Bioinformatics work notes

Preimplantation genetic testing with PGT-A and niPGT-A

Posted April 18, 2021April 18, 2021Posted in genomics, IVF, laboratory, screening

One of the main causes of infertility – or just problems getting pregnant for longer than expected – are wrongly assembled chromosomes in the developing embryos. In particular in women towards the age of 40 or older, a high percentage of the oocytes available in the body tend have a wrong number of chromosomes (aneuploidies) or other alterations that will stop the embryo from developing into a healthy baby. In-vitro fertilization (IVF) can help patients through a pregnancy in many cases, but if one of these embryos is chosen for re-implantation after fertilisation, the treatment will most likely fail.

“Preimplantation Genetic Screening” (PGS), now also called “Preimplantation Genetic Testing for Aneuploidies” (PGT-A), is a test often used as part of IVF cycles. PGT-A can assess the genetic health of the embryo by looking at the the number of chromosomes and parts of the chromosomes. Unfortunately there can be many additional other underlying problems, but choosing an embryo with the genetic makeup comatible with life improves the chances of achieving a successful pregnancy – reducing costs and anxiety for the patients.

The current standard practise for performing PGT-A is through shallow short-read sequencing of the genomic DNA of a few cells extracted from the developing embryo (blastocyst), and analysed e.g. using read-counting as described in this blog post.

Alternative approaches currently being developed are using the DNA from the media the embryo is growing in as part of the IVF treatment (spent culture media or SCM). This DNA originates from the embryo, but is not compacted within cells anymore (cell-free DNA or cfDNA). As this does not involve the embryonic cells themselves this is termed Non-Invasive Preimplantation Genetic Testing (niPGT). The term is also used for methods that extract the cfDNA from the blastocoele fluid (BF).

As it should be preferable to disturb the embryonic development as little as possible, non-invasive method currently receive a lot of interest and will probably be the new standard at some point. In addition, these methods for DNA extraction are cheaper and technically easier to perform.

At the moment the results achieved are less reliable however and should only be used as part of research projects. Potentially fragmentation and degradation of DNA is influencing results; another hypothesis is that preferably unhealthy cells are released by the developing embryo and thus are not representative of the genetic health of the actual embryo.

To address the variability of results, there are currently at least four clinical trials underway conducted by Igenomix, USA (here and here), ART Fertility Clinics, United Arab Emirates and the Mỹ Đức Hospital, Vitenam.

Big Data with SQLite

Posted June 3, 2020Posted in bioinformatics, database, genome informatics

I recently wanted to process genome-wide sequence data that I had extracted into 24 files ranging in size from 3 GB (Gigabytes) for the small chromosome Y to 26 GB for the largest chromosome 1. Further processing this data was challenging as any software script reading the files has to do it in a stream, i.e. line by line or in chunks – or use immense amounts of memory. The actual task was to extract the set of sequences from all chromosome files that are unique across the genome. Command-line tools to sort / make unique / combine would be running for many days if they would finish at all.

The solution was to make use of a database management system (DBMS). There are more powerful DBMS, but the free and simple-to-use file-based database system SQLite can handle a lot of data as well. Theoretically its file can grow up to 140 terabytes and more (ref). I had to optimize the setup in the following ways:

1. Install the latest SQLite version, compiling it as described here. For the Mac OS X it included the following:

conda install -c conda-forge fossil
fossil clone https://www.sqlite.org/src sqlite.fossil
fossil open sqlite.fossil
fossil update release
mkdir bld
cd bld
../configure
make
make sqlite3.c
make test

2. Add the “virtual tables” extension, compiling csv.c from https://github.com/sqlite/sqlite/tree/master/ext/misc

gcc -g -I. -fPIC -dynamiclib /home/felix/sqlite_extensions/csv.c -o /home/felix/sqlite_extensions/csv.dylib

3. When setting up the database, use PRAGMA settings to improve the performance of the DBMS and then load the extension:

sqlite3 sequences.db
>PRAGMA main.page_size = 4096;
>PRAGMA main.cache_size = 10000;
>PRAGMA main.locking_mode = EXCLUSIVE;
>SELECT load_extension('/home/felix/sqlite_extensions/csv.dylib');

4. Load the csv data into a virtual / temporary table first – this is extremely fast. Perform any operations on this table while inserting the data into a final table in the database. This could for example involve pulling out only unique sequences:

>CREATE VIRTUAL TABLE temp.t1 USING csv(filename="/home/felix/data/chrom1.csv");
>CREATE TABLE sequences (
    seqid INTEGER PRIMARY KEY,
    seqstring VARCHAR NOT NULL
);
INSERT INTO sequences(seqstring) SELECT DISTINCT c0 from t1;

If you need indices, add them afterwards!
Trying to remove duplicates by loading into a table with a “UNIQUE” column was too slow, btw.

5. The next steps are then to load data from other chromosomes into this table “sequences” and then pull them out into a new table “unique_sequences” to remove duplicates between different chromosomes as well:

>DROP TABLE temp.t1;
>CREATE VIRTUAL TABLE temp.t1 USING csv(filename="/home/felix/data/chrom2.csv");
>CREATE TABLE sequences (
   seqid INTEGER PRIMARY KEY,
   seqstring VARCHAR NOT NULL
);
INSERT INTO sequences(seqstring) SELECT DISTINCT c0 from t1;

# and so on for other chromosomes
# ...

>CREATE TABLE unique_sequences (
    seqid INTEGER PRIMARY KEY,
    seqstring VARCHAR NOT NULL
);
INSERT INTO unique_sequences(seqstring) SELECT DISTINCT seqstring from sequences;

In my case it was better to use a number of intermediate databases to be able to run the processing in parallel and to keep the file size smaller. The data from the different databases can then be combined into the final db with statements like this:

sqlite3 sequences.db
>PRAGMA main.page_size = 4096;
>PRAGMA main.cache_size = 10000;
>PRAGMA main.locking_mode = EXCLUSIVE;

>ATTACH DATABASE 'sequences.1.db' AS db1;
>INSERT INTO unique_sequences(seqstring) SELECT DISTINCT seqstring from db1.unique_sequences;

The final sqlite database was around 150 GB, but the performance was fine to extract sequences as needed.

Telomeric Regions of the Human Genome

Posted April 21, 2020June 12, 2020Posted in genome informatics, genomics

Telomeres form caps on the ends of chromosomes that prevent fusion of chromosomal ends and provide genomic stability.

During gametogenesis, reprogramming of the germ cells leads to elongation of telomeres up to their species-specific maximum.

In normal somatic cells, telomeres are progressively shortened with every cell division. This shortening in normal human cells limits the number of cell divisions. For human cells to proliferate beyond the senescence checkpoint, they need to stabilize telomere length. This is accomplished mainly by reactivation of the telomerase enzyme. Telomerase expression is under the control of many factors. Expression of telomerase can lead to cell immortalization and is activated during tumorigenesis, i.e. cancer.

Male Xq-telomeres are 1100 bp shorter than female Xq-telomeres.

The telomeric repeat found on all human chromosomes is “TTAGGG”.

The centromeres and telomeres of the human chromosomes are not defined as region attributes in the Ensembl perl API explicitely, so for checking these regions, one option is to pull them out of the UCSC table browser. For this, select the assembly and use the “Mapping and Sequencing tracks” group and the “Gap” table. The extracted locations of the human telomere regions is provided below for the genome assemblies GRCh37 (hg19) and GRCh38 (hg38). The coordinates are given in the 0-based UCSC coordinated system.

Telomere locations

Telomeres of chromosome 17 have not been defined for assembly GRCh37. They are short, but do exists nonetheless. An assembly patch will address this.

Chromosome	Start (hg19)	End (hg19)	Start (hg38)	End (hg38)
chr1	0	10000	0	10000
chr1	249240621	249250621	248946422	248956422
chr2	0	10000	0	10000
chr2	243189373	243199373	242183529	242193529
chr3	0	10000	0	10000
chr3	198012430	198022430	198285559	198295559
chr4	0	10000	0	10000
chr4	191144276	191154276	190204555	190214555
chr5	0	10000	0	10000
chr5	180905260	180915260	181528259	181538259
chr6	0	10000	0	10000
chr6	171105067	171115067	170795979	170805979
chr7	0	10000	0	10000
chr7	159128663	159138663	159335973	159345973
chr8	0	10000	0	10000
chr8	146354022	146364022	145128636	145138636
chr9	0	10000	0	10000
chr9	141203431	141213431	138384717	138394717
chr10	0	10000	0	10000
chr10	135524747	135534747	133787422	133797422
chr11	0	10000	0	10000
chr11	134996516	135006516	135076622	135086622
chr12	0	10000	0	10000
chr12	133841895	133851895	133265309	133275309
chr13	0	10000	0	10000
chr13	115159878	115169878	114354328	114364328
chr14	0	10000	0	10000
chr14	107339540	107349540	107033718	107043718
chr15	0	10000	0	10000
chr15	102521392	102531392	101981189	101991189
chr16	0	10000	0	10000
chr16	90344753	90354753	90328345	90338345
chr17	NA	NA	0	10000
chr17	NA	NA	83247441	83257441
chr18	0	10000	0	10000
chr18	78067248	78077248	80363285	80373285
chr19	0	10000	0	10000
chr19	59118983	59128983	58607616	58617616
chr20	0	10000	0	10000
chr20	63015520	63025520	64434167	64444167
chr21	0	10000	0	10000
chr21	48119895	48129895	46699983	46709983
chr22	0	10000	0	10000
chr22	51294566	51304566	50808468	50818468
chrX	0	10000	0	10000
chrX	155260560	155270560	156030895	156040895
chrY	0	10000	0	10000
chrY	59363566	59373566	57217415	57227415

Centromer locations

The centromers for assembly hg38 are defined in different ways in the UCSC system. According to an explanation by Brian Lee and Christopher Lee of UCSC there now is a specific browser track that “shows the location of Karen Miga’s centromere model sequences. However, these annotations can be smaller than the centromeres shown in the chromosome ideogram and Chromosome Bands track. (…) Depending on your purpose you could use the centromere model regions (red), or the broader Chromosome Bands Ideogram definition of centromere which overlap some annotations (cytoBandIdeo table), or the regions of the assembly that are just NNNNN’s (Gap track).” (link, link)
You can see this location in the screenshot below (from the browser):

As the gap extend is slightly larger than the defined regions, we will have to use the definition of “acen” ideogram regions from the gaps table in most cases where we actually need to use the underlying sequence. The combined centromer regions for hg38 are shown below:

chrom	start	end
chr1	121700000	125100000
chr2	91800000	96000000
chr3	87800000	94000000
chr4	48200000	51800000
chr5	46100000	51400000
chr6	58500000	62600000
chr7	58100000	62100000
chr8	43200000	47200000
chr9	42200000	45500000
chr10	38000000	41600000
chr11	51000000	55800000
chr12	33200000	37800000
chr13	16500000	18900000
chr14	16100000	18200000
chr15	17500000	20500000
chr16	35300000	38400000
chr17	22700000	27400000
chr18	15400000	21500000
chr19	24200000	28100000
chr20	25700000	30400000
chr21	10900000	13000000
chr22	13700000	17400000
chrX	58100000	63800000
chrY	10300000	10600000

Sources:

Read-counting for PGT-A

Posted March 28, 2020November 30, 2021Posted in bioinformatics, IVF, sequencing

ICSI, source: https://upload.wikimedia.org/wikipedia/commons/0/09/ICSI.jpg

This is an introductory article about Pre-Implantation Genetic Testing for Aneuploidy (PGT-A, formerly known as PGS) using sequencing-based read-counting. The procedure is simplified and I am avoiding to talk about specific companies or products here.

Increasing the chances of a successful pregnancy by looking at the embryos chromosomes

During an IVF cycle there are – in most cases – multiple embryos available for transfer to the woman hoping to become pregnant. IVF treatment is expensive and a stressful experience for the patients. The goal of the treatment must therefor be to have success after one or only a few cycles. Factors contributing to success or failure of an IVF cycle are various, many of which are still poorly understood. One of the better-understood factors is the genomic make-up of the embryo(s) transferred: If the cells have a non-standard number of chromosomes, the chances of survival are low for the embryo.

In order to select an embryo with a high chance of implantation and pregnancy, Pre-Implantation Genetic Testing for Aneuploidy (PGT-A) can be performed. This procedure can assess the genome of an embryo at a high level by identifying the number of chromosomes (or regions of the chromsomes) found in a few cells sampled. Deeper levels of genetic analysis are of course also possible, but the impact of these findings is usually poorly understood and would lead to a selection process that we should stay clear of!

Counting chromosomes by counting sequencing reads

Determining the copy-numbers of every chromosome can be reliably done with sequencing-based approaches nowadays. One way to achieve this is the read-counting method employed by many software solutions and commercial products. The method can be outlined as followed:

Extract and amplify the DNA of a few embryonic cells
Generate a sequencing library
Perform shallow-depth sequencing
Align the sequencing reads back to the human reference genome
Count how many reads you see in the different regions and chromosomes and
Determine the copy-numbers from this measurement.

This is of course vastly simplified and cannot be discussed at every level of detail here, but we can focus on the last steps a bit further.
The theory we build on is that if the retrieval of the cells, the generation of the sequencing library and the experimental protocol are kept the same, the number of sequencing reads counted in a specific genomic region should only depend on two factors:

A. The characteristics of that genomic region: Some parts of the genome are amplified more rapidly than others and will be overrepresented in the mixture.
B. The amount of input material: If there was an extra copy of the chromosome we should have more DNA going into the sequencing process and also see more reads coming out for this specific region.

Factors underlying point A are mostly based on the characteristics of the amplification reaction mentioned in step 1 above (e.g. which polymerase enzyme is used) and the base-composition of the genome itself. The latter mostly comes into play when we try to place the sequencing reads found back on the reference genome: If the genomic region the read originates from is repeated a few times on different chromosomes, this alignment process cannot reliable determine where to place the read and it has to be discarded. However, as the genome is >99% identical between individual and because the amplification reaction should be performed in a standardised way, this type of bias is relatively constant. Point A above can therefor be analysed beforehand and this amplification and mappability bias can simply be removed. This leaves us with the differences originating from point B – which are the actual differences that we are trying to assess in the embryo.

In a standard human cell there are 2 copies of each of the autosomes (chromosomes 1 to 22), this becomes our „normal“ level. If we look at the actual numbers of reads counted, we can derive the most likely copy-numbers of the region by comparing them to our normal level:

Theoretical numbers of reads	0*	50	100	150	200
Assumed copy-number of the region	0	1	2	3	4

* There will usually be a few reads erroneously found in these regions

Even with very strict protocols there will be variation in read numbers though, making the the assessment more challenging. Additional processing steps can be applied to clean and smooth the data. For this to work it is assumed that most regions are at the “normal” level and that neighbouring measurement point behave similarly in most cases.

Visualising the measured copy-numbers for each region is a key part in the embryo assessment. Autosome read counts outside of the grey shaded area in the copy-number plot above would be considered gained / lost. The sample shown seems to have 3 copies of chromosome 16. Chromosomes X & Y show copy-number 1 as this would be a male sample.

This assessment can now guide the decision which of the embryos might have the highest change of survival bases on its genomic make-up and could be selected for implantation during an IVF cycle.

Microdeletion and Microduplication Syndromes in the Human Genome

Posted February 21, 2020January 21, 2021Posted in genome informatics, genomics, health

Small changes in the sequence of human chromosomes can have detrimental effects in the person’s health and development. The often affect multiple genes but are too small to be visible with traditional karyotyping methods. These changes seem to appear near regions of low complexity (repetitive sequence regions) with some consistency as novel mutations.

The following table shows a list of known syndromes and their genomic locations in hg19 (GRCh37) coordinates. It was compiled by A.Weise et al. in “Microdeletion and Microduplication Syndromes”, Journal of Histochemistry and Cytochemistry (2012).

Microdeletion Syndrome	Microduplication Syndrome	OMIM	Cytoband	Chromosome	Start Position (kb)	End Position (kb)	Length (bp)
microdeletion 1p36	–	607872	1pter-p36.31	1	0	5309	5309
microdeletion 1p36 (GABRD)	microduplication 1p36 (GABRD)	613060	1pter-p36.3	1	0	10000	10000
–	microduplication 1p34.1	–	1p34.1	1	45591	46808	1217
microdeletion 1p32.2	–	613735	1p32.2	1	55500	60900	5400
microdeletion 1p21.3	–	–	1p21.3	1	97320	99250	1930
microdeletion 1q21.1	microduplication 1q21.1	612475	1q21.1	1	144980	146343	1363
thrombocytopenia-absent radius syndrome/TAR	–	274000	1q21.1	1	144150	144427	277
deletion 1q21.1 (GJA5)	duplication 1q21.1 (GJA5)	121013	1q21.1	1	145040	145860	820
microdeletion 1q24q25	–	–	1q24.3q25.1	1	170135	172099	1964
microdeletion 1q24.3	–	–	1q24.3	1	170000	170600	600
Van der Waude syndrome/VWS1	–	119300	1q32.2-q41	1	207709	208277	568
microdeletion 1q41–42	–	612530	1q41-q42	1	221135	221775	640
corpus callosum agenesis microdeletion	–	612337	1q44	1	242576	242936	360
–	microduplication 2p25.3	–	2p25.3	2	3250	3450	200
Feingold syndrome/FS	–	164280	2p24.3	2	15999	16005	6
hypotonia-cystinuria syndrome/HCS	–	606407	2p21	2	44384	44442	58
holoprosencephaly 2/HPE2	–	157170	2p21	2	45022	45026	4
–	microduplication 2p21	–	2p21	2	45200	45900	700
NRXN1 microdeletion	NRXN1 microduplication	600565	2p16.3	2	50011	50437	426
microdeletion 2p15–16.1	–	612513	2p15–16.1	2	57537	61534	3997
microdeletion 2p14-p15	–	612513	2p14–15	2	63756	65377	1621
microdeletion 2p11.2-p12	–	613564	2p11.2-p12	2	77597	87091	9494
microdeletion 2q11.2 (LMAN2L, ARID5A)	–	–	2q11.2	2	96090	97040	950
mesomelic dysplasia/MMD	–	605274	2q11.2	2	99530	100125	595
microdeletion 2q11.2q13 (NCK2, FHL2)	microduplication 2q11.2q13 (NCK2, FHL2)	602633/604930	2q11.2q13	2	100060	107810	7750
nephronophthisis 1/NPHP1	microduplication 2q11.2q13	256100	2q13	2	110293	110320	27
microdeletion 2q13	microduplication 2q13	–	2q13	2	111050	112950	1900
autism-dyslexia microdeletion 2q14.3	microduplication 2q14.3 (own case)	–	2q14.3	2	124500	125500	1000
Mowat–Wilson syndrome/MWS	–	235730	2q22.3	2	144900	144994	94
microdeletion 2q23.1	–	156200	2q23.1	2	148964	149150	186
microdeletion 2q23.3q24.1	–	156200	2q23.3-q24.1	2	153150	156930	3780
microdeletion 2q24.3	neonatal epilepsy microduplication	607208/604403	2q24.2-q24.3	2	165133	166562	1429
synpolydactyly 1/SPD1	microduplication 2q31.1	613681	2q31.1	2	176659	177679	1020
microdeletion 2q31.2-q32.3	–	612345	2q31.2-q32.2	2	177640	191380	13740
microdeletion 2q33.1	–	612313	2q33.1	2	196538	204915	8377
brachydactyly-mental retardation syndrome/BDMR	–	600430	2q37	2	239620	242951	3331
distal 3p deletion	–	613792	3p25-p26	3	0	6995	6995
Von Hippel Lindau disease/VHL	–	193300	3p25-p26	3	10158	10169	11
microdeletion 3p21.31	–	–	3p21.31	3	49120	52220	3100
microdeletion 3p14.1p13	–	605515	3p14.1-p13	3	71164	71959	795
microdeletion 3p11.1p12.1	–	–	3p11.2-p12.1	3	87069	87408	339
proximal 3q microdeletion syndrome	–	–	3q13.11-q13.12	3	106400	108900	2500
microdeletion 3q13.31	–	–	3q13.31	3	115335	115916	581
blepharophimosis, ptosis, and epicanthus inversus syndrome/BPES	–	110100	3q23	3	140146	140148	2
Dandy–Walker syndrome/DWS	–	220200	3q24	3	148610	148617	7
microdeletion 3q27.3q29	–	–	3q27.3-q29	3	188870	198080	9210
microdeletion 3q29	microduplication 3q29	609425/611936	3q29	3	197126	198982	1856
Wolf–Hirschhorn syndrome/WHS	microduplication 4p16.3	194190	4pter-p16.3	4	0	2043	2043
–	microduplication 4p16.1	–	4p16.1	4	9450	10450	1000
microdeletion 4p15.3	–	–	4p15.3	4	16583	20747	4164
microdeletion 4q21.21q21.22	–	613509	4q21.21q21.22	4	81950	83350	1400
microdeletion 4q21	–	613509	4q21	4	82228	83601	1373
microdeletion 4q21.2q21.3	–	–	4q21.2-q21.3	4	89148	89218	70
–	Parkinson disease/PARK1	163890/168601	4q22.1	4	90747	91018	271
Rieger type 1/RIEG1	–	180500	4q25	4	111758	111779	21
–	4q32.1-q32.2 Triple/Duplication syndrome	613603	4q32.1q32.2	4	157356	161615	4259
Cri–du-Chat syndrome/CdCS	–	123450	5p15.2-p15.33	5	0	11777	11777
Cornelia de Lange syndrome/CDLS	NIPBL microduplication	613174	5p13.2	5	36997	37033	36
spinal muscular atrophy/SMA	–	253300	5q13.2	5	70278	70286	8
microdeletion 5q14.3	–	600662	5q14.3	5	86142	86413	271
microdeletion 5q14.3-q15	–	612881	5q14.3-q15	5	88400	90090	1690
familial adenomatous polyposis/FAP	–	175100	5q22.2	5	112129	112249	120
–	adult-onset autosomal dominant leukodystrophy/ADLD	169500	5q23.2	5	126046	126233	187
PITX1 microdeletion	–	602149	5q31.1	5	134222	134463	241
microdeletion 5q31.3	–	–	5q31.3	5	139117	141682	2565
–	Pseudo trisomy 13 syndrome	264480	5q35.1	5	170222	171584	1362
microdeletion 5q35.1	–	–	5q35.1	5	172592	172595	3
parietal foramina/PFM	–	168500	5q35.2	5	174084	174091	7
Sotos syndrome	microduplication 5q35	117550	5q35.2-q35.3	5	175063	177389	2326
microdeletion 6p	–	612582	6p25	6	0	–
microdeletion 6p22.3	–	–	6p22.3	6	20850	21250	400
adrenal hyperplasia/AH	–	201910	6p21.32	6	32114	32117	3
microdeletion 6p21.31	–	–	6p21.31	6	33273	34086	813
microdeletion 6q13–14	–	613544	6q13–14	6	72650	76310	3660
Prader–Willi like	–	176270	6q16.2	6	100943	101018	75
–	transient neonatal diabetes mellitus 1/TNDM1	601410	6q24.2	6	144303	144427	124
microdeletion 6q25.2-q25.3	–	612863	6q25.2-q25.3	6	155500	158853	3353
PARK2 microdeletion	PARK2 microduplication	602544	6q26	6	161688	162784	1096
microdeletion 6q27 anosmia	Chondroma/CHDM	215400	6q27	6	165554	170762	5208
Saethre–Chotzen syndrome/SCS	–	101400	7p21.1	7	19121	–
Greig cephalopolysyndactyly/GCPS	–	175700	7p14.1	7	41967	42243	276
Williams–Beuren syndrome/WBS	microduplication 7q11.23	609757/194050	7q11.23	7	71971	74255	2284
WBS-distal deletion (RHBDD2, HIP1)	–	613729	7q11.23	7	74800	76500	1700
split hand/foot malformation 1/SHFM1	–	183600/220600	7q21.3	7	95370	96619	1249
microdeletion 7q22.1-q22.3	–	–	7q22.1-q22.3	7	101040	104560	3520
autism/dyslexia microdeletion 7q31.1	–	–	7q31.1	7	110654	111266	612
speech-language-disorder 1/SPCH1	–	602081	7q31	7	114085	114090	5
holoprosencephaly 3/HPE3	–	142945	7q36.3	7	155288	155298	10
–	triphalangeal thumb polysyndactyly syndrome/TPTS	174500	7q36.3	7	155836	156425	589
Currarino syndrome/CS	–	176450	7q36.3	7	156490	156496	6
microdeletion 8p23.1	microduplication 8p23.1	179613	8p23.1	8	8156	11803	3647
microdeletion 8p21.2	–	–	8p21.2	8	20750	24390	3640
microdeletion 8p12p21	–	–	8p12p21	8	24500	31300	6800
–	microduplication 8q11.23	610928	8q11.23	8	53450	54050	600
CHARGE syndrome	microduplication 8q12	214800	8q12.2	8	61754	61942	188
microdeletion 8q12.3q13.2	–	–	8q12.3-q13.2	8	65450	69020	3570
mesomelia-synostoses syndrome/MSS	–	600383	8q13	8	70541	70908	367
microdeletion 8q21.11	–	614230	8q21.11	8	77389	77929	540
nablus mask-like facial syndrome/NMLFS	–	608156	8q21.3-q22.1	8	93210	97940	4730
microdeletion 8q22.2q22.3	–	–	8q22.2-q22.3	8	100690	104560	3870
Langer–Giedion syndrome/LGS	–	150230	8q24.11	8	118881	119193	312
sex reversal syndrome 4/SRXY4	–	154230	9p24.3	9	0	1048	1048
monosomy 9p syndrome	–	158170	9pter-p22.3	9	0	16168	16168
–	microduplication 9q21.11	613558	9q21.11	9	71051	71197	146
microdeletion 9q22.3	PTCH1 microduplication	601309	9q22.3	9	94420	99100	4680
holoprosencephaly 7/HPE7	–	610828	9q22.32	9	97284	97319	35
nail-patella syndrome/NPS	–	161200	9q33.3	9	128417	128499	82
early infantile epileptic encephalopathy 4/EIEE4	–	612164	9q34.11	9	129414	129495	81
microdeletion 9q34 (EHMT1)	microduplication 9q34 (EHMT1)	607001	9q34.3	9	136950	140200	3250
subtelomere deletion 9q	–	610253	9q34.3	9	139473	140273	800
hypoparathyroidism, sensorineural deafness, and renal disease/HDRS	–	146255	10p15	10	8137	8157	20
Di George syndrome 2/DGS2	–	601362	10p12.31	10	21144	21170	26
microdeletion 10q22-q23 (NRG3, GRID1)	–	–	10q22-q23	10	81655	88984	7329
juvenile polyposis syndrome/JPS	–	612242	10q23.2-q23.3	10	88675	89613	938
–	Split-Hand/Foot Malformation 3/SHFM3	246560	10q24.32	10	102977	103445	468
microdeletion 10q25q26	–	609625	10q25q26	10	117098	qter	18319
Beckwith–Wiedemann syndrome/BWS—Silver Russell syndrome/SRS microdeletion	Beckwith–Wiedemann syndrome/BWS—Silver Russell syndrome/SRS microduplication	130650	11p15.5	11	2861	2864	3
WAGR syndrome	microduplication 11p13	194072/612469	11p13	11	31767	32467	700
Potocki–Shaffer syndrome/PSS	–	601224	11p11.2	11	43905	46080	2175
–	spinocerebellar ataxia type 20/SCA20	608687	11q12.2q12.3	11	61210	61503	293
microdeletion 11q14.1	–	–	11q14.1-q14.2	11	86334	86344	10
Jacobsen syndrome/JBS	–	147791/188025	11q23.3-qter	11	115400	134452	19052
–	microduplication 12p13.31	–	12p13.31	12	8050	8250	200
microdeletion 12q14	–	–	12q14	12	63356	66932	3576
nasal speech-hypothyroidism microdeletion/NSH	–	–	12q15-q21.1	12	68802	701392	632590
Noonan syndrome 1/NS1	–	163950	12q24.1	12	111341	111432	91
microdeletion 13q12 (CRYL1)	microduplication 13q12 (CRYL1)	–	13q12.11	13	19710	19910	200
spastic ataxia Charlevoix–Saguenay/SACS	–	270550	13q12.12	13	22336	23807	1471
microdeletion 13q12.3-q13.1	–	600185	13q12.3-q13.1	13	31137	31871	734
retinoblastoma/RB1	–	613884	13q14.2	13	47776	47954	178
Hirschsprung disease 2/HSCR2	–	600155	13q22	13	77369	77391	22
holoprosencephaly5/HPE5	–	609637	13q32.3	13	99432	99437	5
microdeletion 14q11.2	–	613457	14q11.2	14	20920	20947	27
congenital Rett variant/CRV	microduplication 14q12	613454	14q12	14	28300	30000	1700
microdeletion 14q22-q23	–	607932	14q22-q23	14	53486	60261	6775
autism spherocytosis microdeletion/ASC	–	–	14q23.2-q23.3	14	63924	64471	547
microdeletion 14q32.2	–	–	14q32.2	14	99463	100574	1111
microdeletion 15q11.2 (NIPA1)	microduplication 15q11.2 (NIPA1)	608145	15q11.2	15	20350	20640	290
Angelman syndrome Typ1/AS1	microduplication 15	105830	15q11.2-q13.1	15	20405	26231	5826
Angelman syndrome Typ2/AS2	microduplication 15	105830	15q11.2-q13.1	15	21309	26231	4922
Prader–Willi syndrome Typ 1/ PWS1	microduplication 15	176270	15q11.2-q13.1	15	20405	26231	5826
Prader–Willi syndrome Typ 2/ PWS2	microduplication 15	176270	15q11.2-q13.1	15	21309	26231	4922
microdeletion 15q13.3 (CHRNA7)	microduplication 15q13.3 (CHRNA7)	612001	15q13.3	15	28525	30489	1964
microdeletion 15q14	–	–	15q14	15	33471	35072	1601
deafness and male infertility syndrome/DMIS	–	611102	15q15.3	15	41613	41747	134
microdeletion 15q21	–	–	15q21	15	48382	48565	183
microdeletion 15q24 (BBS4,NPTN, NE01)	–	601907	15q24	15	70700	72200	1500
microdeletion 15q24	microduplication 15q24	613406	15q24	15	72158	73949	1791
orofacial clefting/OC	–	614294	15q24.3-q25.2	15	76080	80338	4258
microdeletion 15q25	–	614294	15q25	15	82900	83600	700
microdeletion 15q26.1	–	–	15q26.1	15	91100	91600	500
Fryns syndrome/FNS	–	229850	15q26.2	15	92238	96520	4282
microdeletion 15q26.2-qter	–	–	15q26.2-qter	15	95600	100339	4739
ATR-16-syndrome	–	141750	16p13.3	16	0	774	774
tuberous sclerosis microdeletion syndrome/PKDTS	tuberous sclerosis microduplication	600273	16p13.3	16	2038	2079	41
Rubinstein–Taybi syndrome 1/RSTS1	Rubinstein–Taybi-microduplication	610543/613458	16p13.3	16	3762	3801	39
microdeletion 16p13.1 (MYH11)	microduplication 16p13.1 (MYH11)	132900	16p13.1	16	14789	16281	1492
microdeletion 16p11.2-p12.2	microduplication 16p11.2-p12.2	613604	16p11.2-p12.2	16	21521	28950	7429
microdeletion 16p12.1 (EEF2K,CDR2)	microduplication 16p12.2 (EEF2K,CDR2)	117340/606968	16p12.1	16	21850	22370	520
16q11.2 distal microdeletion (SH2B1)	16q11.2 distal microduplication (SH2B1)	–	16q11.2	16	28680	29020	340
microdeletion 16p11.2 (TBX6)	microduplication 16p11.2 (TBX6)	602427/611913	16p11.2	16	29551	30059	508
microdeletion 16q11.2-q12.1	–	–	16q11.2-q12.1	16	45401	45579	178
microdeletion 16q21-q22	–	–	16q21-q22	16	65621	65692	71
microdeletion 16q12.1-q12.2	–	–	16q12.1-q12.2	16	48018	52726	4708
microdeletion 16q24.1	–	601089	16q24.1	16	82908	85153	2245
FANCA deletion	–	227650	16q24.3	16	88392	88411	19
Miller–Dieker syndrome/MDLS	Miller–Dieker microduplication	247200/613215	17p13.3	17	0	2492	2492
microdeletion 17p13.3 (YWHAE)	microduplication 17p13.3 (YWHAE)	247200/613215	17p13.3	17	2–310	2870	2870
microdeletion 17p13.1	–	613776	17p13.1	17	7429	7937	508
hereditary liability to pressure palsies/HNPP	Charcot–Marie–Tooth 1A/CMT1A	162500/118220	17p12	17	13855	15375	1520
Smith–Magenis syndrome/SMS	Potocki–Lupski syndrome/PTLS	610883	17p11.2	17	16527	20423	3896
neurofibromatosis 1/NF1	microduplication NF1	613675	17q11	17	26102	27243	1141
microdeletion 17q11.2-q12	–	–	17q11.2-q12	17	26280	31030	4750
microdeletion 17q12a	–	–	17q12	17	31977	33150	1173
renal cysts and diabetes syndrome/RCAD	microduplication 17q12b	137920	17q12	17	31830	33350	1520
Van Buchem disease/VBCH	–	239100	17q12-q21	17	39187	39192	5
microdeletion 17q21.3 (MAPT)	microduplication 17q21.31 (MAPT)	610443/613533	17q21.3	17	40988	41566	578
microdeletion 17q21.31-q21.32	–	–	17q21.31-q21.32	17	41769	43113	1344
microdeletion 17q22-q23.2	–	–	17q22–23.2	17	48300	54200	5900
–	microduplication 17q23.1–23.2	613355/613618	17q23.1–23.2	17	55457	57693	2236
microdeletion 17q24.2-q24.3	–	–	17q24.2-q24.3	17	61730	65690	3960
carney complex syndrome 1/CNC1	–	160980	17q24.2-q24.3	17	63260	65594	2334
–	microduplication 17q24.3	278850	17q24.3	17	65642	66847	1205
holoprosencephaly 4/HPE4	–	146390	18p11.31	18	3445	3448	3
proximal 18q microdeletion	–	601808	18q12.3-q21.1	18	37500	42500	5000
Pitt–Hopkins syndrome/PTHS	–	610954	18q21.1	18	51083	51282	199
microdeletion 18q22.3-q23	–	607842	18q22.3-q23	18	70474	73111	2637
–	Sotos-like microduplication 19p13.2	–	19p13.2	19	9107	11094	1987
microdeletion 19p13.13	microduplication 19p13.13	613638	19p13.13	19	12793	13104	311
microdeletion 19p13.12	–	–	19p13.12	19	14119	14439	320
microdeletion 19p13.11	–	–	19p13.11	19	16485	17554	1069
microdeletion 19q13.11	–	613026	19q13.11	19	37300	40200	2900
Diamond–Blackfan anemia/DBA	–	105650	19q13.2	19	47056	47067	11
microdeletion 20p12.3	–	112261	20p12.3	20	6907	7012	105
Alagille syndrome 1/ALGS1	–	118450	20p12	20	10478	10669	191
microdeletion 20q13.13-q13.2	–	–	20q13.13-q13.2	20	49760	50840	1080
Albright hereditary osteodystrophy/AHO	–	103580	20q13.32	20	56900	56920	20
microdeletion 20q13.33	–	–	20q13.33	20	61246	62376	1130
microdeletion 21q21.1	–	–	21q21.1	21	19950	20250	300
–	microduplication 21q21.3	–	21q21.3	21	25960	26470	510
platelet disorder/PD	–	601399	21q22.12	21	34743	35343	600
–	Down syndrome/DS	190685	21q22.13	21	37300	38502	1202
–	Cat-Eye syndrome/CES	115470	22p11.1-q11.21	22	0	16977	16977
Di George syndrome/CATCH22/DGS	microduplication 22q11.2	608363/145410	22q11.21-q11.23	22	16932	20672	3740
distal microdeletion 22q11.2 (BCR, MAPK1)	distal microduplication 22q11.2 (BCR, MAPK1)	611867	22q11.2	22	20446	22026	1580
neurofibromatosis 2 microdeletion syndrome	–	101000	22q12.2	22	28330	28425	95
Phelan–McDermid syndrome	microduplication 22q13 (SHANK3)	606232	22q13	22	49449	49691	242
Leri–Weill dyschondrosteosis/LWD	–	127300	Xp22.33	X	0	724	724
X-Linked autism-2/AUTSX2	–	300495	Xp22.32-p22.31	X	5818	6157	339
Steroid sulphatase deficiency/STS	–	308100	Xp22.31	X	6452	8128	1676
Kallmann syndrome 1/KAL1	–	308700	Xp22.31	X	8457	8660	203
MIDAS syndrome	–	309801	Xp22.2	X	11039	11659	620
Nance–Horan syndrome/NHS	–	302350	Xp22.13	X	16853	17768	915
microdeletion Xp22.11	–	300830	Xp22.11	X	22928	23309	381
X-linked congenital adrenal hypoplasia/AHC	DAX1 microduplication	300679	Xp21.2	X	30233	30237	4
complex glycerol kinase/CGK	–	300679	Xp21.2	X	30233	30659	426
muscular dystrophy Duchenne/DMD	–	310200	Xp21.2	X	32445	33268	823
Xp11.3 deletion syndrome	–	300578	Xp11.3	X	46193	46627	434
Goltz syndrome/GS	–	305600	Xp11.23	X	48252	48264	12
–	17-beta-hydroxysteroid dehydrogenase X/HSD	300801	Xp11.22	X	53467	53730	263
–	microduplication Xq12q13.1	300127	Xq12-q13.1	X	67435	68633	1198
X inactivation specific transcript/XIST	–	314670	Xq13.2	X	72863	73063	200
Bruton agammaglobulinemia/XLA	–	300755	Xq22.1	X	100490	100497	7
microdeletion Xq22.2	Pelizaeus–Merzbacher microduplication/PMD	312080	Xq22.2	X	102609	103098	489
microdeletion Xq22.3q23	–	300194/303631	Xq22.3-q23	X	107214	110239	3025
lymphoproliferative syndrome 1/XLP1	–	308240	Xq25	X	123308	123335	27
–	X-linked hypopituitarism/SRXX3	300833	Xq27.1	X	139413	139415	2
fragile site mental retardation 1/FMR1	–	309550	Xq27.3	X	146801	146840	39
microdeletion Xq28	–	–	Xq28	X	147043	147543	500
Rett syndrome/RS	MECP2 microduplication	300475/300815/ 300845	Xq28	X	152535	153044	509
sex-determining region Y/SRY	–	480000	Yp11.31	Y	2715	2716	1
AZFa microdeletion	–	415000	Yq11.21	Y	12934	13664	730
AZFb microdeletion	–	415000	Yq11.221- q11.223	Y	18698	24475	5777
AZFb+c microdeletion	–	415000	Yq11.221-q11.23	Y	18474	26203	7729
AZFc microdeletion	–	415000	Yq11.223-q11.23	Y	23387	26203	2816