged prostate during verbal interview with trained nurse
Uterine fibroids
History of uterine fibroids during verbal interview with trained nurse
Migraine
History of migraine during verbal interview with trained nurse
Depression
History of depression during verbal interview with trained nurse
Anxiety
History of anxiety/panic attacks during verbal interview with trained
Coronary heart disease
Atrial fibrillation/flutter
Heart failure
Stroke
Peripheral vascular disease
Venous thromboembolism
Aortic stenosis
Type 2 Diabetes
Chronic kidney disease
Inflammatory bowel disease
nurse
Osteoporosis
History of osteoporosis during verbal interview with trained nurse
Osteoarthritis
History of osteoarthritis during verbal interview with trained nurse
Sciatica
History of sciatica during verbal interview with trained nurse
Prolapsed disc
History of prolapsed disc/slipped disc during verbal interview with trained
nurse
History of asthma during verbal interview with trained nurse
Asthma
Pneumonia
History of chronic obstructive airways disease, emphysema/chronic
bronchitis or emphysema during verbal interview with trained nurse
History of pneumonia during verbal interview with trained nurse
Hayfever
History of hayfever during verbal interview with trained nurse
Lung cancer
History of lung cancer, small cell lung cancer or non-small cell lung cancer
during verbal interview with trained nurse
History of breast cancer during verbal interview with trained nurse
COPD/Ephysema
Breast cancer
Colorectal cancer
Skin cancer
Prostate cancer
Cervical malignancy
Other cancer
Any cancer
History of large bowel cancer/colorectal cancer, colon cancer/sigmoid
cancer
or rectal cancer during verbal interview with trained nurse
History of skin cancer, malignant melanoma, non-melanoma skin cnacer,
basal cell carcinoma or squamous cell carcinoma during verbal interview
with trained nurse
History of prostate cancer during verbal interview with trained nurse
History of cervical cancer or cin cells at the cervix during verbal interview
with trained nurse
History of any other cancer than lung cancer, breast cancer, colorectal
cancer, skin cancer, prostate cancer or cervical malignancy during verbal
interview with trained nurse
History of any cancer during verbal interview with trained nurse
Abbreviations: COPD, chronic obstructive pulmonary disease; ICD, international
classification of disease
Supp. Table 3. DNA sequence variants in the LPA gene and effects on log-transformed
plasma lipoprotein (a) levels, derived from ARIC.
Variant
Effect
Lipoprotein(a)Lowering Allele
Lipoprotein(a)Raising Allele
Lipoprotein(a)
-Lowering
Allele
Frequency
(UK Biobank)
Effect on
lipoprotein(a)
levels (SD
units)
rs3798220
Missense
(I1891M)
Intron
Splice
Donor
Splice
Acceptor
T
C
0.981
-0.98
A
T
G
C
0.918
0.038
-0.91
-0.62
T
C
0.002
-0.92
rs10455872
rs41272114
rs143431368
Supp. Table 4. Rare loss of function variants in the LPA gene in MIGen
Consequence
CHR:POS_REF/ALT
6:160962168_C/T
6:160962215_T/C
6:160968819_A/G
6:160968821_AT/A
6:160969584_G/C
6:160969693_T/C
6:160977157_G/A
6:160998167_C/T
6:160998230_A/AC
6:160998268_G/A
6:160999621_C/A
6:161006077_C/T
6:161006111_C/T
6:161006178_G/A
6:161006199_G/A
6:161006238_C/G
6:161007501_GTGCT/G
6:161007664_T/C
6:161011975_C/A
6:161011975_C/T
6:161012052_C/T
6:161015050_TG/T
6:161020531_C/T
6:161020621_G/C
6:161020641_G/A
6:161026077_C/T
6:161026238_C/T
6:161027527_C/A
6:161032593_C/T
6:161032694_G/A
6:161056287_AAGGAGCTGC
CACAGCAT/A
Stop Gained
Splice Acceptor
Splice Donor
Frameshift Variant
Stop Gained
Splice Acceptor
Stop Gained
Splice Donor
Frameshift Variant
Stop Gained
Stop Gained
Splice Donor
Stop Gained
Stop Gained
Stop Gained
Splice Acceptor
Frameshift Variant
Splice Acceptor
Splice Donor
Splice Donor
Stop Gained
Frameshift Variant
Splice Donor
Stop Gained
Stop Gained
Splice Donor
Splice Acceptor
Stop Gained
Splice Donor
Stop Gained
Frameshift Variant
Amino
Acid
Change
N of 7153
CHD Cases
N of 8137
Controls
1
0
1
0
6
36
0
2
0
1
0
2
1
0
0
0
0
1
0
1
0
0
0
0
0
0
0
1
3
0
0
1
1
1
5
42
2
5
1
0
1
1
2
3
1
3
1
0
1
0
1
1
1
1
1
1
1
0
6
1
1
0
6:161071470_G/A
1
Abbreviations: CHR, chromosome; POS, position; REF, reference allele; ALT,
alternative allele; CHD, coronary heart disease.
1
Stop Gained
W/*
N/X
S/*
R/*
C/WX
R/*
E/*
W/*
R/*
R/*
ST/X
W/*
Q/X
Y/*
Q/*
E/*
R/*
DAVAAP
/X
R/*
Supp. Table 5. Rare loss of function variants in the LPA gene in UK Biobank
Consequence
CHR:POS_REF/ALT
6:160969584_G/C
6:160969693_T/C
6:160998167_C/T
6:161006178_G/A
6:161006238_C/G
6:161026077_C/T
6:161032593_C/T
Stop Gained
Splice
Acceptor
Splice
Donor
Stop Gained
Splice
Acceptor
Splice
Donor
Splice
Donor
Stop Gained
Amino
Acid
Change
N of 4453 CHD Cases
N of 107538 Controls
S/*
2
85
10
427
0
0
3
19
1
68
13
373
0
7
R/*
R/*
6:161071470_G/A
0
Abbreviations: CHR, chromosome; POS, position; REF, reference allele; ALT,
alternative allele; CHD, coronary heart disease.
7
Supp. Table 6. Distribution of the LPA gene variant score in UK Biobank
LPA gene variant score (SD)
Number of Participants
Proportion of Participants
< -2.5
> -2.5, <-1.5
> -1.5, <-0.5
> -0.5, <0.5
> 0.5, <1.5
> 1.5
1
1118
19318
83897
7987
17
0.00089%
0.1%
17%
75%
7.1%
0.015%
Supplementary Figures
Supp. Figure 1. Association of a one standard deviation genetically-lowered logtransformed lipoprotein(a) with prevalent coronary heart disease by age, sex, diabetes
status and body mass index. Estimates were derived from UK Biobank, using logistic
regression, adjusting for age, sex, ten principal components of ancestry and array type.
Abbreviations: OR, odds ratio; SD, standard deviation; BMI, body mass index.
CHD Cases
Controls
Supp. Figure 2. Association of loss of function variants in the LPA gene with risk of
coronary heart disease. Estimates from MIGen and UK Biobank were derived using
logistic regression, with adjustment for sex, cohort and five principal components of
ancestry for the Myocardial Genetics Consortium, and adjustment for age, sex, ten
principal components of ancestry and array type for UK Biobank. Abbreviations: OR,
odds ratio; CHD, coronary heart disease; MIGen, Myocardial Genetics Consortium.
Supplementary References
1. International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret
GB, Munroe PB, et al. Genetic variants in novel pathways influence blood pressure and
cardiovascular disease risk. Nature 2011;478:103–109.
2. Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, et al. Discovery and
refinement of loci associated with lipid levels. Nat. Genet. 2013;45:1274–1283.
3. Scott RA, Lagou V, Welch RP, et al. Large-scale association analyses identify new
loci influencing glycemic traits and provide insight into the underlying biological
pathways. Nat. Genet. 2012;44:991–1005.
4. Shungin D, Winkler TW, Croteau-Chonka DC, et al. New genetic loci link adipose and
insulin biology to body fat distribution. Nature 2015;518:187–196.
5. Locke AE, Kahali B, Berndt SI, et al. Genetic studies of body mass index yield new
insights for obesity biology. Nature 2015;518:197–206.
6. Pattaro C, Teumer A, Gorski M, et al. Genetic associations at 53 loci highlight cell
types and biological pathways relevant for kidney function. Nat Commun 2016;7:10023.
7. Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia Investigators.
Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease. N.
Engl. J. Med. 2016;374:1134–1144.
8. CARDIoGRAMplusC4D Consortium. A comprehensive 1000 Genomes-based
genome-wide association meta-analysis of coronary artery disease. Nat. Genet.
2015;47:1121–1130.
9. Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides
insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet.
2012;44:981–990.
10. Smith NL, Felix JF, Morrison AC, et al. Association of genome-wide variation with
the risk of incident heart failure in adults of European and African ancestry: a prospective
meta-analysis from the cohorts for heart and aging research in genomic epidemiology
(CHARGE) consortium. Circ Cardiovasc Genet 2010;3:256–266.
11. Soranzo N, Sanna S, Wheeler E, et al. Common variants at 10 genomic loci influence
hemoglobin A₁(C) levels via glycemic and nonglycemic pathways. Diabetes
2010;59:3229–3239.
12. Meigs JB, D'Agostino RB, Wilson PW, Cupples LA, Nathan DM, Singer DE. Risk
variable clustering in the insulin resistance syndrome. The Framingham Offspring Study.
Diabetes 1997;46:1594–1600.
13. Carroll MD, Kit BK, Lacher DA, Shero ST, Mussolino ME. Trends in lipids and
lipoproteins in US adults, 1988-2010. JAMA : the journal of the American Medical
Association 2012;308:1545–1554.
14. Foley RN, Wang C, Ishani A, Collins AJ. NHANES III: influence of race on GFR
thresholds and detection of metabolic abnormalities. Journal of the American Society of
Nephrology : JASN 2007;18:2575–2582.
15. Sharrett AR, Ballantyne CM, Coady SA, et al. Coronary heart disease prediction from
lipoprotein cholesterol levels, triglycerides, lipoprotein(a), apolipoproteins A-I and B,
and HDL density subfractions: The Atherosclerosis Risk in Communities (ARIC) Study.
Circulation 2001;104:1108–1113.
16. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The
ARIC investigators. American journal of epidemiology 1989;129:687–702.
17. Li AH, Morrison AC, Kovar C, et al. Analysis of loss-of-function variants and 20 risk
factor phenotypes in 8,554 individuals identifies loci influencing chronic disease. Nat.
Genet. 2015;47:640–642.
18. Virani SS, Brautbar A, Davis BC, et al. Associations between lipoprotein(a) levels
and cardiovascular outcomes in black and white subjects: the Atherosclerosis Risk in
Communities (ARIC) Study. Circulation 2012;125:241–249.
19. Clarke R, Peden JF, Hopewell JC, et al. Genetic variants associated with Lp(a)
lipoprotein level and coronary disease. N. Engl. J. Med. 2009;361:2518–2528.
20. Do R, Stitziel NO, Won H-H, et al. Exome sequencing identifies rare LDLR and
APOA5 alleles conferring risk for myocardial infarction. Nature 2015;518:102–106.
21. Khera AV, Won H-H, Peloso GM, et al. Diagnostic Yield of Sequencing Familial
Hypercholesterolemia Genes in Patients with Severe Hypercholesterolemia. J. Am. Coll.
Cardiol. 2016.
22. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the
consequences of genomic variants with the Ensembl API and SNP Effect Predictor.
Bioinformatics 2010;26:2069–2070.
23. Karczewski KJ. LOFTEE (Loss-Of-Function Transcript Effect Estimator). 2015.
Available at: https://github.com/konradjk/loftee.