Dr Sally Ann Lynch
email: lynch@olchc.ie

N Foulds

A C Thuresson

A L Collins

G Annerén

B O Hedberg

J Iremonger

C M Murray

J A Crolla

C Costigan

W Lam

D R Fitzpatrick

R Regan

S Ennis

& Fh

The 12q14 microdeletion syndrome; 6 new cases confirming the role of HMGA2 in growth

Keywords: 12q14 microdeletion, array-CGH, HMGA2, short stature

We report 6 patients with array deletions encompassing 12q14. Out of a total of 2538 array investigations performed on children with developmental delay and dysmorphism in 3 diagnostic testing centres; 6 positive cases gives a frequency of 1 in 423 for this deletion syndrome. The deleted region in each of the six cases overlaps significantly with previously reported cases with microdeletions of this region. The chromosomal range of the deletions extends from 12q13.3q15. In the current study we report overlapping deletions of variable extent and size but primarily comprising chromosomal bands 12q13.3q14.1. Four of the six deletions were confirmed as de novo events. Two cases had deletions which included HMGA2 and both children had significant short stature. Neither case had osteopoikilosis despite both being deleted for LEMD3. Four cases had deletions that ended proximal to HMGA2 and all of these had much better growth. Five cases had congenital heart defects including 2 with atrial septal defects, one each with pulmonary stenosis, sub-aortic stenosis and a patent ductus. Four cases had moderate delay, two had severe developmental delay, and a further two had a diagnosis of autism. All six cases had significant speech delay with subtle facial dysmorphism.

INTRODUCTION

Chromosomal microdeletions and duplications are known to be a significant cause of developmental delay and congenital malformation in humans. In recent times the impact of array-CGH technologies has allowed the rapid identification and delineation of many new syndromic conditions in patients with idiopathic learning disability and congenital dysmorphism. Microdeletions of chromosomal region 12q14 have been associated with characteristic features that include low birth weight, failure to thrive, short stature, developmental delay and facial dysmorphism. [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF][START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF][START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF][START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] To date, six cases with deletions spanning this region and with similar phenotype have been reported as 12q14 microdeletion syndrome. Similar deletions have also been described in children with features of Silver-Russell syndrome and/or primordial dwarfism. [START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] The severe growth retardation in these cases is thought to be due to loss of HMGA2 which lies within this region. Osteopoikilosis, due to deletion of the LEMD3 gene, has been described in some but not all cases reported to date.

We present a further 6 children with developmental delay and array-CGH detected deletions within 12q14. Two of the children have deletions that included HMGA2 and LEMD3. The other four have deletions that ended proximal to these two genes. The children with the more distal deletions, extending to and including HMGA2 and LEMD3 were severely growth retarded with growth of 4 standard deviations below the mean.

Neither child had osteopoikilosis. The other four children with more proximal deletions have much milder evidence of growth retardation. Our cases confirm the association between severe growth retardation and loss of HMGA2 and highlight the possibility that cases of primordial dwarfism and/or Silver-Russell syndrome with developmental delay should be investigated for deletions within this region.

CLINICAL CASES

Case 1

The child, a girl, was born by caesarean section at 36 weeks weighing 2.05 kg at birth (Figure 1A). Her head circumference was 32 cm and length 45 cm. Her failure to thrive resulted in a provisional diagnosis of Silver-Russell being made, although there was no asymmetry, no café au lait patches and no history of excess sweating. She sat independently at 18 months, and she "bottom shuffled" until the age of 4 yrs when she eventually walked. She has speech delay although this is predominantly expressive. At the age of 11 yrs she is moderately developmentally delayed and is quite hyperactive, although she can concentrate. She can speak full sentences in both English and Polish, however she had significant expressive speech delay as a younger child. She cannot read or write. She can recognise colors, is good at jigsaws, and likes singing and music. She can dress herself and manage basic hygiene. She is described as a naughty but popular child. She suffers from gastrointestinal reflux and previously suffered from constipation. On examination aged 7 yrs 11 months her Ht was 101.5 cm (-4.21 SDS), Wt 14.2 (-4.42 SDS) and OFHC 53.2 cm (+1.13 SDS). Her mother and father are 161 cm and 176 cm tall respectively. She has relative macrocephaly. Her height velocity has been 3-4 cm per year. Her leg length (-2.4 SDS) is greater than her sitting height (4.9 SDS). She has a convergent squint, a high nasal bridge, long eyelashes and a cupids bow. She has clinodactyly and her terminal phalanges are broad. She had 5 th finger clinodactyly bilaterally. From the age of 10 years, she has developed truncal obesity with her weight velocity being greater than her height velocity.

Clinical Investigations

The following investigations were normal: U&E, TFTs, routine chromosome analysis, uniparental disomy for chromosome 7, MLPA telemore testing, IGF 1 18.7 nmol/l (12 -50), and IGFBP3 2.6 mg/l (1.3 -4.66). Cortisol levels were measured on several occasions and were always within normal limits and she produced a good cortisol response to insulin induced hypoglycaemia. A small anterior pituitary gland was seen on brain MRI and no other abnormality was detected. A skeletal survey noted a mild scoliosis but there was no evidence of osteopoikilosis. The bone age was 8 yrs 10 months at a chronological age of 10.5 yrs. An insulin tolerance test revealed growth hormone deficiency: Overnight growth hormone levels were subnormal for growth hormone secretion with only two peaks greater than 3.3 within 12 hours (4.5 & 4.66 mg/l). A normal result is established if at least 3 peaks per night are identified [START_REF] Brook | Clinical Pediatric Endocrinology[END_REF] . As this is less than the normal range, she has recently been started on growth hormone treatment. Of note, this child suffered a fracture to her left frontal bone as a young child and this may have been a contributory factor to the growth hormone deficiency.

Case 2

The second child, a girl was born weighing 1.93 kg at 37+4 weeks (Figure 1B). She failed to thrive from birth. She was found to have an atrial septal defect. She is severely growth retarded with growth parameters as follows: 3 months: Ht 50.2 cm (>-4SD), Wt 4.0 kg (-3SD). OFHC 37.5 cm (-2SD);17 months: Ht 67.5(>-4SD), Wt 7.5 kg(-4SD), head circumference 45.5 cm(-1.5SD); 3 yrs: Ht 81 cm(>-3.1SD), Wt 9.9 kg(-3.4SD). She still only weighed 10 kg at 3 yrs 8 months of age. The mother's height is 160 cm and her fathers 175 cm. She has failure to thrive and is currently being fed by gastrostomy. She has relative macrocephaly and is hyperactive. She has a bilateral convergent squint. She has an aversion to food and she also very rarely will take a drink. She sat independently at 1 yr and started walking at the age of 2 yrs. She has no speech but can express herself by showing what she wants. She has had several ears infections and pneumonia. She has the following dysmorphic signs, a short broad neck, frontal bossing, hypertelorism, upslanting palpebral fissures, a high nasal bridge, a short nose. The philtrum is flat, the upper lip is thin and her mouth is rather big. She has a micrognathia and low set ears. She has a single palmar crease on the left hand and a sacral dimple. X-ray at age 3 years showed no signs of skeletal/bone abnormalities. She has been diagnosed with moderate mental retardation with autism and significant behavioral difficulties. The 12q14 microdeletion was not present in the mother but DNA from a phenotypically normal father was not available for testing.

Case 3

The third case, a male was born at 39 weeks weighing 3.6 kg (Figure 1C). He had feeding difficulties as a neonate. At 4 ½ yrs of age his Ht was 101 cm (-0.6 SDS), Wt 15 kg (-1.12 SDS) and head circumference 51.5 cm (+0.79 SDS). He has moderate global developmental delay and has no speech at the age of 6 years. He walked at 2 ½ yrs. He has a mild degree of pulmonary stenosis and a progressive pectus deformity. Dysmorphic features include long eyelashes, low post hairline, frontal hair whorl, mild brachydactyly.

He has small joint hypermobility. He has poor oromotor control and constantly dribbled until the age of 4 yrs. The 12q14 microdeletion occurred as a de novo event.

Case 4

The fourth case, a male was born at 36 weeks weighing 1.9 kg (Figure 1D). He was diagnosed with pyloric stenosis for which he had surgery. He is severely developmentally delayed, hyperactive, and is thought to be on the autistic spectrum. At the age of 2 yrs 9 months he had no words. His growth parameters at age 2 yrs 11 months -OFC 48.5 cms 25 th centile, Ht 87.5 50 th cms and Wt 50 th centile IUGR 4 lbs 11 oz at 36 weeks. At age 2 yrs 3 months his growth parameters were as follows: -Ht 83.5 cms (-0.66 SDS), Wt 12.7 kg (-0.05 SDS), OFC 48.5 cms (0 SDS). At age 5 yrs 8 months his height was 105.5, weight was 18.5 kg. It was not possible to measure his OFHC. He has a history of several upper respiratory tract infections as a young child. Dysmorphic features include a marked widow's peak, up slanting palpebral fissures, a pouty lower lip with protruding tongue. He has strikingly short, broad fingers and toes, particularly his thumbs. His genitalia are normal and otherwise examination was unremarkable. At 6 yrs and 5 months he has much more interaction with people around him. He has developed single words and is using sounds in addition to defining by action very well. However, he is still extremely active and still in nappies. He is not interested in food and seems to need only a little sleep. He has started to bite his arms a bit and get frustrated with communication at times. Skeletal survey showed broad spatulate tips to distal phalanges. His parents declined genetic testing but were phenotypically normal.

Case 5

The pregnancy was complicated by a car accident at 28 weeks. The baby, a boy was born weighing 2.89 kg at 36 weeks (Figure 1E). He was admitted to SCBU overnight and fed rather poorly from the beginning. At age 8 months he was diagnosed with global developmental delay. He sat at 8 months, and walked at 18 months. He has marked speech delay. He is now 20 yrs old and attends a special school. Dysmorphic features include upslanting palpebral fissures, pinched nose, high palate, flat face, broad hands, flat feet, 2/3 syndactyly. A provisional earlier diagnosis of Lujan Fryns was made. Growth at 17.7 yrs: Ht 161.9 (-0.04 SDS), Wt, 51.1 kg (-0.80 SDS), OFC 53.2 cm (-0.99SDS). He had an atrial septal defect that required closure. The 12q14 microdeletion occurred as a de novo event.

Case 6

The sixth case (Figure 1F) was the second child of non-consanguineous parents, born at term by emergency caesarean section for fetal distress weighing 2.85 kg. This followed an uncomplicated pregnancy. There were no feeding problems. There were concerns from the beginning as the parents noticed that he was delayed in achieving his milestones. At 2 yrs and 6 months he was diagnosed to have a moderated sub-aortic stenosis. At 4 yrs and 4 months he was assessed to have moderate global developmental delay and still not putting

METHODS

Microarray Analysis

Following initial diagnostic investigations by karyotyping and multilplex ligation dependent probe amplification (MLPA (subtelomere kits P036D and P070) MRC Holland))

where no imbalances were detected, whole genome screening was carried out using whole genome 0.5Mb 'CytoChip TM ' microarrays (BlueGnome) for cases 1, & 5, SNP6.0 analysis for case 2 (Affymetrix), 44K oligo (Agilent) microarray for cases 3-4 and 135K oligo (Nimblegen) for case 6. Microarray procedures were carried out on the respective microarray platforms using standard procedures. Higher resolution screening using a 135K oligonucleotide arrays (Roche-Nimblegen) were subsequently used to more precisely define deleted breakpoints for case 1;3-6. To refine breakpoints in case 2, an Affymetrix Genome-Wide SNP Array 6.0 (Affymetrix Inc) was performed in accordance with the manufacturer's instructions. All CytoChip BAC microarrays were scanned at 10μm resolution using a GenePix Pro 5.0 array scanner (Axon) and analysed using BlueFuse for Microarrays analysis software version 3.4 (BlueGnome). All Oligo and SNP arrays were performed according to the manufacturer's protocols, scanned at 2-5 μm, and analysed using SignalMap v1.9 (135K), DNA Analytics v 4.0.76 (44K) or Genotyping Console v4.0 (SNP6.0).

Confirmation of array-CGH results

FISH techniques were performed on metaphase spreads prepared from peripheral blood for patients 1,3,& 5 and both parents wherever possible, using standard techniques. [START_REF] Chong | A revision of the lissencephaly and Miller-Dieker syndrome critical regions in chromosome 17p13.3[END_REF] Microdeletions of 12q14 were confirmed in each case using BAC probes within each respective deleted region. In case 2, three specifically designed MLPA-probes were designed to cover the region of interest. [START_REF] Stern | Multiplex ligationdependent probe amplification using a completely synthetic probe set[END_REF] Data analysis was performed with GeneMarker software 1.85 (SoftGenetics). The parents of case 4 declined genetic testing but were identified as being phenotypically normal. Quantitiative (Real-Time) PCR (qPCR) was carried out in quadruplicate (case 5 only) and data was analysed using the relative quantification method (ddC T ). [START_REF] Schmittgen | Analyzing real-time PCR data by the comparative C T method[END_REF] The control used was pooled DNA from 12 normal individuals from the blood bank and the reference/endogenous control was the single copy RNase P. Microarray-CGH investigations using a Nimblegen 135K array was using for confirmatory studies in case 6.

RESULTS

Detection of deletions encompassing 12q14

The deletions in all six affected individuals were initially detected as part of an array-CGH screen for cryptic rearrangements in patients with moderate-severe learning disability and / or congenital anomalies following normal karyotype and subtelomere MLPA analysis. In total, we had 6 positive cases out of 2538 cases referred with developmental delay for array analysis, giving a frequency of 1 in 423 cases. It is not possible to estimate an overall frequency of this deletion syndrome as the cases tested by array are a biased cohort, selected by Clinical Geneticists and would not represent the developmental delay population in general. High resolution oligo / SNP arrays were used to estimate the minimum size of the deletion breakpoints (median probe spacing ~12,524bp (135K), and ~40,000bp (44K), ~680bp (SNP6.0)), Case (NCBI Human Genome Build 36). The deleted region in each of the six cases (Figure 2) overlaps significantly with previously reported cases with microdeletions of this region [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF][START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF][START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF][START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] .

In the current study, the chromosomal range of deletions extends from 12q13.3q15. With the exception of case 5 in the current study, cases 1-4;6 carry overlapping deletions of variable size but comprise chromosomal bands 12q13.3q14.1. No other copy number variants were identified elsewhere in the genome in the six cases that have not been previously been identified as high frequency CNVs in normal population studies according to the Database of Genomic Variants (http://projects.tcag.ca/variation/). FISH verification experiments were performed on metaphase spreads from cases 1;3;5 (case 2 where MLPA was performed, case 4 (parents unavailable) and case 6 where array-CGH was used for confirmation), to validate each copy number imbalance as detected by array-CGH. All FISH probes used to confirm the array-CGH findings for each of the cases were within each respectively deleted 12q region. Normal FISH signals for each probe were seen for parental cases 1;3;5 (data not shown) confirming that these 3 cases were de novo deletions of the 12q13.3q15 region. For case 2, the microdeletion was not identified in the mother, but father was not available for testing. The parents of case 4 declined genetic testing. qPCR analysis using the TaqMan/Universal Probe Library sequence specific probes to RDH16, together with FISH was used to confirm the de novo deletion in case 5. Microarray analysis using a 135K oligo array (Nimblegen) was used to confirm a de novo deletion in case 6.

DISCUSSION

In the current communication we report a further 6 cases with deletions encompassing 12q14 which brings the total number to 12 reported cases. At a time when phenotypic variability is often associated with newly identified microdeletion syndromes, 12q14 deletions have been reported as a relatively uniform and constant phenotype of low birth weight, poor feeding in infancy, short stature and learning disability. [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF][START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF][START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF][START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] There also have been reports of some cases presenting with osteopoikilosis and skin lesions. [START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF] A further case reported by Jurenka and van Allen, [START_REF] Jurenka | Mixed sclerosing bone dysplasia, small stature, seizure disorder and mental retardation: a syndrome?[END_REF] presenting with short stature, learning disability and a mixed sclerosing bone dysplasia is also suspected of a having a 12q14 microdeletion by Mari et al. [START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF] The 12q14 deletions in our cases varied in size from 1.28Mb -10.12Mb. The apparent lack of low copy repeats near the breakpoints in each of our cases indicates that the underlying genetic mechanism of non-homologous end-joining may be responsible for recurrent deletions within this region. The number of HGNC genes within the deleted regions of all 6 cases ranged from 35-58 with 15-39 OMIM listed genes. While there is substantial genomic overlap with the previously reported 12q14 microdeletion cases, it should be reported that the shortest region of overlap (SRO) between all six cases reported in this communication differs substantially from the SRO of the reported cases in the literature. This will inevitably explain phenotypic variation between cases. The SRO of the previously reported 12q14 microdeletion cases [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF][START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF][START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF][START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] includes HMGA2. Genotype phenotype correlations are difficult to ascertain as only cases 1 & 2 are severely growth retarded but common characteristics seen include moderate -severe learning disability, speech delay, and congenital heart defects (not known for case 4) so it's possible that deletions of one or more of these genes could be responsible for any of these aspects of the phenotype. DCTN2 and KIF5A have been associated with disruptions in axonal transport, [START_REF] Lamonte | Disruption of dynein/dynactin inhibits axonal transport in motor neurons causing late-onset progressive degeneration[END_REF][START_REF] Fichera | Evidence of kinesin heavy chain (KIF5A) involvement in pure hereditary spastic paraplegia[END_REF] so in theory haploinsufficiency could account for the neurodevelopmental anomalies seen in these cases. Congenital heart defects have not been reported in any of the 6 reported 12q14 microdeletions in the literature, this suggests that the candidate cardiac gene lies upstream of the 5' ends of previously reported cases. The largest region of overlap seen in our cases was between case 1 & 2 comprised 9.65Mb and includes both HMGA2 and LEMD3.

The first report of the 12q14 microdeletion syndrome by Menten in 2007, [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF] described three patients with a deletion of 12q14 who presented with growth retardation & osteopoikilosis.

There was a 3.44 Mb common deleted region which contained LEMD3, the causal gene for osteopoikilosis. [START_REF] Hellemans | Loss of function mutations in LEMD3 result in osteopoikilosis, Buschke-Ollendorff syndrome and melorheostosis[END_REF] However, growth retardation, failure to thrive and learning disability have not been reported in patients with osteopoikilosis or melorheostosis. Mari et al, 2 described a case with primordial dwarfism and a smaller deletion of the 12q14.4 region. The child, a boy had severe pre and postnatal growth failure, and mild developmental delay. There was a history of poor feeding and failure to thrive, similar to the phenotype of primordial dwarfism or severe Silver-Russell syndrome (SRS). There was no evidence of osteopoikilosis but this child's deletion started distally to the Menten et al, [START_REF] Menten | Osteopoikilosis, short stature & mental retardation as key features of a new microdeletion syndrome on 12q14[END_REF] cases and did not include LEMD3. Buysse et al, [START_REF] Buysse | The 12q14 microdeletion syndrome: Additional patients and further evidence that HMGA2 is an important genetic determinant for human height[END_REF] described two children, one of whom had an intragenic deletion of HMGA2 and short stature providing evidence that HMGA2 is the likely growth gene. HMGA2 lies at 12q14.3 which would explain the normal growth in our cases 3-6 whose deletion ended proximally. Spengler et al, [START_REF] Spengler | Sub microscopic chromosomal imbalances in idiopathic Silver-Russell syndrome (SRS): the SRS phenotype overlaps with the 12q14 microdeletion syndrome[END_REF] identified one further case with a 12q14 deletion in a series of 20 children with a diagnosis of SRS. This child's deletion included HMGA2 and LEMD3 although he had no evidence of osteopoikilosis. Cases where HMGA2 remains intact had growth within the normal centiles. This supports the proposal that HMGA2 is the most significant gene associated with growth in this region. Loss of function of Hmga2 in the mouse results in the pygmy phenotype that combines pre and postnatal growth failure, with resistance to the adipogenic effect of overfeeding. As such it has been suggested that haploinsufficiency of HMGA2 could be associated with both reduced growth and poor feeding, failure to thrive and lack of adipose tissue that has been reported in some cases. [START_REF] Mari | Refinement of the 12q14 microdeletion syndrome: primordial dwarfism and developmental delay with or without osteopoikilosis[END_REF] This would not explain poor feeding and failure to thrive that was seen in cases 3-6 in this report where HMGA2 was not shown to be deleted.

Interestingly, case 1 had documented low overnight growth hormone levels. As this child had a significant head injury resulting in a frontal fracture as a baby, it is difficult to know if the low growth hormone levels are related to the deletion or as a result of the head injury or perhaps a combination of both events. This child has just recently commenced growth hormone therapy and we wait to see how this affects growth. The case reported by Mari et al, 2 had been treated with growth hormone without any obvious benefit. Growth hormone levels in the other cases have not been documented. There were some dysmorphic features common to cases 1-4 including a cupids bow upper lip, of all cases: Two of the children (cases 1 & 3) had similar facial dysmorphism with long eyelashes and a cupids bow. A prognathic appearance is also present in cases 1-3. There was no evidence of osteopoikilosis in any of our cases despite a deletion of LEMD3 in cases 1 & 2. Five of the 6 cases had congenital heart anomalies. Two cases had an atrial septal defect (case 2 & 5), one had pulmonary stenosis (case 3), one had subaortic stenosis and case 1 presented with a patent ductus. One child had pyloric stenosis but all bar case 6 had feeding difficulties.

Interestingly, severe speech delay was noted in cases 2-6. Case 1, having expressive speech delay early on can currently speak full sentences in both Polish and English at age 11 years. We conclude that the 12q14 microdeletion syndrome is a recurrent deletion syndrome with a dysmorphic phenotype which may be recognisable in some cases. One needs to consider the diagnosis in children with severe growth retardation and developmental delay. Our cases, particularly cases 3-6 whose growth was within normal centiles and whose deletion ended proximal to HMGA2 provide further evidence that HMGA2 is a good candidate gene for growth retardation. We would recommend growth hormone level measurements in other cases with deletions of HMGA2 to see if growth hormone deficiency is a common finding.

Further testing for loss of function mutations in HMGA2 is also required to test for idiopathic short stature.

TITLE AND LEGENDS TO FIGURES

Birth wt (Kg)

2kg @ 36 wk 1.9 @ 37 wk +4 3.6kg @ 39 wk 1.9 @36wk 2.9 @ 36wk 2.86 @ 40 wk

Development

2

  words together. He has hypermetropia. His growth parameters were Ht 95 cm (-2.3 SDS), Wt 15.1 kg (-1.1 SDS) and OFHC 49 cm (-2.3 SDS). Dysmorphic features include brachycephaly, deep set eyes, bilateral low set posteriorly rotated ears, short nasal bridge with an upturned nose and a long simple philtrum with thin upper lip. He has focal cortical dysplasia in insular cortex on brain MRI. The 12q14 microdeletion occurred as a de novo event.

  Two of the cases(2 & 4) were also identified as having "autistic features". The dysmorphism in these children is subtle. They all had a cupids bow. Cases 1 & 2 had deep set eyes and a squint and these two children have a very similar facial appearance. The dysmorphism in the other 4 cases was subtle. All cases bar case 6 had a tall forehead. Cases 1, 3, 4 & 6 had arched eyebrows.

Figure 1 .

 1 Figure 1. Clinical photographs of 5 affected individuals with 12q14 deletions. Informed

Figure 2 .

 2 Figure 2. Schematic illustration of the six 12q14 deletions detected by array-CGH using

Figure 1

 1 Figure 1 Photographs of Case 1-4 & 6. All have cupids bow & tall forehead. Case 1 & 2 have deep set eyes and a squint

Table 1 .

 1 Summary of the clinical features presented in 6 patients with deletion of 12q14.

		Case 1	Case 2	Case 3	Case 4	Case 5	Case 6
	Sex	F	F	M	M	M	M

ACKNOWLEDGEMENTS

We would like to thank all of the families for agreeing to participate in this study.

CONFLICT OF INTEREST

The authors confirm no conflict of interest.


Ricardo Segurado
email: ricardo.segurado@tcd.ie

Mark A Bellgrove

Francesca Manconi

Michael Gill

Ziarah Hawi

Epistasis between neurochemical gene polymorphisms and risk for ADHD

Keywords: ADHD, epistasis, candidate gene

A number of genes with function related to synaptic neurochemistry have been genetically associated with Attention-Deficit/Hyperactivity Disorder. However, susceptibility to the development of common psychiatric disorders by single variants acting alone, can so far only explain a small proportion of the heritability of the phenotype. It has been postulated that the unexplained "dark heritability" may at least in part be due to epistatic effects, which may account for the small observed marginal associations, and the difficulties with replication of positive findings. We undertook a comprehensive exploration of pair-wise interactions between genetic variants in 24 candidate genic regions involved in monoaminergic catabolism, anabolism, release, re-uptake and signal transmission, in a sample of 177 parent-affected child trios using a case-only design and a casepseudocontrol design using conditional logistic regression. Markerpairs thresholded on interaction odds ratio and p-value are presented.

We detected a number of interaction odds ratios greater than 4.0, including an interesting correlation between markers in the ADRA1B and DBH genes in affected individuals, and several further interesting, but smaller effects. These effects are no larger than you would expect by chance under the assumption of independence of all pair-wise relations, however independence is unlikely. Furthermore, the size of these effects is of interest and attempts to replicate these results in other samples are anticipated.

Introduction

The heritability of Attention Deficit/Hyperactivity Disorder (ADHD) is well established, and genetic association data has been reviewed and meta-analysed recently by Gizer and colleagues [START_REF] Gizer | Candidate gene studies of ADHD: a meta-analytic review[END_REF] . The detection of DNA variants which increase risk for this disorder is important for biochemical and pharmacological research into this disorder, and may permit the facilitation of diagnosis, or refinement of the phenotype on the basis of a biological marker. Specifically, association between monoaminergic genes and ADHD is an active area of investigation, stimulated principally by the mode of action of current pharmacological treatments for the disorder, primary among which is methylphenidate, a known dopamine and noradrenaline re-uptake inhibitor. Associations between variants at the dopamine, serotonin and noradrenaline transporter and receptor genes, with ADHD, have been investigated, and meta-analyses support the presence of ADHD risk alleles with small to moderate effects at the DRD4, DRD5 and SLC6A3 genes [START_REF] Gizer | Candidate gene studies of ADHD: a meta-analytic review[END_REF][START_REF] Li | Meta-analysis shows significant association between dopamine system genes and attention deficit hyperactivity disorder (ADHD)[END_REF][START_REF] Yang | A meta-analysis of association studies between the 10-repeat allele of a VNTR polymorphism in the 3'-UTR of dopamine transporter gene and attention deficit hyperactivity disorder[END_REF] in candidate gene studies, and modest evidence at ADRA1A, TPH2 and DDC from a recent meta-analysis of genome-wide association studies [START_REF] Neale | Meta-analysis of genomewide association studies of attention-deficit/hyperactivity disorder[END_REF] .

The influence of higher-order interactions between genes has been posited for common, complex genetic disorders on the basis of biological plausibility, and the presence of interactions influencing gene expression and protein function both in model organisms and in human disease susceptibility [START_REF] Elena | Test of synergistic interactions among deleterious mutations in bacteria[END_REF][START_REF] Clark | Epistasis in measured genotypes: drosophila Pelement insertions[END_REF] . In the case of ADHD the most likely known risk genes inhabit the same biochemical neighbourhood, in that the functions of each gene product are related directly via proteinprotein interaction, or indirectly via regulatory pathways. Therefore there are grounds for prior supposition that common genetic variants in several risk genes may act synergistically to influence disease risk. Methods for detecting interaction, or epistasis, in case-control samples have received much attention recently [START_REF] Cordell | Detecting gene-gene interactions that underlie human diseases[END_REF][START_REF] Chatterjee | Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions[END_REF] , particularly in the rapidly developing areas of machine learning. Methods for family-based samples are less well developed -usually adaptations of case-control methods (e.g. using matched case-pseudo-control samples). However, in addition to large family based samples being used in the latest generation of gene-mapping studies (e.g. the Autism Genome Project 11 ; IMAGE: [START_REF] Neale | Genome-wide association scan of attention deficit hyperactivity disorder[END_REF] ), family-based samples were extensively used in candidate gene experiments prior to the emergence of genome-wide association studies.

These are excellent genotype resources for exploring epistasis as these studies have genotyped a relatively small number of genetic polymorphisms within strong candidate genes.

The aim of this study was to use a subset of genetic markers which we expect to be enriched for true involvement in pathogenesis of ADHD, to: 1) attempt to detect any large epistatic effects which may exist;

2) explore epistatic effect sizes between candidate genes, for prioritisation of further investigation in this sample, and replication in other samples. To this end a limited number of variants in a range of strong candidate genes for ADHD were selected (see Methods). As this study was a focused exploration of interaction, we elected to use two principal techniques to analyse the data thoroughly for various forms of interaction effects. Thus, firstly, a case-only design was adopted in order to detect association between unlinked genes in affected individuals. This allows calculation of an "interaction odds ratio", equivalent to the interaction relative risk under the assumption of independence of the genes in the general population [START_REF] Pierce | Case-only genome-wide interaction study of disease risk, prognosis and treatment[END_REF] . Secondly, a similar model was fitted to binary affection status by logistic regression [START_REF] Cordell | Case/Pseudocontrol analysis in genetic association studies: a unified framework for detection of genotype and haplotype associations, gene-gene and gene-environment interactions, and parent-of-origin effects[END_REF] . This method examines patterns of transmission of alleles from each of the markers in a pair, thus is robust to bias arising from population stratification.

Methods

Recruitment and Sample description

Marker selection and Genotyping

Gene selection began with a set of candidate genes previously typed in this sample, and was expanded to include several genes found to be associated in other samples and to genes within neurochemical systems plausibly linked to ADHD (table 1; supplemental tables S1 and S2).

This study was limited to 25 autosomal genes (CES1 and SLC6A2 were treated as one unit due to physical proximity). Markers within these genes were either (i) previously characterised in this sample; (ii) previously associated in other samples; (iii) selected due to known or suspected functional relevance (on gene or protein expression, protein sequence, and including presence in a known promoter region and lying within 500 base pairs of an exonic boundary); or (iv) in order to tag the remainder of the gene (r 2 ≥ 0.8) insofar as gene size and marker density made possible. Markers and genes were placed on the human genome 18 physical map (hg18; NCBI build 36.3) using dbSNP (http://www.ncbi.nlm.nih.gov/SNP/), or by linear interpolation from flanking markers.

A total of 179 markers were genotyped. Single Nucleotide Polymorphisms (SNPs) were genotyped commercially at K-Bioscience using a competitive allele-specific polymerase chain reaction (PCR) -KASPar (K-Bioscience, Hoddesdon, UK; personal communication), or Taqman chemistries (Applied Biosystems, California, USA). Three markers, at the DBH (rs1611115 and rs6271) and COMT (rs4680) genes were genotyped in-house with Taqman assays on a 7900HT sequence analyzer (Applied Biosystems). Microsatellite and minisatellite markers were typed inhouse as described previously; these included the markers in SLC6A3 and DRD5 [START_REF] Hawi | Linkage disequilibrium mapping at DAT1, DRD5 and DBH narrows the search for ADHD susceptibility alleles at these loci[END_REF] , DRD4 [START_REF] Lowe | Multiple marker analysis at the promoter region of the DRD4 gene and ADHD: evidence of linkage and association with the SNP -616[END_REF] , TH [START_REF] Hawi | Preferential transmission of paternal alleles at risk genes in attention-deficit/hyperactivity disorder[END_REF] and SLC6A4 [START_REF] Kent | Evidence that variation at the serotonin transporter gene influences susceptibility to attention deficit hyperactivity disorder (ADHD): analysis and pooled analysis[END_REF] .

Multiallelic (> 2 alleles) markers were collapsed into 2 categories on the basis of 1) bimodality of the allele frequency histogram in founder individuals, 2) so as to approximate equifrequency of the down-coded "alleles". This might be expected to reflect the genetic history of the microsatellite. We did not use previously associated alleles at these markers as a basis for recoding [START_REF] Gatto | Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias[END_REF] .

Only autosomal genetic markers with a minor allele frequency in founders of >0.05 were retained; only markers with over 80% genotype rate were included; Pedcheck 21 was used to detect Mendelisation errors, genotypes showing errors were removed for the entire trio; finally, markers which showed extreme deviation from Hardy-Weinberg equilibrium in the parents were excluded due to the possibility of miscalled genotypes. When markers within the same gene were in near perfect linkage disequilibrium in founders (r 2 ≥ 0.98), the one with least complete genotyping was discarded.

Data Analysis

An allelic case-only test for pair-wise interaction of SNPs 22 was performed, using the proband from each pedigree. In order to exclude the effect of linkage disequilibrium between SNPs, all intragenic SNP pairs, and all intergenic SNP pairs separated by less than 500 kilobases were excluded. Tables with any zero cell count were discarded. A chi-square test (1 d.f.) was performed on the 2x2 tables, and an odds ratio estimate was calculated.

The null distribution of the odds ratio for each pair of SNPs examined was simulated by fixing genotypes at the first marker, and permuting genotypes at the second. Two-sided p-values for the odds ratios were estimated from 50 000 permutations, or from 1 000 000 permutations for marker pairs which showed an initial empirical p-value less than 0.001. 95% confidence intervals for the interaction Odds Ratios were generated from 1 000 bootstrap samples, for each marker pair. A global, family-wise p-value was also calculated from the proportion of the 50 000 permutations in which an odds ratio was exceeded in any of the pair-wise tests.

Interactions were also tested in a logistic regression model, implemented on a matched case-pseudocontrol sample [START_REF] Cordell | Case/Pseudocontrol analysis in genetic association studies: a unified framework for detection of genotype and haplotype associations, gene-gene and gene-environment interactions, and parent-of-origin effects[END_REF] . Fifteen pseudocontrols were generated for each trio, for each marker pair, from all combinations of parental two-marker genotypes. Two models were fitted to the data in R (http://cran.r-project.org) with the coxph function from the survival package, using robust variance estimation, stratified on a family ID variable (equivalent to a conditional logistic regression). An interaction parameter was tested by contrasting the model with a nested model containing main effects alone using a likelihood ratio chi-square test with one degree of freedom. Only additive allelic effects were examined. For each marker pair, perfectly collinear variables were handled by removing one from the model. A pseudo-R 2 for each model representing how much of the variance in the dependent variable (affection status) is explained by the genetic terms was also calculated.

Results

Twenty-seven markers were excluded (see Methods) due to low genotyping rate, 4 due to low minor allele frequency in parents, and 3 due to extreme deviation from Hardy-Weinberg equilibrium in parents (goodness-of-fit Chi-square, p < 0.00033 = 0.05/177). Interaction between one pair of genes (CES1-SLC6A2) was not examined due to physical proximity <500 kbp. Four markers were removed due to near perfect linkage disequilibrium in parents with neighbouring markers (r 2 = 0.98 -1.00). Table 1 shows genic positions, marker number and spacing. The Chi-square test statistic had very low correlation with the intermarker distance, for syntenic markers (supplemental figure S3).

The remaining 141 markers had a mean missing genotype rate of 0.075 in affected individuals. We estimated from single-marker missingness that 82.4% (142) of cases would be genotyped at both markers of a markerpair, on average across all between-gene pairs. The observed average was 85.3%. Mean minor allele frequency in founders was 0.304 (range: 0.051 -0.498). The mean expected frequency of minor allele -minor allele conjunctions over all marker pairs in cases was 0.0874 (range: 0.0023 -0.2468), and approximately 64% of expected minor alleleminor allele pair frequencies were greater than 0.05. The observed mean minor allele -minor allele frequency in cases was 0.0871, with 71.0% being greater than 0.05. A total of 9 269 SNP-SNP case-only interaction tests were performed within the 276 gene pairs. After exclusion of sparse tables 9 262 tests (99.9%) remained. A QQ plot of the chi-square statistic showed some deflation of the statistic relative to the expected 1 degree of freedom chi-squared distribution, with a "stepped" appearance, most likely due to non-independence of tests (supplemental figure S2).

Results are presented sorted by the odds ratio estimate, or by p-value (table 2 and supplemental table S3).

The largest odds ratio observed was 7.27 between rs129882 in Dopamine Beta-Hydroxylase (DBH), and rs2283135, a relatively rare variant in the SLC18A2 gene. Scaled to lie above 1, nine estimated interaction odds ratios exceeded 4.0, of which five included the rarest variant in the study: rs916455 in DRD4, which had a minor allele frequency of 3% in cases. For most of these the bootstrap confidence interval calculation failed at either the upper or lower bound due to a sparse table. A total of 20 marker pairs showed an Odds Ratio > 3.0, including 12 pairs to which rs916455 contributed.

The smallest p-value observed was 5.4 x 10 -5 between rs11953285 and rs1611115, in the ADRA1B and DBH genes, respectively (Table 2A). We note that no global p-value dipped below 0.97, between SLC18A2 and DBH, and all marker-pairs excluded from Table 2 showed a global pvalue = 1.

The conditional logistic regression approach yielded maximum pseudo-R 2 values of 0.033 (partial model) to 0.045 (full model), indicating a small fraction of the variance in affection status could be accounted for by the interaction terms. Correlation with the top case-only test results was strong (Spearman rho for p-values = 0.776); over 80% of interaction Odds Ratios were in the same direction, i.e. with the same allele (supplemental figure 5). Table 2 and supplmentary table S3 show the results from the logistic regression.

Discussion

From Pierce and Ahsan [START_REF] Pierce | Case-only genome-wide interaction study of disease risk, prognosis and treatment[END_REF] , we estimate that a single pair-wise case-only analysis with our sample size may have had good power to detect relatively large interaction relative risks (>2), with interacting minor allele frequencies of approximately 0.3. Considering a range of minor allele frequencies from 0.05 to 0.5, the least power would be obtained in the case where interacting loci both have risk alleles with frequency of 0.05. In this case 80% power would be obtained for an odds ratio of 4.86 (p < 0.05). If one locus had risk allele frequency of 0.05, and the other locus 0.5, 80% power is obtained at OR = 2.24. If both risk alleles had frequency 0.5, an odds ratio of 2.5 could be detected with 80% power at a significance level of 5.1 x 10 -6 . Effects of these magnitudes are large by the standards seen to date in genetic association studies of ADHD, however with the large number of tests between all markers in the candidate genes we examined, we did not expect to be able to achieve an acceptable gobal false positive level, nor in fact was this acheived. Therefore, we leave these findings as exploratory, to be used for prioritisation of future studies in independent samples. In a more detailed exploration of the interaction model (using, for example a four-parameter model of additive x additive, additive x dominance, dominance x additive, dominance x dominance) it was universally the case that unreliable parameter estimates were obtained for the top marker-pair interactions, and the model likelihoods did not converge. This may simply reflect our modest sample size.

The presence of markers with a low MAF in the top interactions ranked by the odds ratios is expected, due to unreliability of odds ratios derived from tables with small cell sizes as indicated by wide confidence intervals, and as reflected in the p-values for these tests (see supplemental figure 4). When the Odds Ratio is sensitive to small observed cell counts, the empirical p-value is of use to judge whether a particularly large estimate would be likely to occur by chance alone.

It is probable that several factors exist which preclude the independence of the individual pair-wise interaction tests performed with either the case-only design, or the regression. These include linkage disequilibrium within genes to due ancestral co-segregation.

We hope to have robustly excluded any effect due to LD between syntenic markers by using a severe minimum distance between genes of 500 kilobasepairs. However, adjacent genes frequently share features at a functional level and in expression profile [START_REF] Hurst | The evolutionary dynamics of eukaryotic gene order[END_REF] , and a large minimum distance may exclude some true functional interactions. Other potential confounders may also cause correlation between genes. These include, but are probably not limited to, higher-order interaction effects on disease, on survival to age of sampling, and membership of cryptic subpopulations. We have not tested the first of these due to sample size limitations, and to avoid a large increase in the number of tests and increased computational burden. Epistatic effects on survival to sampling cannot be excluded, however no reports of marginal effects at these genes exist in the literature. Unfortunately this sample was not genotyped at sufficient loci to allow an estimate of population stratification, or to allow a genomic correction to be applied, however, the sample is ethnically homogeneous therefore we do not expect any severe bias. This is supported by the strong correlation between the case-only and the family trio-based logistic regression results, which is not susceptible to the cryptic population structure in the sample.

As this study was intended to be hypothesis-generating, no threshold defining statistical significance was enforced. Formal statistical evaluation of the evidence for interaction at the gene pair level is not trivial, as it would require consideration of both the number of markers within each gene and the extent of linkage disequilibrium, as well as higher order effects. Methods such as that of Chatterjee and colleagues 10 are of interest, but are not yet applicable to familybased or case-only designs. The LD patterns within each gene are relatively low (supplemental figure 1), and use of a gene-based permutation procedure which controls for LD will be important when a threshold for statistical significance is applied.

The most studied gene -gene interaction in ADHD is for a joint effect of the dopamine receptor 4 gene (DRD4) and the dopamine transporter gene (SLC6A3) VNTR polymorphisms, with some suggestive results [START_REF] Roman | Attention-deficit hyperactivity disorder: a study of association with both the dopamine transporter gene and the dopamine D4 receptor gene[END_REF][START_REF] Carrasco | Genotypic interaction between DRD4 and DAT1 loci is a high risk factor for attentiondeficit/hyperactivity disorder in Chilean families[END_REF][START_REF] Henríquez | Combination of DRD4 and DAT1 genotypes is an important risk factor for attention deficit disorder with hyperactivity families living in Santiago, Chile[END_REF] . We were unable to demonstrate strong effects between DRD4 and SLC6A3.

There is also a report in the literature of an interaction between the Noradrenaline transporter (SLC6A2 / NET1) and the catechol-Omethyltransferase (COMT) genes affecting quantitative traits derived from an ADHD self-report scale [START_REF] Retz | Norepinephrine transporter and catecholamine-O-methyltransferase gene variants and attentiondeficit/hyperactivity disorder symptoms in adults[END_REF] . Pairs of markers from these genes are amongst our top results, ranked by p-value: COMT marker rs737866, when paired with three SLC6A2 markers had Odds Ratios of 1.89 (95% CI:

1.27-2.83; p = 0.000866) with rs36017, 1.66 (1.14-2.41; p = 0.0085) with rs1345429, and 1.76 (1.23 -2.68; p = 0.00128) with rs5569. These three SLC6A2 variants are relatively highly correlated with each other (r 2 from 0.38 to 0.90; all D' > 0.97), which may explain the correlation of odds ratios (see Supplmentary materials, figure S1(O)).

It has been hypothesised that functional disruption of serotoninrelated genes (SLC6A4, HTR1A, HTR1B) may act synergistically at the synapse to increase extracellular serotonin levels [START_REF] Stoltenberg | Epistasis among presynaptic serotonergic components[END_REF] . We did observe modest interaction effects (table 2) between two of these genes -SLC6A4 and HTR1B (we did not examine HTR1A). While this did not stand out as a finding in the case-only analysis, the logistic regression showed a large interaction odds ratio estimate of 3.24 (suppl. table S3).

Two pairs of interactions with the smallest p-values stand out. ADRA1B showed a minimum p-value of 0.00005 with DBH (odds ratio = 4.56). DBH converts dopamine (DA) to noradrenaline (NA), and in particular the SNP rs1611115 (-1021C→T) in the DBH gene promoter has been shown to have a strong effect on plasma enzyme activity [START_REF] Zabetian | A quantitative-trait analysis of human plasma-dopamine beta-hydroxylase acitivty: evidence for a major functional polymiorphism at the DBH locus[END_REF] . Here the "lowactivity" C allele (implying lack of NA and accumulation of DA) shows a synergistic effect with an ADRA1B allele to increase risk. An interaction at or near this SNP could potentially explain some of the heterogeneous marginal association findings at this gene [START_REF] Gizer | Candidate gene studies of ADHD: a meta-analytic review[END_REF] . There is a dearth of well-targetted studies on the ADRA1B gene product, and its central neural functions are unknown.

The second pair involves HTR2A with ADRA1A, showing a minimum p-value of 0.0008, with an odds ratio = 1.96 (see table 2, supplemental table S3). Linking these results, one publication has suggested that a HTR2A-mediated pathway may compensate for ADRA1B inactivity in a mouse hyperactivity model [START_REF] Auclair | 5-HT2A and alpha1b-adrenergic receptors entirely mediate dopamine release, locomotor response and behavioural sensitization to opiates and psychostimulants[END_REF] , raising the possibility that the products of these two genes may have some complementary function in a dopaminergic regulation that is also targetted by amphetamines (including methylphenidate).

Of course, a reasonable disease mechanism may be deduced from any subset of interactions that one selects, especially since these genes and polymorphisms were preselected for this very reason -this discussion on the potential functional relevance of these results is purely speculative, especially in the absence of known functional effects of the markers studied. However, these results have implicated a number of gene-pairs with interesting effect sizes, and suggestive of a functionally consistent set of interactions which may converge on a common neurochemical phenotype, and which should be prioritised in future studies.

While the detection of a statistical interaction may imply a biological (physico-chemical) interaction between the genes and/or gene products or via regulatory mechanisms, proof and further elucidation require extensive cell and molecular experiments. We also note that the statistical tests performed were exploratory, and we place greater emphasis on estimates of the effect sizes demonstrated here; which must, however, must await replication in a larger, independent sample. Here we were able to primarily investigate interaction in the form of a deviation from multiplicative effect on the risk ratio at pairs of markers in or near strong candidate genes.

Interactions may lead to a marginal effect on disease risk at either or both of the loci, however such marginal effects may appear considerably smaller than the true, unmeasured interaction, and therefore on a genome-wide scale power to detect such effects will be low, while controlling the family-wise error rate. For this reason we feel that directed studies of interaction such as the present one may be fruitful, when small but suggestive marginal effect sizes at the genes of interest have been demonstrated. This exploration of interactions between a select set of candidate genes in our sample of ADHD cases has found some interesting results, justifying such further study. chromosomes are colour-coded; gene spans in black. Note that the regions around the DDC and DRD5 genes are rescaled reltive to other genes (1:17 and 1:5 respectively) due to distance to the furthest marker; interaction links are shaded by quantile: grey = 99.5 th -99.9 th , black = >99.9 th ; arc widths are proportional to the interaction Odds Ratio. position (base pairs from pter; hg18); odds ratio were rescaled to lie above 1, confidence intervals were derived from 1,000 bootstrap samples, "N/A" indicates that a ceiling or floor effect was observed in the bootstrap estimates, due to sparse contingency table; see supplementary materials for the full results.

Titles and legends to figures

(1) -empirical p-value from 50,000 or 1,000,000 (in bold) Monte Carlo permutations.

(2) -Minor allele frequency < 0.1 in parents.

(3) -McFadden pseudo-R 2 for the full model including main effects and interaction.

NB: The p-values presented are unadjusted for multiple testing; none of them are significant at p < 0.05 after correction for the number of tests.

  One hundred and seventy seven children and their families participated in the study including 5 families with two affected children. The families were recruited from child guidance clinics, ADHD support groups, and via direct clinician refferal. A stringent diagnosis of DSM-IV and/or ICD10 ADHD was made as described by Kirley et al 15 , using the Child and Adolescent Psychiatric Assessment (CAPA) and the Teacher Telephone Interview. Exclusion criteria included epilepsy, fragile X syndrome, foetal alcohol syndrome, pervasive developmental disorder, Tourette's syndrome, psychosis or IQ <70. Affected children met research criteria for ICD-10 hyperkinetic disorder (HKD: 100 children) and/or DSM-IV ADHD (combined: 112, inattentive: 21, or hyperactiveimpulsive: 11 subtypes), and/or a clinical diagnosis of ADHD not meeting full research criteria, but deemed to lie on the broad ADHD phenotypic spectrum. Therefore the present sample consisted of 172 parent -affected child trios.

Figure 1 .

 1 Figure 1. Visual representation of the interaction relative risk

Table 1 .

 1 Description of candidate genes typed and marker coverage. Spacing based on human genome build hg18.

			Position (b.p.)		Mean marker
						spacing
	Gene	Chr	start	stop	#markers	(bp)
	ADRA2B	2	96,142,350	96,145,615	1	-
	ADRA2C	4	3,738,094	3,740,051	1	-
	DRD5	4	9,392,356	9,394,731	3	663,473
	ADRA1B	5	159,276,318	159,332,595	11	4,433
	ADRB2	5	148,186,349	148,188,381	2	33
	SLC6A3	5	1,445,910	1,498,538	2	17,933
	HTR1B	6	78,228,667	78,229,839	5	470
	DDC	7	50,493,628	50,600,648	2	3,744,601
	ADRA1A	8	26,661,584	26,778,839	14	8,550
	DBH	9	135,491,306	135,514,287	11	2,315
	ADRA2A	10	112,826,780	112,830,652	3	1,859
	SLC18A2	10	118,990,706	119,027,085	5	9,173
	DRD4	11	627,305	630,703	5	1,011
	TH	11	2,141,735	2,149,611	1	-
	TPH1	11	17,998,660	18,018,911	3	3,526
	TPH2	12	70,618,893	70,712,488	12	6,266
	HTR2A	13	46,305,514	46,369,170	8	8,701
	SLC6A2	16	54,248,057	54,295,201	9	6,526
	CES1	16	54,394,265	54,424,576	3	22,743
	PNMT	17	35,078,033	35,080,254	3	3,093
	SLC6A4	17	25,547,506	25,587,080	5	9,879
	ADRA1D	20	4,149,278	4,177,659	11	2,359
	SNAP25	20	10,147,477	10,236,065	16	6,019
	COMT	22	18,309,263	18,337,496	5	6,396

Table 2 .

 2 Top case-only interaction results (p-value < 0.005 and OR > 2)

		Locus 1			Locus 2		Case-only test		Regression test
	gene	marker	chr:pos	gene	marker	chr:pos	Odds Ratio (95% C.I.)	p-value (1)	Odds Ratio	p-value	full model pseudo-R 2 (3)
			5:159,324,38				4.56 (2.10-
	ADRA1B	rs11953285	9	DBH	rs1611115	9:135,490,336	20.02)	0.000054	4.83	0.0004	0.018
							2.41 (1.46-
	SLC6A3	Intron8 VNTR 5:1,464,855	SNAP25	rs362562	20:10,191,186	4.23)	0.000558	1.60	0.0871	0.008
			5:159,302,00				2.43 (1.50-
	ADRA1B	rs7737796	7	SNAP25	rs363006	20:10,228,083	4.37)	0.000711	2.27	0.0102	0.016
							3.64 (1.61-
	ADRA1A	rs472865	8:26,754,388 DRD4	exon 3 VNTR	11:629,989	14.41	0.00100	3.80	0.0048	0.015
			5:159,281,02				2.18 (1.38-
	ADRA1B	rs6884105	1	SNAP25	rs363006	20:10,228,083	3.85)	0.00104	2.13	0.0215	0.008
					120 bp		2.46 (1.42-
	ADRA1A	rs486354	8:26,766,207 DRD4	duplication	11:625,946	5.21)	0.00134	2.66	0.0111	0.012
			9:135,513,49			10:118,989,92
	DBH	rs129882	0	SLC18A2	rs2283135 (2)	2	7.27 (N/A)	0.00178	9.45	0.0037	0.016
							2.47 (1.38-
	DRD4	rs12720373	11:626,688	CES1	rs3815589	16:54,384,420	5.36)	0.00204	2.21	0.0222	0.015
	DRD5	D4S2928 (2)	4:10,612,311 DRD4	exon 3 VNTR	11:629,989	5.84 (N/A)	0.00250	3.66	0.0664	0.007
			16:54,384,42				2.03 (1.23-
	CES1	rs3815589	0	ADRA1D	rs4815670	20:4,164,864	3.47)	0.00314	2.03	0.0252	0.010
							2.33 (1.36-
	ADRA1A	rs472865	8:26,754,388 DBH	rs2797849	9:135,491,762	4.71)	0.00328	2.28	0.0220	0.011
			5:159,299,85				2.67 (1.41-
	ADRA1B	rs13162302	3	ADRA1D	rs6052456	20:4,173,573	6.64)	0.00410	3.82	0.0010	0.021
							2.60 (1.38-
	DRD5	D4S2928 (2)	4:10,612,311 COMT	rs4680	22:18,331,271	5.27)	0.00416	2.04	0.0904	0.004
							2.06 (1.23-
	HTR1B	rs1228814	6:78,230,539 SLC6A4	rs1872924	17:25,570,472	4.09)	0.00456	3.24	0.0068	0.016
			10:119,026,6				3.38 (1.59-
	SLC18A2	rs363279	15	SLC6A2	rs9930182	16:54,298,798	13.27)	0.00474	4.15	0.0033	0.017
	Legend:
	VNTR = Variable Number of Tandem Repeats polymorphism; chr:pos = chromosome:

Acknowledgements

The authors gratefully acknowledge the Health Research Board, Ireland for salaries and project funding (RS, ZH).

Conflict of Interest The authors declare no conflict of interest. Supplementary information is available at the European Journal of Human Genetic's website.


Dr Andreas Walter Kuss
email: kuss_a@molgen.mpg.de.

Lars Riff Jensen

Wei Chen

Bettina Moser

Bettina Lipkowitz

Luciana Musante

Andreas Tzschach

Hans-Hilger Hilger Ropers

Christopher Schroeder

Ilaria Meloni

Vera M Kalscheuer

Martine Raynaud

Hilde Van Esch

Jamel Chelly

Arjan P M De Brouwer

Anna Hackett

Sigrun Van Der Haar

Wolfram Henn

Jozef Gecz

Olaf Riess

Michael Bonin

Richard Reinhardt

Hybridization-based resequencing of 17 X-linked intellectual disability genes in 135 patients reveals novel mutations in ATRX, SLC6A8 and PQBP1

Keywords: X-linked intellectual disability, X-linked mental retardation, array based resequencing, mutation analysis, automated PCR

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Mental retardation/Intellectual disability (ID) has a prevalence of about 2% in the general population 1 . Therefore, as well as because of the life-long restrictions for the patients and their families in combination with extremely limited therapeutic options, ID ranges among the top health issues of our time. Significantly more males compared to females are affected, which can be partly accounted for by mutations in genes on the X chromosome. Moderate to severe forms of X-linked ID (XLID) affect 0.05% 2 of the population in developed countries. The majority of the patients show non-syndromic forms of XLID (NS-XLID), where ID is the only clinically consistent manifestation.

On the genetic level NS-XLID is highly heterogeneous and the causative mutations known to date were found in more than 37 genes, each gene accounting for only a few percent of the cases 1, [START_REF] Gécz | The genetic landscape of intellectual disability arising from chromosome X[END_REF] . Families with several affected members from different generations are suitable for linkage analysis, and such families have therefore been instrumental in finding many of the presently known mutations that cause NS-XLID.

To identify the underlying genetic defect in small ID families or in sporadic patients, however, a different approach is required. Thus, in order to investigate the genetic basis of ID in 135 small families from the cohort of the EURO-MRX consortium (www.euromrx.com), we have designed a resequencing array based on the Affymetrix 50k platform, containing the coding and splice site regions of 17 XLID genes (see below) with a relatively high mutation frequency in NS-XLID patients. Application of this array led to the identification of 8 previously unknown changes in these genes.

Materials and methods

Patients and Controls

DNA from 135 unrelated ID patients without a molecular diagnosis were collected through the European MRX Consortium. The majority of patients were from families with no more than two affected brothers. After obtaining written informed consent from the probands or their parents DNA was extracted from patient blood or blood derived lymphoblastoid cell lines using standard methods. The study was approved by the relevant institutional review board.

Resequencing array

A custom resequencing array was designed based on the Affymetrix 50k platform (Affymetrix, Santa Clara, CA), allowing up to 50 kb of DNA to be sequenced in both orientations in a single hybridisation experiment.

The oligonucleotides on the array contain sequences that include the coding regions and splices sites (10 nucleotides flanking each exon) of ACSL4, ARX, ATRX, DLG3, FTSJ1, GDI1, IL1RAPL1, JARID1C, MECP2, NLGN4, PAK3, PHF6, PHF8, PQBP1, SLC6A8, TM4SF2 and ZNF41.

PCR amplification of DNA, purification and hybridisation

A total of 151 PCR amplicons were produced from genomic DNA (Supplementary material) and subsequently purified by alcohol. The purified DNA fragments were pooled in equimolar quantities (0.055 pmoles of each PCR product i.e. 36.3 ng pr Fragmentation of the products, labelling of the products with biotin, hybridisation, washing and scanning procedures were carried out according to the CustomSeq Resequencing Array Protocol version 2.0, Affymetrix.

Data analysis

The raw sequencing data were analysed using the Affymetrix GeneChip Analysis Software (GSEQ) version 4.0 (Affymetrix, Santa Clara, CA). Only nucleotides that were called on both DNA strands using a quality score threshold equal to 0 were considered. As we only used DNA from male patients (hemizygous for Xchromosomal genes) we did not consider heterozygous base calls. The entire dataset was also analysed using SeqC (JSI medical systems, Kippenheim, Germany) as previously described [START_REF] Schroeder | High-throughput resequencing in the diagnosis of BRCA 1/2 mutations using oligonucleotide resequencing microarrays[END_REF] .

RT-PCR and measurement of ACSL4 and SLC6A8 activity

RT-PCR was performed according to standard protocols. Enzymatic activity of ACSL4 and the urinary creatine/creatinine ratio were measured as described previously [START_REF] Longo | A third MRX family (MRX68) is the result of mutation in the long chain fatty acid-CoA ligase 4 (FACL4) gene: proposal of a rapid enzymatic assay for screening mentally retarded patients[END_REF][START_REF] Almeida | Creatine and guanidinoacetate: diagnostic markers for inborn errors in creatine biosynthesis and transport[END_REF] .

Protein extraction and Western blot analysis

Cells from control and patient lymphoblastoid cell lines were lysed in buffer containing 48% urea, 15 mM Tris pH 7.5, 8,7% glycerol, 1% SDS, 0,004% bromophenol blue, and 143 mM β-mercaptoethanol. Western blot analysis was performed as previously described [START_REF] Musante | Common pathological mutations in PQBP1 induce nonsense-mediated mRNA decay and enhance exclusion of the mutant exon[END_REF] .

Results and discussion

Using a combination of automated and manual PCR we produced 151 amplicons, containing the coding regions and splice sites of 17 known XLID genes from each of 135 ID patient DNAs (Supplementary Table 1).

As all PCR products had the expected size we could exclude the presence of larger deletions or insertions in these 17 genes. Small in-dels cannot be detected due to the nature of hybridisation-based sequencing [START_REF] Hartmann | Validation of microarray-based resequencing of 93 worldwide mitochondrial genomes[END_REF][START_REF] Fokstuen | A DNA resequencing array for pathogenic mutation detection in hypertrophic cardiomyopathy[END_REF] , but since in-dels in the 17 investigated genes are much less frequent than missense mutations (Human Mutation Database) it is possible, but not very likely that undetected in-dels are present in these 17 genes in the analysed patient cohort.

In addition, we sequenced the amplicons containing the tri-nucleotide repeats in ARX by Sanger sequencing to rule out mutations in this region as repeat expansions are also not detectable by array based resequencing.

The remaining PCR products were submitted to fragmentation and labelling. After hybridisation, washing and scanning of the arrays we obtained call rates between 90 and 96% and found 478 different nucleotide changes (excluding known SNPs). The specified call accuracy for this array type is 99.99% (1 error in 10.000 nucleotide calls) when -like in this study-no heterozygous base calls are expected. Thus 4-5 falsely called nucleotides per patient DNA (600 in 135 patients) had to be expected.

As a filtering criterion to remove false positive sequence changes from the dataset we used the call rate among the 5 immediately adjacent bases on either side of a given base. Assuming that fragments with poor sequence quality harboured a substantial amount of falsely called bases, we focused on sequence changes for which the 10 surrounding nucleotides contained at least 6 positions with a base call. This threshold reduced the number of putative nucleotide changes from 478 to 75. The amplicons containing these changes were then investigated by Sanger sequencing, which led to the identification of 10 verifiable nucleotide substitutions in 9 families. All of these sequence changes were found in fragments where at least 8 called bases were present in the 10 surrounding nucleotide positions.

To test the validity of the filtering procedure we applied SeqC, a module of the Sequence Pilot software (JSI medical systems, Kippenheim, Germany), for an independent analysis of the whole dataset and identified one additional change in the intron of ACSL4. This change was not detected with the previously applied filtering procedure because it is located close to the end of an amplicon and therefore lacked the mandatory 5 nucleotides on one side.

For each verified nucleotide alteration we then sequenced DNA from all available family members to investigate co-segregation with the disorder. Furthermore, we analysed DNA from 138 healthy males to exclude common polymorphisms. This ruled out the change in PHF8 c.441T>G, which was found once in the control panel and the change in NLGN4X c.968A>G, which did not segregate with the disorder in the family. These two changes are therefore unlikely to be pathologically relevant.

The remaining 8 potentially disease causing changes are listed in Table 2.

For the silent change in JARID1C and the intronic change in ACSL4 RT-PCR experiments showed normal levels of gene expression and a normal splicing pattern in patient lymphoblastoid cell lines (not shown), indicating that these changes are probably benign. Also in case of the missense change in ACSL4 (c.1382C>T, p.461P>L), the functional integrity of the gene product seemed uncompromised, as enzymatic activity of ACSL4 in a lymphoblastoid cell line from affected individual was normal. In addition to the intronic change in ACSL4 we also found an intronic change in PQBP1 in the same patient DNA (P122). The intronic change (c.586+25G>A) in the main known PQBP1 transcript (NM_005710.2) did not alter the PQBP1 splicing pattern as investigated by RT-PCR. However, the change also introduces a missense change (p.201G>D) in one PQBP1 isoform (AJ973600.1). This isoform is very weakly expressed and further studies are required to determine whether this change is pathogenic or not.

The patient in whom we found a p.1761M>T missense change in ATRX was previously considered to have a non-syndromic phenotype. As our finding indicated that he was actually suffering from ATRX syndrome we initiated a clinical reexamination. This revealed the presence of characteristic dysmorphic features providing strong evidence for the pathological relevance of this novel mutation.

One change in SLC6A8 (c.1169C>T, p.390P>L) is identical to a change previously reported in a family with ID 10 and later shown to be pathogenic [START_REF] Rosenberg | Functional characterization of missense variants in the creatine transporter gene (SLC6A8): improved diagnostic application[END_REF] .

The second change (c.1430C>T, p.477S>L) in SLC6A8 was not observed before and investigation of the urine creatine/creatinine ratio in the index patient revealed an abnormally elevated value of 1.01 (normal levels in adults: < 0.25) confirming the molecular finding.

Interestingly, we also identified a novel nonsense mutation (c.463C>T) in exon 4 of PQBP1, which is frequently mutated in ID patients [START_REF] Kalscheuer | Mutations in the polyglutamine binding protein 1 gene cause X-linked mental retardation[END_REF] . The change results in the introduction of a premature stop codon ( p.155R>X). Western blot analysis demonstrated that truncated PQBP1 protein resulting from this mutation is present in the patient (Fig. 1), as already shown for other patients with similar PQBP1 mutations 7 , supporting its pathological relevance.

In summary, the discovery of 4 putative disease-causing mutations in 7 out of 135 XLID patients equals a proportion of about 3%, which is well within the expected range. Firstly because based on observations by the EURO-MRX consortium only approximately 40% of the male patients (i.e. here 54 out of 135) from small families can be assumed to carry a single gene defect on the X chromosome [START_REF] De Brouwer | Mutation frequencies of Xlinked mental retardation genes in families from the EuroMRX consortium[END_REF] and secondly because the frequencies of disease causing mutations in the selected genes are each below a few percent 1, [START_REF] Tarpey | A systematic, large-scale resequencing screen of X-chromosome coding exons in mental retardation[END_REF] . The fact that we found an excess of missense mutations as compared to protein truncating mutations is also in line with previous findings [START_REF] Tarpey | A systematic, large-scale resequencing screen of X-chromosome coding exons in mental retardation[END_REF] . The advantages of array based resequencing include the potential for automation of sample preparation, the speed with which base calling is performed and the comparatively low cost. Array based resequencing is therefore an option when large cohorts are analysed for sequence changes in defined DNA regions. Considerations for array design and the limitations of array based resequencing have been thoroughly discussed [START_REF] Kothiyal | An overview of custom array sequencing[END_REF] . However, alternatives to array based resequencing are now available in the form of next-generation sequencing methods, which utilise sequencing by synthesis. Recently, a protocol for enrichment and subsequent sequencing of DNA fragments that contain the desired sequences has been published [START_REF] Hodges | Hybrid selection of discrete genomic intervals on custom-designed microarrays for massively parallel sequencing[END_REF] . This or similar approaches can reduce the time involved in the sequencing procedure considerably and will provide new options for elucidating the etiology of ID in the so far unresolved cases.

  Figure 1

Table 1 .

 1 Genes represented on the custom-made resequencing array Number of amplicons required to cover the coding region and splice sites of the respective genes

	Symbol	RefSeq	OMIM# Gene name

Table 2 .

 2 DNA changes found in MR patients but not in dbSNP or in controls (n=138) The change c.1169C>T in SLC6A8 was reported byRosenberg et al., 2004. Am J Hum Genet 75:97-105.

			Nucleotide	Amino acid			Pathologically
	Gene	Ref sequence	change	change	Family	Cosegregation	relevance
	ACSL4	NM_022977.2	c.1382C>T	p.461P>L	A100	Cosegregating	Benign
	ACSL4	NM_022977.2	c.1855+11A>G		P122	No DNA available	Benign
	ATRX	NM_000489.3	c.5282T>C	p.1761M>T	D020	Cosegregating	Causative
	JARID1C NM_004187.2	c.1827C>T	p.609Y>Y	T056	No DNA available	Benign
	PQBP1 NM_005710.2	c.463C>T	p.155R>X	N143	Cosegregating	Causative
	PQBP1 NM_005710.2	c.586+25G>A		P122	No DNA available	Probably benign
	SLC6A8 NM_005629.3	c.1169C>T	p.390P>L	D077	Cosegregating	Causative a
	SLC6A8 NM_005629.3	c.1430C>T	p.477S>L	L088	Cosegregating	Causative
	a

Acknowledgement

We thank Sven Poths for technical assistance. The study was supported by the MAX-Planck Innovation Fund. Additional funding was provided to HHR by the German Federal Ministry of Education and Research (MRNET 01GS08161-2).

Databases

UCSC Genome Browser: http://genome.ucsc.edu/cgi-bin/hgTracks?org=human

The Human Gene Mutation Database: http://www.hgmd.cf.ac.uk/ac/validate.php Ensembl: http://www.ensembl.org/index.html Euro-MRX Consortium: http://www.euromrx.com

Competing Interest: None declared.


Lorraine Southam

Kalliope Panoutsopoulou

N William Rayner

Kay Chapman

Caroline Durrant

Teresa Ferreira

Nigel Arden

Andrew Carr

Panos Deloukas

Michael Doherty

John Loughlin

Andrew Mccaskie

William Er Ollier

Stuart Ralston

Timothy D Spector

Ana M Valdes

Gillian A Wallis

Mark Wilkinson

Jonathan Marchini

Dr Eleftheria Zeggini
email: eleftheria@sanger.ac.uk.

The effect of genome-wide association scan quality control on imputation outcome for common variants

Keywords: genome-wide association study, imputation, quality control, single nucleotide polymorphism

Imputation is an extremely valuable tool in conducting and synthesising genome-wide association studies (GWAS). Directly-typed SNP quality control is thought to affect imputation quality. It is therefore common practice to use quality-controlled (QCed) data as input for imputing genotypes. This study aims to determine the effect of commonlyapplied QC steps on imputation outcomes. We performed several iterations of imputing SNPs across chromosome 22 in a dataset consisting of 3,177 samples with Illumina 610k GWAS data, applying different QC steps each time. The imputed genotypes were compared to the directly-typed genotypes. In addition, we investigated the correlation between alternatively QCed data. We also applied a series of post-imputation QC steps balancing elimination of poorly-imputed SNPs and information loss. We found that the difference between the unQCed data and the fully-QCed data on imputation outcome was minimal. Our study shows that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome. A minority of common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency. These findings may not generalize to the imputation of low frequency and rare variants.

Introduction

Genome-wide association scans (GWAS) have proven to be a successful strategy for detecting common variants exerting modest effects on complex disease risk. Currently available commercial platforms focus on common variants and capture the majority of HapMap (1) SNPs with minor allele frequency (MAF) >0.05 in European populations [START_REF] Barrett | Evaluating coverage of genome-wide association studies[END_REF].

Several large-scale consortia have been formed in order to carry out GWAS meta-analyses for various phenotypes, with successful outcome (for example [START_REF] Zeggini | Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes[END_REF][START_REF] Prokopenko | Variants in MTNR1B influence fasting glucose levels[END_REF][START_REF] Franke | Replication of signals from recent studies of Crohn's disease identifies previously unknown disease loci for ulcerative colitis[END_REF][START_REF] Barret | Genome-wide association study and metaanalysis find that over 40 loci affect risk of type 1 diabetes[END_REF][START_REF] Soranzo | A genome-wide meta-analysis identifies 22 loci associated with eight hematological parameters in the HaemGen consortium[END_REF]. To enable the combination of data across studies carried out on different platforms, and to enable in silico fine mapping of association signals, imputation approaches were proposed a few years ago [START_REF] Marchini | A new multipoint method for genome-wide association studies via imputation of genotypes[END_REF] as a means of statistically inferring genotypes at untyped loci using a reference set, for example the HapMap (~2,500,000 SNPs).

An important aspect of any GWAS analysis is the implementation of a series of rigorous quality control (QC) steps prior to testing for association. These QC procedures help guard against genotyping error, population stratification, sample duplication and other confounders that can affect the analysis results. QC steps are typically applied at the sample-and SNP-specific level. Sample-level QC includes filtering out samples with low call rates, evidence for different ethnic origin, high heterozygosity, relatedness/ duplication, gender discrepancies and genotyping batch effects. SNP-level QC includes filtering out SNPs with low call rates and deviation from Hardy-Weinberg equilibrium (HWE) at pre-determined thresholds. It is generally believed that datasets should be stringently QCed at the marker level before applying imputation approaches. For this reason, lower MAF SNPs tend to also be excluded, as their accuracy can be hampered by poor clustering properties and incorrect automated genotype calling (at least with currently widely-used algorithms). Even though such weight is placed on pre-imputation SNP QC, the effects of applying different criteria and thresholds to the starting dataset have not been investigated thus far. In this report, we evaluate the effect of GWAS QC on imputation outcome, and find that imputation works very well for common variants irrespective of QC and that a minority of some common-frequency SNPs with particular properties cannot be accurately imputed regardless of QC stringency.

Materials and Methods

We used an empirical GWAS dataset to assess the effect of QC on imputation outcome. We focused on chromosome 22, (n=9,038 directly-typed SNPs) from 3,177 osteoarthritis (OA) cases from the UK, typed on the Illumina 610k quad chip as part of the arcOGEN consortium GWAS (manuscript submitted). Chromosome 22 is representative of the genome in terms of the proportion of directly-typed to imputed SNPs. All samples included in our analysis had passed standard sample-level QC (based on call rate, heterozygosity, relatedness, ethnicity and gender discrepancies). We imputed genotypes at variants on the basis of HapMap phase II release 22 CEU data (n=33,815 SNPs on chr22) using IMPUTE v1 [START_REF] Marchini | A new multipoint method for genome-wide association studies via imputation of genotypes[END_REF]. We performed each imputation in duplicate, with and without the IMPUTE v1 -pgs (predict genotyped SNPs) flag, which resulted in one set of imputed data containing the original genotypes and in the other imputed genotypes. To assess the effect of varying levels of QC, we carried out several rounds of imputation, using differently QCed OA SNP data as the starting point.

Initially, we imputed on the basis of no SNP-level QC, including all directly-typed SNPs, regardless of MAF, call rate and HWE. We also imputed on the basis of only those SNPs that passed stringent QC thresholds (call rate>95% for SNPs with a MAF≥5% and call rate>99% for SNPs with a MAF<5%, HWE exact p>0.0001, MAF >0.01 and removing all SNPs with GC or TA alleles) (Table 1). Although imputation biases can occur due to poorlyclustering SNPs with miscalled genotypes in the starting dataset, cluster plot checking is not feasible at the genome-wide scale and therefore it is not implemented in standard GWAS QC.

We evaluated the accuracy of imputed genotypes by comparing allele frequencies at the same SNP between imputed and true, directly typed data. For each QC-imputation iteration, we performed an allele frequency comparison between the actual directly-typed and imputed SNPs. Under perfect imputation, we would expect to see alignment with the null hypothesis of no association. We used SNPTEST [START_REF]Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls[END_REF] to investigate differences between directly-typed and imputed genotypes at the same variants within the same samples, taking into account the distribution of genotype probabilities for each individual. For the purposes of our comparison, we used those SNPs that were directly genotyped in OA cases and also present in the HapMap reference samples. Table 1 summarizes the number of these SNPs for each QC threshold.

When comparing directly-typed with imputed allele frequencies at the same variant in the same individuals, we arbitrarily considered p<10 -6 as significantly different. We calculated the correlation between imputed and directly-typed MAF, using the expected counts to allow for genotype-associated probabilities. We also applied a series of post-imputation QC steps in order to eliminate unreliably imputed SNPs, aiming to filter out as many of these SNPs as possible whilst retaining a good proportion of non-significant SNPs. We compared two alternative methods for post imputation QC filtering, firstly the IMPUTEinfo score, which is associated with the imputed allele frequency estimate which ranges from 1, indicating high confidence, to 0 suggesting decreased confidence, and secondly the freq-add-proper-info score provided by SNPTEST, a relative statistical score ranging from 0 to 1, representing no information to complete information respectively. The SNPTEST freq-add-proper-info score has been shown to be highly correlated with the IMPUTE-info score under the additive model [START_REF] Marchini | Genotype imputation for genome-wide association studies[END_REF]. In both scenarios we also filtered out SNPs with MAF <5%. Figure 1 illustrates the effects of altering post-imputation QC filters on the QCed data. Based on these results we chose to use the IMPUTE-info score with a filtering threshold <0.8 and MAF <5% which effectively eliminated ~79% of the significant SNPs whilst retaining ~85% of the non-significant ones (SNPTEST freq-add-proper-info <0.9

and MAF 5% would be roughly equivalent to this eliminating ~73% of the significant SNPs whilst retaining ~89% of the non-significant ones). We applied this post-imputation filter to each of our datasets and compared the results. We looked at the unQCed and QCed datasets first, as synopsised in Table 1. For each scenario, we examined frequency differences between the directly-typed and the imputed genotypes as described above. In addition, we compared the imputed genotypes at imputed SNPs only for the unQCed and the fully QCed (QCed data with all poorly-clustering markers removed) strategies.

Results

Table 1 summarises the number of SNPs with significantly (p<10 -6 ) different allele frequencies between the directly-typed and imputed data in the same set of individuals for each of the different QC sets. Correlation plots and R 2 values for the comparisons of the QCed and unQCed datasets are presented in Figure 2. The difference between the unQCed (R 2 =0.993) and QCed data (R 2 =0.994) was minimal. After post-imputation filtering there were 77 SNPs with significantly different (imputed v. directly-typed) allele frequencies in the unQCed data compared with 67 significant SNPs in the QCed data. In an attempt to improve imputation for the small subset of poorly-imputed SNPs in the QCed data we excluded all SNPs with MAF<5% and, subsequently, also SNPs with MAF<10%. We found that eliminating these lower MAF SNPs prior to imputation had little effect overall.

The R 2 for the post-imputation QC filtered comparison with the QCed data was virtually identical both when excluding all SNPs with MAF<5% (R 2 = 0.994) and when excluding all SNPs with MAF<10% (R 2 = 0.991).

Given this apparent minimal influence of input data QC on imputation outcome, we investigated further the small set of SNPs displaying significant allele frequency differences for the presence of a common characteristic that could conceivably be used as a post-imputation filter. In order to rule out poor genotyping as the cause of these significant differences, we examined all cluster plots for the unfiltered significant SNPs (p<1x10 -6 , n=325). Fourteen poorly-clustering SNPs were removed and the data were reimputed. After post-imputation QC, 3 additional SNPs were not significant and 6 were less significant. We then inspected the cluster plots for 10 SNPs on either side of the 61 SNPs remaining significantly different to rule out poor imputation due to flanking SNP poor clustering properties. We examined the cluster plots for 1,008 SNPs and found that 36 of these were poor; these resided in the proximity of 35 of the significant SNPs. We subsequently removed these SNPs and re-imputed. We found that following postimputation QC filtering, only 3 of the 61 SNPs were no longer significant and the R 2 remained the same as for the QCed data (R 2 = 0.994) for the post-imputation QC filtered data. When we repeated comparisons using IMPUTE v2 with the HapMap3 (CEU, release #2 Feb 2009) and data from the 1,000 genomes project (Pilot 1 genotypes released Mar 2010; phased haplotypes released Jun 2010) as the reference panels, we observed qualitatively similar results. Differences in region-specific recombination rates may account for the few remaining significant SNPs, as variants in areas of especially high recombination rate may be more challenging to impute accurately regardless of QC. To investigate this, we firstly examined the QCed unfiltered data and found that when the data were dichotomized into those markers with lower (<1cm/Mb) and higher (≥1cM/Mb) recombination rates there were more significant SNPs present in the higher recombination rate group compared to the lower recombination group (p=1.85x10 -27 , average recombination rates of 12.8 and 3.04 respectively). When we examined the QCed data post-imputation QC, this difference disappeared (p=0.526). This clearly indicates that application of the post-imputation QC filter successfully identifies the majority of significant SNPs with high recombination rates.

Therefore, to include recombination rate as an extra filter would not be prudent, for example using the QCed post-imputation QC filtered data and applying a further filter using a recombination rate threshold of >1cM/Mb would eliminate 2,075 SNPs, only 24 of which are significantly different.

Discussion

The imputation accuracy of common variants does not appear to be substantially affected by GWAS QC steps. Our data demonstrate that there is little difference in imputation accuracy observed in unQCed GWAS data when compared with QCed GWAS data. Furthermore, the implementation of additional QC steps (e.g. filtering out variants with MAF<0.05 and <0.10) does not considerably improve overall imputation accuracy. Missing variants and directly typed variants that fail pre-imputation QC checks are imputed and these data are used for downstream analyses. Post-imputation QC successfully eliminates a good proportion of inaccurately-imputed SNPs. Specifically, by applying a very stringent post-imputation QC threshold a smaller set of variants with more accurately predicted genotypes remain. The IMPUTE-info threshold of <0.8 and MAF≤5% criterion successfully filtered out the majority of poorly-imputed SNPs. However, the application of these strict filters in GWAS data could result in many SNPs being excluded from the data and thus potential true association signals could be missed. Some of the inaccurately-imputed variants were due to poor clustering properties. It is plausible that the handful of variants that still remained inaccurately imputed could be due to differences in ethnicity between our data and the HapMap CEU reference panel from which the genotypes were predicted.

We have used IMPUTE, but do not expect our results and conclusions to qualitatively differ with different imputation methods, for example BEAGLE and MACH exhibit similar imputation accuracy to IMPUTE [START_REF] Nothnagel | A comprehensive evaluation of SNP genotype imputation[END_REF]. Differences in population structure between the reference panel and target dataset can be a source of imputation inaccuracy. Imputation accuracy for common SNPs may be further increased by using larger reference panels with data on denser sets of variants. Our results show that GWAS QC is not of paramount importance for the imputation of common variants. This may be different for the imputation of low frequency and rare variants based on emerging reference panels such as the 1000 genomes (www.1000genomes.org) and UK10k (wwww.uk10k.org) projects. In summary, our study demonstrates that imputation of common variants is generally very accurate and robust to GWAS QC, which is not a major factor affecting imputation outcome.

Titles and legends to figures Table 1.

arcOGEN data for chromosome 22 detailing the different pre-imputation QC steps. A breakdown of the SNP number for each QC threshold is indicated both with and without the post-imputation QC. A. Imputation results for the QCed data indicating the total number of SNPs filtered for different QC thresholds using the IMPUTE-info and freq-add-proper-info scores. The SNPs remaining after the filter (red bar) have been subdivided into SNPs that are significant (green bar) and not significant (yellow bar). B. The same data as percentage of significant and non significant SNPs removed for each threshold. Both methods of filtering appear to be equivalent, but the freq_add_proper_info is shifted to the right for the same numerical threshold; we chose the impute-info <0.8 for further analysis (similar to a freq_add_proper_info <0.9).

Figure 1 .

 1 Figure 1.

Figure 2 .

 2 Figure 2. Correlation plots and the associated R 2 for (A) The unQCed and the QCed with and

Figure 1 .

 1 Figure 1.

Significant SNPs with poor cluster plots removed.

Those SNPs flanking the significant SNPs with poor cluster plots removed. NS, not significant (pш1x10 Ͳ6 ); S, significant SNPs (p<1x10 Ͳ6 )

Acknowledgements

EZ is supported by the Wellcome Trust (WT088885/Z/09/Z), LS is supported by the European Community Framework 7 large collaborative project grant TREAT-OA, KC is supported by a Botnar Fellowship and by the Wellcome Trust (WT079557MA), NWR is supported by the Wellcome Trust (WT079557MA), JMW is supported by the Higher Education Funding Council for England. JL receives support from the UK NIHR Biomedical Research Centre for Ageing and Age-related disease award to the Newcastle upon Tyne Hospitals NHS Foundation Trust. The arcOGEN consortium is funded by a special purpose grant from Arthritis Research UK (grant 18030).

Conflict of Interest Statement

The authors declare no conflict of interest.