This document summarizes the findings of the Human Genome Structural Variation Consortium (HGSV) which used multiple technologies including Illumina, PacBio, BioNano Genomics, 10X Genomics, and Strand-Seq to comprehensively discover structural variations in selected individual genomes. Key findings include the identification of on average 818,181 indels, 31,599 structural variations over 50bp, and 121 inversions per person. Combining different technologies allowed for a 7-fold increase in detected structural variations per person compared to previous studies. No single technology was found to be comprehensive.

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Human Structural Genomic Variation
Charles Lee, Ph.D., FACMG
SCIENTIFIC DIRECTOR AND PROFESSOR
The Jackson Laboratory for Genomic Medicine
January 25, 2018
Conclusion of the 1000G Project: Published September 30, 2015

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Steven McCarroll
Deanna Church
Jonas Korlach
Scott Devine
Ali Bashir Ryan Mills
Ken Chen
Kai Ye
Mark Gerstein
Gabor Marth
Jonathan Sebat
Michael Talkowski
Goo Jun
Li Ding
Bing Ren
Paul Flicek Pui-Yan Kwok
Peter Lansdorp
Xinghua ShiTobias Marschall
Jan Korbel
Evan Eichler
Chong Lek Koh
Charles Lee
HGSV: Human Genome Structural Variation Consortium
PRINCIPAL INVESTIGATORS:
HGSV: Strategy
Illumina Hiseq
Jumping Libraries
Pac Bio
Chrom (10X)
Illumina (Moleculo)
Hi-C
Strand Seq
BioNano
Affy Cytoscan
Short reads
High error rate
Low base calling accuracy
Not true long read
Cost
High library cost
Long run time
Not commercially available
WGS remains a challenge
Low sequence quality
GC bias
CONS
Minimal sample
FastCapable of long range phasing
True long reads
DNA-protein interactions
Easy sample prep
High quality de novo assembly
DNA methylation analysis
High quality
No assembly required
High consensus
Uniform coverage
PROS

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HGSV: Selected Trios
GOAL: to utilize multiple platforms for comprehensive discovery
of structural variations in selected individual genomes
NA19240 HG00514 HG00733
NA19239 NA19238
Yoruban Han Chinese Puerto Rican
HG00512 HG00513 HG00731 HG00732
(high genetic diversity) (low genetic diversity) (population admixture)
Parents were previously sequenced in 1000G
Children sequenced in HGSV to 223-fold combined coverage
Illumina HiSeq
Short read sequencing, deep coverage PCR-free (bridge amplification)
Sequences reads (approximately 150bp) from both ends of a DNA fragment = PAIRED END
Three approaches for SV detection (using paired-end sequencing data):
READ DEPTH
PAIRED END MAPPING
SPLIT MAPPING
* Short Read Sequencing

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PacBio SMRT Sequencing
Single Molecule Real Time (SMRT) long insert sequencing, no amplification required
Sequences long and complex regions
* Long Read Sequencing
True long read sequencing, average read size >10,000 bases
Bio Nano Video
Bio Nano Genomics Irys * Optical Mapping
Analyzes single, linearized DNA molecule (no amplification required)
Optical mapping = complimentary way to interrogate genome for SVs
Whole genome maps enable detection of translocations, repeats and deletions
through de novo assembly

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Illumina Moleculo
Generate long reads from existing tagged short reads, synthetic length is ~100kb
Genome is sheared to 10kb fragments, which are tagged with unique barcode
* Synthetic Long Reads
Long reads allow for de novo assembly
BARCODE: A series of known bases added to template molecule (through ligation or amplification).
Chromium (10X Genomics)
Long range genomic cell-by-cell gene expression (low DNA input: 1ng)
* Synthetic Long Reads
Long range information enables phasing, SV calling and copy number determination
10X Video
Standard short reads are attached with molecular barcodes, synthetic length is ~100kb

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Strand-Seq
1 2 3 96
BrdU incorporated
into new strands
MitosisReplication
Cell Daughter Cells Isolate and deposit single daughter
cell into single well
MNase
digestion
Fragment DNA
to 150bp
Ligate adaptors
Hoechst-UV
Treatment
BrdU+ strands nicked
Remove BrdU+ strands
PCR
Amplification
Only template strands
are amplified
Introduce multiplex barcode
during PCR amplification
Sequence and
Ideogram
B B B
Template
B B B
Template
B B B
Template
B B B
Template
A2 A1
Template
A2’ A1’
A1’ A2’
Template
A1 A2
Create paired-end
sequencing libraries
Create Ideogram
Falconer et al. Nature Methods 9: 1107-12, 2012; Sanders et al. Genome Res 26: 1575-1587, 2016
Haplotype A
Haplotype B
10kb reads
Partitioning PacBio Reads to Haplotypes
67% of reads could be haplotype partitioned

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Integrative Phasing
Illumina
Illumina-SLR
PacBio
Chro
HiC
Strand-Seq
WhatsHap
Dense, global (chromosomal scale) haplotypes
DENSE + LOCAL SPARSE + GLOBAL
Fraction of Phase Connection (power to call and resolve haplotypes)
heterozygous heterozygous heterozygous
Illumina
PacBio
Chromium (10X)
Strand-Seq
F.P.C.%
68%
95%
92%
65%
C G A
C G A
C AG
G
G A
Chromium (10X)
+ Strand-Seq C G A
Lowest mismatch error rate (0.23%). 96.5% of all heterozygous SNVs were phased.

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Discovering Indels (1-49bp)
Illumina
Short reads
818,181
Indels/child
PacBio
Long reads
Increase:
119,274
Phased-SV
698,907
Indels/child
GATK
FreeBays
Pindel
Discovering SVs (>50bp): Unified Callsets
Illumina
Short reads
Increase:
20,963SVs
BNGPacBio
Long reads Optical Mapping
12,680 Deletions
18,919 Insertions
31,599 SVs (avg/individual)
Various algorithms
(Assemble/Map , Call Variants)
Haplotype-resolved Phased SV
6808 Deletions
3035 Insertions
793 Duplications
10,636 SVs (avg/individual)
11 Algorithms
(Assemble/Map , Call Variants)

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NA19240
Unified Callset for SV (DELETIONS)
7176
90
202
1186
5109
60
721
PacBio
(n=13066)
Illumina
(n=7218)
BioNano
(n=1073)
NA19240
Unified Callset for SV (INSERTIONS)
13443
435
121
1365
2939
1073
87
PacBio
(n=17542)
Illumina
(n=4512)
BioNano
(n=1716)

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PacBio Assemblies have Reduced Ability to Detect CNVs
~341 genes/child had a CNV that was NOT detected by PacBio assembly
Therefore it’s important to continue read-depth based CNV detection
24.9%of all SD’s detected by PacBio
All Known
Segmental Duplications
All CNVs detected
by dCGH
All CNVs detected
by GenomeSTRiP
6.2%of all CNVs detected by PacBio
27%of all CNVs detected by PacBio
Discovering Inversions
BNGIllumina PacBio
Short reads, WGS Long read
Various algorithms
(Assemble/Map , Call Variants)
Optical Mapping
Illumina
Jumping library
(liWGS)
Strand Seq
126 91 118 28 170
306 Inversions
(across all 9 individuals)
121 Simple Inversions
(per person)

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Inversion Calls in Trios, Based on Technologies
81 heterozygous
38 homozygous
Smallest: 263 bp (ch1:187497343-187497605)
Largest: 1.28Mb (chr8: 8230468-12095842)
121 Simple Inversions
(per person)
Technology
Illumina
Jumping
PacBio
Strand-seq
Bionano
Refining Meiotic Breakpoints
Strand-seq
~25 kbp
Meiotic breakpoint
median resolution:
~1.5 kbp
PacBio + Strand-seq
(trio-aware phasing)
Maternal meiotic recombination events
Alu retrotransposons (p=3.07E-7)
THE1-A,B (p=0.08)
15-mer motif

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Discovering Full-length L1s
On average ~190 FL-L1 (with 2 ORFs) per child
124 of these FL-L1s were found in all 3 children
- Some are known active (hot) L1 elements associated with disease such as cancer.
- 4-6 unique active (hot) L1s found in each genome.
Cumulative differences in L1 mutagenesis suggest that
such diversity may translate into differential risk levels
for cancers and other disorders.
Take Home Message
Combining different technologies/algorithms, we have identified
7x more structural variation per person than was previously appreciated
No single technology / algorithm will be comprehensive
Application to disease association studies and clinical interpretation
of genomes
• Average of 818,181 indels (<50 bp) per person
• Average of 31,599 SVs (>50 bp) per person
• Average of 121 inversions per person (simple + complex)
• Active LINE-1 elements

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Discovery of Haplotype-resolved SVs - bioRxiv