Weighted burden analysis has been used in exome-sequenced case-control studies to identify genes in which there is an excess of rare and/or functional variants associated with phenotype. Implementation in a ridge regression framework allows simultaneous analysis of all variants along with relevant covariates, such as population principal components. In order to apply the approach to a quantitative phenotype, a weighted burden score is derived for each subject and included in a linear regression analysis. The weighting scheme is adjusted in order to apply differential weights to rare and very rare variants and a score is derived based on both the frequency and predicted effect of each variant. When applied to an ethnically heterogeneous dataset consisting of 49,790 exome-sequenced UK Biobank subjects and using body mass index as the phenotype, the method produces a very inflated test statistic. However, this is almost completely corrected by including 20 population principal components as covariates. When this is done, the top 30 genes include a few which are quite plausibly associated with the phenotype, including LYPLAL1 and NSDHL. This approach offers a way to carry out gene-based analyses of rare variants identified by exome sequencing in heterogeneous datasets without requiring that data from ethnic minority subjects be discarded. This research has been conducted using the UK Biobank Resource.

1.
Curtis
D
.
A rapid method for combined analysis of common and rare variants at the level of a region, gene, or pathway
.
Adv Appl Bioinform Chem
.
2012
;
5
:
1
9
.
[PubMed]
1178-6949
2.
Curtis
D
.
Pathway analysis of whole exome sequence data provides further support for the involvement of histone modification in the aetiology of schizophrenia
.
Psychiatr Genet
.
2016
Oct
;
26
(
5
):
223
7
.
[PubMed]
0955-8829
3.
Zhao
Z
,
Bi
W
,
Zhou
W
,
VandeHaar
P
,
Fritsche
LG
,
Lee
S
.
UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test
.
Am J Hum Genet
.
2020
Jan
;
106
(
1
):
3
12
.
[PubMed]
0002-9297
4.
Cirulli
ET
,
White
S
,
Read
RW
,
Elhanan
G
,
Metcalf
WJ
,
Tanudjaja
F
, et al
Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts
.
Nat Commun
.
2020
Jan
;
11
(
1
):
542
.
[PubMed]
2041-1723
5.
Curtis
D
.
A weighted burden test using logistic regression for integrated analysis of sequence variants, copy number variants and polygenic risk score
.
Eur J Hum Genet
.
2019
Jan
;
27
(
1
):
114
24
.
[PubMed]
1018-4813
6.
Curtis
D
,
Coelewij
L
,
Liu
SH
,
Humphrey
J
,
Mott
R
.
Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology
.
Behav Genet
.
2018
May
;
48
(
3
):
198
208
.
[PubMed]
0001-8244
7.
Curtis
D
,
Bakaya
K
,
Sharma
L
,
Bandyopadhay
S
.
Weighted burden analysis of exome-sequenced late onset Alzheimer’s cases and controls provides further evidence for involvement of PSEN1 and demonstrates protective role for variants in tyrosine phosphatase genes
.
Ann Hum Genet
.
2019
;
84
:
291
302
.
[PubMed]
0003-4800
8.
Van Hout
CV
,
Tachmazidou
I
,
Backman
JD
,
Hoffman
JX
,
Ye
B
,
Pandey
AK
, et al
Whole exome sequencing and characterization of coding variation in 49,960 individuals in the UK Biobank.
BioRxiv
.
2019
:572347.
9.
Genovese
G
,
Fromer
M
,
Stahl
EA
,
Ruderfer
DM
,
Chambert
K
,
Landén
M
, et al
Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia
.
Nat Neurosci
.
2016
Nov
;
19
(
11
):
1433
41
.
[PubMed]
1097-6256
10.
Fry
A
,
Littlejohns
TJ
,
Sudlow
C
,
Doherty
N
,
Adamska
L
,
Sprosen
T
, et al
Comparison of Sociodemographic and Health-Related Characteristics of UK Biobank Participants With Those of the General Population
.
Am J Epidemiol
.
2017
Nov
;
186
(
9
):
1026
34
.
[PubMed]
0002-9262
11.
Zhou
H
,
Sealock
JM
,
Sanchez-Roige
S
,
Clarke
TK
,
Levey
DF
,
Cheng
Z
, et al
Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits
.
Nat Neurosci
.
2020
Jul
;
23
(
7
):
809
18
.
[PubMed]
1097-6256
12.
Xu
Y
,
Yang
XL
,
Yang
XL
,
Ren
YR
,
Zhuang
XY
,
Zhang
L
, et al
Functional Annotations of Single-Nucleotide Polymorphism (SNP)-Based and Gene-Based Genome-Wide Association Studies Show Genes Affecting Keratitis Susceptibility
.
Med Sci Monit
.
2020
May
;
26
:
e922710
.
[PubMed]
1234-1010
13.
Curtis
D
,
Balloux
F
.
Editorial: topical ethical issues in the publication of human genetics research
.
Ann Hum Genet
.
2020
Jul
;
84
(
4
):
313
4
.
[PubMed]
0003-4800
14.
Long
T
,
Hicks
M
,
Yu
HC
,
Biggs
WH
,
Kirkness
EF
,
Menni
C
, et al
Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites
.
Nat Genet
.
2017
Apr
;
49
(
4
):
568
78
.
[PubMed]
1061-4036
15.
King DE. Dlib-ml: A Machine Learning Toolkit. J Mach Learn Res. 2009;10:1755–58.
16.
McLaren
W
,
Gil
L
,
Hunt
SE
,
Riat
HS
,
Ritchie
GR
,
Thormann
A
, et al
The Ensembl Variant Effect Predictor
.
Genome Biol
.
2016
Jun
;
17
(
1
):
122
.
[PubMed]
1474-7596
17.
Adzhubei
I
,
Jordan
DM
,
Sunyaev
SR
.
Predicting functional effect of human missense mutations using PolyPhen-2.
Curr Protoc Hum Genet
2013
;7 Unit7.20.
18.
Kumar
P
,
Henikoff
S
,
Ng
PC
.
Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm
.
Nat Protoc
.
2009
;
4
(
7
):
1073
81
.
[PubMed]
1754-2189
19.
Purcell
S
,
Neale
B
,
Todd-Brown
K
,
Thomas
L
,
Ferreira
MA
,
Bender
D
, et al
PLINK: a tool set for whole-genome association and population-based linkage analyses
.
Am J Hum Genet
.
2007
Sep
;
81
(
3
):
559
75
.
[PubMed]
0002-9297
20.
Chang
CC
,
Chow
CC
,
Tellier
LC
,
Vattikuti
S
,
Purcell
SM
,
Lee
JJ
.
Second-generation PLINK: rising to the challenge of larger and richer datasets
.
Gigascience
.
2015
Feb
;
4
(
1
):
7
.
[PubMed]
2047-217X
21.
Purcell
SM
,
Wray
NR
,
Stone
JL
,
Visscher
PM
,
O’Donovan
MC
,
Sullivan
PF
, et al;
International Schizophrenia Consortium
.
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder
.
Nature
.
2009
Aug
;
460
(
7256
):
748
52
.
[PubMed]
0028-0836
22.
R Core Team
.
R: A language and environment for statistical computing. Vienna, Austria
.
Austria
:
R Foundation for Statistical Computing
;
2014
.
23.
Wickham
H
. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag;
2016
.
24.
Subramanian
A
,
Tamayo
P
,
Mootha
VK
,
Mukherjee
S
,
Ebert
BL
,
Gillette
MA
, et al
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
.
Proc Natl Acad Sci USA
.
2005
Oct
;
102
(
43
):
15545
50
.
[PubMed]
0027-8424
25.
Bian
L
,
Hanson
RL
,
Muller
YL
,
Ma
L
,
Kobes
S
,
Knowler
WC
, et al;
MAGIC Investigators
.
Variants in ACAD10 are associated with type 2 diabetes, insulin resistance and lipid oxidation in Pima Indians
.
Diabetologia
.
2010
Jul
;
53
(
7
):
1349
53
.
[PubMed]
0012-186X
26.
Bloom
K
,
Mohsen
AW
,
Karunanidhi
A
,
El Demellawy
D
,
Reyes-Múgica
M
,
Wang
Y
, et al
Investigating the link of ACAD10 deficiency to type 2 diabetes mellitus
.
J Inherit Metab Dis
.
2018
Jan
;
41
(
1
):
49
57
.
[PubMed]
0141-8955
27.
Watson
RA
,
Gates
AS
,
Wynn
EH
,
Calvert
FE
,
Girousse
A
,
Lelliott
CJ
, et al
Lyplal1 is dispensable for normal fat deposition in mice
.
Dis Model Mech
.
2017
Dec
;
10
(
12
):
1481
8
.
[PubMed]
1754-8403
28.
Scott
RA
,
Lagou
V
,
Welch
RP
,
Wheeler
E
,
Montasser
ME
,
Luan
J
, et al;
DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium
.
Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways
.
Nat Genet
.
2012
Sep
;
44
(
9
):
991
1005
.
[PubMed]
1061-4036
29.
Lotta
LA
,
Gulati
P
,
Day
FR
,
Payne
F
,
Ongen
H
,
van de Bunt
M
, et al;
EPIC-InterAct Consortium
;
Cambridge FPLD1 Consortium
.
Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance
.
Nat Genet
.
2017
Jan
;
49
(
1
):
17
26
.
[PubMed]
1061-4036
30.
Chen
Z
,
Yu
H
,
Shi
X
,
Warren
CR
,
Lotta
LA
,
Friesen
M
, et al
Functional Screening of Candidate Causal Genes for Insulin Resistance in Human Preadipocytes and Adipocytes
.
Circ Res
.
2020
Jan
;
126
(
3
):
330
46
.
[PubMed]
0009-7330
31.
Nie
J
,
Han
X
,
Shi
Y
.
SAD-A and AMPK kinases: the “yin and yang” regulators of mTORC1 signaling in pancreatic β cells
.
Cell Cycle
.
2013
Nov
;
12
(
21
):
3366
9
.
[PubMed]
1538-4101
32.
Kulkarni
SS
,
Karlsson
HK
,
Szekeres
F
,
Chibalin
AV
,
Krook
A
,
Zierath
JR
.
Suppression of 5′-nucleotidase enzymes promotes AMP-activated protein kinase (AMPK) phosphorylation and metabolism in human and mouse skeletal muscle
.
J Biol Chem
.
2011
Oct
;
286
(
40
):
34567
74
.
[PubMed]
0021-9258
33.
McLarren
KW
,
Severson
TM
,
du Souich
C
,
Stockton
DW
,
Kratz
LE
,
Cunningham
D
, et al
Hypomorphic temperature-sensitive alleles of NSDHL cause CK syndrome
.
Am J Hum Genet
.
2010
Dec
;
87
(
6
):
905
14
.
[PubMed]
0002-9297
34.
Ramphul
K
,
Kota
V
,
Mejias
SG
.
Child Syndrome
.
2019
.
35.
Bautz
DJ
,
Broman
KW
,
Threadgill
DW
.
Identification of a novel polymorphism in X-linked sterol-4-alpha-carboxylate 3-dehydrogenase (Nsdhl) associated with reduced high-density lipoprotein cholesterol levels in I/LnJ mice
.
G3 (Bethesda)
.
2013
Oct
;
3
(
10
):
1819
25
.
[PubMed]
2160-1836
36.
Chen
D
,
Wu
P
,
Yang
Q
,
Wang
K
,
Zhou
J
,
Yang
X
, et al
Genome-wide association study for backfat thickness at 100 kg and loin muscle thickness in domestic pigs based on genotyping by sequencing
.
Physiol Genomics
.
2019
Jul
;
51
(
7
):
261
6
.
[PubMed]
1094-8341
37.
Davey
JR
,
Humphrey
SJ
,
Junutula
JR
,
Mishra
AK
,
Lambright
DG
,
James
DE
, et al
TBC1D13 is a RAB35 specific GAP that plays an important role in GLUT4 trafficking in adipocytes
.
Traffic
.
2012
Oct
;
13
(
10
):
1429
41
.
[PubMed]
1398-9219
38.
Lawson
DJ
,
Davies
NM
,
Haworth
S
,
Ashraf
B
,
Howe
L
,
Crawford
A
, et al
Is population structure in the genetic biobank era irrelevant, a challenge, or an opportunity?
Hum Genet
.
2020
Jan
;
139
(
1
):
23
41
.
[PubMed]
0340-6717
Copyright / Drug Dosage / Disclaimer
Copyright: All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher.
Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
Disclaimer: The statements, opinions and data contained in this publication are solely those of the individual authors and contributors and not of the publishers and the editor(s). The appearance of advertisements or/and product references in the publication is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements.
You do not currently have access to this content.