Using genotyping and whole-genome sequencing to identify causal variants associated with complex phenotypes

2014
Using genotyping and whole-genome
causal variants associated with
complex phenotypes
J.B. Cole
sequencing to identify
Animal Genomics and Improvement Laboratory
Agricultural Research Service, USDA
Beltsville, MD
john.cole@ars.usda.gov

Overview
l What have we learned about causal
variants?
l What do we know about chromosome 18?
l How can sequencing help us
learn more?
l What did we learn when we
looked at the data?
l How did we approach these
new challenges?
Source: Ianuzzi (Chromosome
Res., 4:448–456)
Universidade Federal de Viçosa, MG, Brasil 9 September 2014 (2) Cole

Genotypes evaluated
400,000
350,000
300,000
250,000
200,000
150,000
100,000
50,000
0
Jun
A
O
Jan
Young imputed
Old imputed
Female Young <50K
Male Young <50K
Female Old <50K
Male Old <50K
Female Young >=50K
Male Young >=50K
Female Old >=50K
Male Old >=50K
F
A
M
J
J
A
S
O
N
D
Jan
F
M
A
M
J
J
A
S
O
N
D
Jan
F
M
A
M
J
J
A
S
O
N
D
Jan
F
M
A
M
J
J
A
S
Animals genotyped (no.)
2009 2010 2011 2012 2013
Evaluation date

Genotypes received since July 2013
Breed Female Male
All
animals
%
female
Ayrshire 1,359 229 1,588 86
Brown Swiss* 892 6,253 7,145 12
Holstein 172,956 31,657 204,613 85
Jersey** 26,434 4,804 31,238 85
All 201,641 42,943 244,584 82
*Includes >5,000 bulls added from Interbull in June 2014
**Includes 1,068 Danish bulls added in November 2013

Phenotypes may come from genotypes
Name Chrome Location (Mbp) Freq of minor haplotype Gene Name
HH1 5 63.15 1.92 APAF1
HH2 1 94.8 to 96.6 1.66 unknown
HH3 8 95.41 2.95 SMC2
HH4 1 1.27 0.37 GART
HH5 9 92 to 94 2.22 unknown
JH1 15 15.70 12.10 CWC15
BH1 7 42.8 to 47.0 6.67 unknown
BH2 19 10.6 to 11.7 7.78 unknown
AH1 17 65.86 to 66.16 11.80 unknown
For a complete list, see: http://aipl.arsusda.gov/reference/recessive_haplotypes_ARR-G3.html.

Success – APAF1 (HH1)
l APAF1 - Bos taurus apoptotic peptidase activating factor 1
w ATP binding factor
l Gene expression for APAF1 in murine development begins
between 7 and 9 d in heart, mesenchyme, periderm, and primitive
intestine (Muller et al., 2005)
l Gene knockout of APAF1 in mice leads to embryonic lethality
(Muller et al., 2005)
w Proteins required for this
pathway/cascade are important
for neural tube closure in vivo

Success – CWC15 (JH1)
Will and Lührmann. 2011.
Spliceosome structure and
Function. Cold Spring
Harb Perspect Biol.

There’s still a gap to bridge
l Causal variants for Mendelian recessives
are sometimes easy to identify
l Identification of causal variants for QTL
associated with quantitative traits is
much more complex
w It can be done (e.g., DGAT1)
w Does genomics and next generation
sequencing make that easier?

A simple strategy doesn’t always work
l Compute SNP effects for trait of interest
l Look for peaks
l Perform bioinformatics on regions under
interesting peaks
w NCBI/Ensembl
w Bovine Gene Atlas
w Bovine QTLdb
l This doesn’t always work…as we’ll see!

Introduction to chromosome 18
l Several studies (Kuhn et al., 2003; Cole
et al., 2009; Seidenspinner et al., 2009)
have reported QTL on BTA 18 associated
with dystocia
l Bioinformatic analysis using SNP data has
not identified the causal variant
l Next generation sequencing (NGS) has
recently been used to find causal
variants for novel recessive disorders

Chromosome 18 is different
l Markers on chromosome 18 have large effects
on several traits:
w Dystocia and stillbirth: sire and daughter
calving ease and sire stillbirth
w Conformation: rump width, stature,
strength, and body depth
w Efficiency: longevity and net merit
l Large calves contribute to reduced cow
lifetimes and decreased profitability

Marker effects for dystocia complex
AR-BFG-`GS-109285
ARS-BFGL-NGS-109285
Cole et al., 2009 (J. Dairy Sci. 92:2931–2946)
Source: https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO

Correlations in dystocia complex

The QTL also affects gestation length
Maltecca et al., 2011 (Animal Genet. 42:585-591)

The dystocia complex
l The key marker is ARS-BFGL-NGS-109285 at
(rs109478645 ) 57,589,121 Mb on BTA18
l Intronic to Siglec-12 (sialic acid binding Ig-like
lectin 12)
l Recent results indicate effects on gestation
length (Maltecca et al., 2011) and calf birth
weight (Cole et al., 2014), as well as calving
traits (Purfield et al., 2014)

Where did it come from?
Source: http://bit.ly/VsIups
Source: https://www.cdcb.us/CF-queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm?

Who popularized it?
57,861 daughters
>2 million granddaus
Source: http://bit.ly/1BkTTsE.
Maternal haplotype from
Ivanhoe
Source: https://www.cdcb.us/CF-queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm?

This is a gene-rich region
Discussed on Tuesday
(Abstract 288, Mao).
http://useast.ensembl.org/Bos_taurus/Location/View?r=18%3A57583000-57587000
http://www.ncbi.nlm.nih.gov/gene?cmd=Retrieve&dopt=Graphics&list_uids=618463

Copy number variants are present
Hou et al. 2011 (BMC Genomics,12:127)
l ARS-BFGL-NGS-109285 is flanked by CNV
w There’s a loss and a gain to the left (8
SNP region)
w There’s a gain to the right (10 SNP
region)
l This can result in assembly problems

What if we look at a different trait?
l Cole et al. (2009) proposed the following
mechanism:
w Siglec-12 may sequester circulating
leptin
w This increases gestation length
w Calf birth weight (BW) is higher
because of increased gestation length
w Higher BW is associated with dystocia

We don’t have birth weight data
l Birth weights are not routinely recorded
in the US
l Collaborated with Hermann Swalve’s
group to develop a selection index
prediction of BW PTA
l Performed GWAS and gene set
enrichment analysis to search for
interesting associations (Cole et al.,
2014, JDS 97:3156-3172)

GWAS for birth weight PTA
h
Cole et al., 2014 (J. Dairy Sci., 97:3156–3172)

Are we measuring anything new?
l Identified a SNP on BTA16 intronic to
LHX4, which is associated with cow body
weight and length (Ren et al., 2010, Mol.
Bio. Reprod., 37:417-422).
l 4 SNP in the QTL region on BTA 18 had
large effects
l Several other SNP with large effects
intronic or adjacent to genes with
unknown functions

KEGG pathways for birth weight
What does
regulation of
the actin
cytoskeleton
have to do with
birth weight in
cattle?
That is, do
these results
make sense?
Maybe…these
pathways may
be involved in
establishment
& maintenance
of pregnancy,
as well as
coordination of
growth and
development.
Cole et al. (2014)

Pedigree & haplotype design
Arlinda Chief
AA, SCE: 8
Chief
AA, SCE: 7
MGS
Arlinda Rotate
AA, SCE: 8
δ = 10 Tradition
Melwood
Aa, SCE: 8
CMV Mica
Aa, SCE: 14
Jed
Aa, SCE: 15
Leduc
Aa, SCE: 18
Aa, SCE: 10
MGS
These bulls carry
the haplotype with
the largest, negative
effect on SCE:
Rockman Ivanhoe
Aa, SCE: 6
Delegate
Aa, SCE: 15
Laramie
aa, SCE: 15
Couldn’t obtain DNA:
Combination
??, SCE: 7

How many scientists does it take…
You just missed his talk
(Abstract 164, Bickhart
et al.)!
You went to her
poster on Tuesday
(Abstract 799,
Cooper et al.), right?
He’s back in
Maryland,
working.

Sequencing coverage
Bull name SCE1 Genotype2 Total reads Coverage
Pawnee Farm Arlinda Chief 7 AA 333,628,731 12.03
Glendell Arlinda Chief 8 AA 981,726,824 35.41
Sweet Haven Tradition 10 Aa 390,387,538 14.01
Arlinda Rotate 8 AA ~476,000,000 17.00
Arlinda Melwood 8 Aa ~448,000,000 16.00
Juniper Rotate Jed 15 Aa 656,190,604 23.66
CMV Mica 14 Aa 433,353,161 15.63
Lystel Leduc 18 Aa 767,440,677 27.68
Willow-Farm Rockman Ivanhoe 6 Aa 195,769,690 7.06
Cass-River Select Delegate 15 Aa 377,380,110 13.61
Wedgwood Laramie 15 aa 371,477,172 13.39
1Predicted transmitting ability (PTA) for sire calving ease, the percentage of offspring born with difficulty. Small
values are desirable and large values are undesirable.
2The genotype of the tag SNP for the QTL, where “A” and “a” are the major and minor alleles, respectively.

Results from Illumina sequencing
l Data analyzed using paired-end read
alignments and split-read mapping
l Portions of two exons and a connecting
intron within the Ig-like protein domains
may have been duplicated
l Some heterozygotes with desirable SCE
also have deletions near the N-terminal
end of the protein

Possible assembly problem on BTA18
This could be a GC-rich region (bias in
Illumina chemistry).
More reads than expected may align
here because repetitive elements were
combined during assembly.

Genome assembly (simplified)
Reads must be assembled into chromosomes
Assembly is a computational process (Liu et al., 2009; Zimin et al., 2009)
This process is imperfect – repetitive regions are hard to assemble correctly!
Sometimes, this…
should be this.

Can it be corrected using long reads?
l BTA18 genomic DNA extracted
from CHORI-240 BAC library
(L1 Domino 99375) at AGIL
Source: Pacific Biosystems
l Sequencing libraries constructed at USDA
MARC, pooled, and run on PacBio RS II
BAC ID Insert size (bp) Start End
CH240-389P14 174,682 56,954,654 57,129,335
CH240-234E12 178,618 57,058,248 57,236,865
CH240-280L6 175,831 57,092,237 57,268,067
CH240-34N7 158,841 57,129,383 57,288,223

Processing of PacBio reads
l BAC DNA was pooled at MARC to have
enough material to construct a
sequencing library
l Reads were assembled into contigs using
HGAP in SMRTanalysis v2.2.0
l 44 contigs with an N50 of 31 kb were
constructed

Analysis of alignments
l PacBio contigs aligned against UMD3.1
contigs using MUMmer 3.0
l Short (Illumina) reads aligned against
PacBio contigs using BWA 0.7.5a-r405
l Paired-end discordancy interrogated
using custom scripts (Bickhart,
unpublished data)

Alignment of BAC contigs with UMD3.1
A line with a slope of 1 indicates that a segment
is conserved between the two sequences – this
contig is almost identical between our PacBio
assembly and the UMD3.1 reference assembly.

Discordancy analysis
l Illumina reads aligned w/PacBio contigs
l Reads with lengths ±4σ were counted
l Discordancies may indicate
w Problems in the PacBio assembly
w The presence of repetitive elements
w Structural differences between the
Holstein and Hereford (unlikely)

DNA in PacBio and not in UMD3.1
20000
18000
16000
14000
12000
10000
8000
6000
4000
2000
0
Reads map to PacBio and UMD3.1 contigs.
~10 kbp of DNA in PacBio contig that doesn’t map to
UMD3.1!
Reads map to PacBio and UMD3.1—
ARS-BFGL-NGS-109285 is placed here.
0 50000 100000 150000 200000 250000 300000
scf7180000000136|quiver
REF

There are clearly assembly problems
25000
20000
15000
PacBio sequence duplicated
10000
5000
0
PacBio sequence duplicated
on UMD3.1 contig
on UMD3.1 contig
0 20000 40000 60000 80000 100000 120000
scf7180000000103|quiver
REF

What have we learned?
l This is more complex than SNP
genotyping, and unsuccessful
experiments are expected
l You needs lots of high-quality DNA for
constructing PacBio libraries
l Overlapping BACs should not be pooled
(some people already know this)
l Data editing and error-correction are
critical

Next steps
l Re-assemble raw reads following more
stringent edits and data cleaning
l Re-sequence single BACs or pooled, non-overlapping
BACs
l Sequence the RPCI-42 Holstein BACs
(Monsanto calf)
w Are structural differences between
Holstein and Angus in this region

Conclusions
l Structural variants in and around the
Siglec-12 gene are associated with
differences in SCE
l SNP are misplaced on the UMD3.1
assembly
l A region ~8 kb downstream of ARS-BFGL-NGS-
109285 appears to be misassembled
l The causal variant on BTA18 has not yet
been conclusively identified

Acknowledgments
l USDA-ARS appropriated project 1245-31000-
101-00
l CNPq “Ciência sem Fronteiras” program
l Cooperative Dairy DNA Repository and Council
on Dairy Cattle Breeding

Animal Improvement Program team

Questions?

Using genotyping and whole-genome sequencing to identify causal variants associated with complex phenotypes

More Related Content

What's hot (20)

Similar to Using genotyping and whole-genome sequencing to identify causal variants associated with complex phenotypes (20)

More from John B. Cole, Ph.D. (13)

Recently uploaded (20)

Using genotyping and whole-genome sequencing to identify causal variants associated with complex phenotypes

Editor's Notes