Severe delayed posttonsillectomy haemorrhage due to a pseudoaneurysm of the lingual artery

A 3.5-year-old child is presented with severe, recurrent haemorrhages after a tonsillectomy. The haemorrhages were caused by a pseudoaneurysm of the lingual artery, which was visualised more than 2 weeks postoperatively by angiography. The pseudoaneurysm was successfully embolised by coils. Coiling is an adequate therapeutic option for severe posttonsillectomy haemorrhage due to arterial damage.
Introduction
Postoperative haemorrhage is the most common serious complication of adenotonsillectomy and its incidence is around 3% [1]. Intraoperative bleeding (<24 h) may be related to the surgical technique or a bleeding diathesis. Delayed postoperative haemorrhage (>24 h) predominantly occurs on the fifth to seventh-day postoperatively when the slough separates from the granulating fossa. The most critical posttonsillectomy haemorrhages are due to arterial dissections and aneurysms. In this report we present a boy with a severe, delayed posttonsillectomy haemorrhage due to a pseudoaneurysm of the lingual artery.
Case report
An otherwise healthy 3.5-year-old boy was brought in with severe oral bleeding. Nine days prior to the presentation he had undergone an adenotonsillectomy for chronic adenotonsillitis in another hospital. The tonsils had been removed with cold dissection and sutures had been placed in both tonsil areas. The procedure and the immediate postoperative period went uncomplicated. On the fifth postoperative day he had been admitted for a one-night observation because of haemorrhage from the mouth. During the admission no active bleeding was observed and his haemoglobin level of 4.9 mmol/l (normal values: 6.5–8.4 mmol/l) was treated conservatively.
The second postoperative bleeding started 9 days postoperatively, late at night when the patient was lying in bed. There were no provoking events. The bleeding lasted around 10 min but had already stopped at arrival in our emergency room. We saw a pale and restless boy. Intra-oral examination in general anaesthesia showed lacerated pharyngeal muscles in the left tonsillar fossa and a loose suture was removed. The right tonsillar fossa revealed diffuse bleeding from the pharyngeal muscles without pulsating masses. The nasopharynx and tongue base showed no abnormalities. The haemoglobin was increased from 2.9 to 8.2 mmol/l by transfusion with two units of packed red blood cells. The coagulation values were within the normal range. The postoperative period was uncomplicated, so he was discharged after 5 days, 15 days after the primary intervention, with dietary advices.
One day later the boy arrived by ambulance with heavy bleeding from the mouth. He was pale, shivering and drowsy. Again, the severe bleeding started without a provoking moment and persisted for about 15 min. Immediately, after arrival at our emergency department, he was re-explored intra-orally under general anaesthetics. We saw a bluish, pulsating mass of 5 by 12 mm low in the right tonsillar fossa without active bleeding. The faucial pillars on the right side were approximated without tearing the mucosa or induration of the aneurysm. However, during extubation a massive bleeding occurred. The boy was re-intubated, and with digital oral pressure during 15 min haemostasis was achieved.
The patient was stabilised and an arteriography was performed. A pseudoaneurysm of the right lingual artery was visualized with selective catheterisation using a vertebral catheter (Fig. 1). The pseudoaneurysm was coiled distally and proximally using a microcatheter (Excelsior 10, Boston scientific, Natick, MA, USA) and microcoils (GDC 10, Boston Scientific, Natick, MA, USA). The close relation of the proximal part of the pseudoaneurysm to the entrance of the facial artery made coiling of the facial artery inevitable. The pseudoaneurysm was coiled successfully and no bleeding focus was seen on the control arteriography (Figs. 2, 3). During the procedures blood transfusion was needed to restore the low haemoglobin level.
Fig. 1Pseudoaneurysm of the right lingual artery seen from right-anterior side (3D-surface rendering)Fig. 2Arteriography of pseudoaneursym of the right lingual artery seen from right-anterior sideFig. 3After coiling of the pseudoaneurysm, including the facial artery; compare to Fig. 2
The boy was extubated without any bleeding and 4 days after the embolisation he was discharged in a good clinical condition. At a follow-up examination, he did not report any intra-oral or neurological abnormalities.
Discussion
True aneurysms develop through congenital or acquired weakness of all three layers of the vascular wall. False or pseudoaneurysms may arise due to localised arterial wall laceration caused by blunt or penetrating trauma. The intima or adventitia layer of the vessel wall is dissected, which creates a periarterial haematoma. Pseudoaneurysms after tonsillectomy may be triggered by blunt or direct trauma during dissection or due to placing ligation sutures.
Haemorrhages from posttonsillectomy pseudoaneurysms of the lingual, facial and internal carotid artery have been observed within 5 h postoperatively to the eighth postoperative day in a few cases [2, 3]. In those reports no intra-oral pulsating masses were observed. The pseudoaneurysms were shown with angiography and treated with embolisation. Mitchell et al. reported a case of fulminant bleeding during the tonsillectomy procedure from an aberrant lingual artery with a pseudoaneurysm, which was treated with embolisation [4]. A pulsating neck mass can also be the first symptom of a pseudoaneurysm, which has been seen in two cases without any bleeding [5, 6].
It is striking that the above-mentioned cases of posttonsillectomy pseudoaneurysms concerned children under the age of 10 years. No cases of posttonsillectomy haemorrhage due to pseudoaneurysms in adults have been reported. This suggestive higher incidence of pseudoaneurysms in children might result from the smaller anatomy and thinner pharyngeal muscles, and a subsequent higher risk of damaging the large vessels.
Since the angiography of our patient did not show any aberrant courses of arteries, trauma to the artery during dissection or placing the suture in the tonsillar fossa could have caused the pseudoaneurysm in our patient. In the two first oral inspections no masses were observed, only after the third bleeding (16th day postoperatively) a pulsating mass was seen. This suggests a gradual development of the aneurysm. False aneurysms are known to expand when the periarterial clot dissolves and more blood flows into the periarterial space [5]. The size of the lesion to the vessel wall might determine the amount of blood and whether the pseudoaneurysm is large enough to be observed. Maybe the vessel lesion in our patient was small, which made the pseudoaneurysm grow slowly. Although no real provoking events seem to have initiated the bleedings, lying down in bed might have raised the blood pressure in the carotid artery, which made the aneurysm leak. A new clot made the severe short lasting bleeding stop.
Severe posttonsillectomy haemorrhage is treated with external ligation of the internal or external carotid artery or endovascular coiling of the injured artery. Although a randomized controlled trial has not been performed, embolisation seems to have advantages over external ligation [7]. Firstly, the diagnostic evaluation can be combined with direct therapeutic intervention. Furthermore, coiling is more selective. This is especially convenient in posttonsillectomy haemorrhages, where several branches of the external or internal carotid artery could be damaged. The third advantage is that embolisation is less mutilating and has less risk of damaging the vagal and accessory nerves. The potential complications of embolisation are perforation of the vessel with leakage of coils, ischemia of mucosa or nerves, or vasospasm. External ligation seems to be the only quick option in massive bleeding and unstable patients.
Conclusion
Pseudoaneurysms are life-threatening and should be considered in severe posttonsillectomy haemorrhage. Pseudoaneurysms do not always present as intra-oral or cervical pulsating masses, and can develop gradually. If the technique and technical crew are available, interventional arteriography is strongly recommended in severe posttonsillectomy haemorrhage. It is diagnostic and therapeutic at the same time and more selective than surgical ligation.

posttonsillectomy haemorrhage
pseudoaneurysm
lingual artery
embolisation


How do Consumers Search for and Appraise Information on Medicines on the Internet? A Qualitative Study Using Focus Groups

Background Many consumers use the Internet to find information about their medicines. It is widely acknowledged that health information on the Internet is of variable quality and therefore the search and appraisal skills of consumers are important for selecting and assessing this information. The way consumers choose and evaluate information on medicines on the Internet is important because it has been shown that written information on medicines can influence consumer attitudes to and use of medicines.
Introduction
Consumers frequently use the Internet as an information source and it has been reported that 80% of adult Internet-users have accessed it for general health information [1]. More specifically, 36% of Internet-using consumers have used the Internet as a source of information on medicines [1].
It is broadly acknowledged that health information on the Internet is of variable quality as evidenced by the large number of studies that have explored the quality of consumer health information on the Internet [2]. This is to be expected because the Internet is a free medium. It has also been widely postulated that consumers searching for health information are in danger of being harmed by poor-quality information even though there is little evidence of this [3]. A consumer's risk for encountering poor-quality health information is purportedly related to the proportion of poor-quality information on the Internet and the consumer's ability to filter out this information [2]. As the quality of information on the Internet cannot be controlled, the more-imperative issue is the ability of consumers to search through information and assess its quality so they are able to avoid untrustworthy information [4]. An Australian study suggested that consumers found it difficult to describe how they distinguished good-quality information on medicines from poor-quality information on medicines on the Internet [5]. However, this study was limited by a small (N = 9), select sample and did not explore in-depth the way consumers searched for and selected information on medicines.
There is little information concerning consumer Internet-search behavior for health information. One study reported that participants mainly select Web sites that looked and read professionally and preferred understandable Web sites from official sources that used scientific references [6]. When participants were observed while searching for health information on the Internet, it was found that they mainly used search engines and were described as having "suboptimal" search skills [6]. This study reported that participants did not find blatantly-incorrect health information in their searches [6]. This indicates that they had used selection criteria to decide on the Web sites, though the criteria were not fully described in this paper.
Consumer use of information on medicines is an important issue because written information on medicines has been shown to influence consumer attitudes towards their medicines, and affect their medicine-taking behavior [7]. Furthermore, medicines, unlike general health issues, have overtly-commercial imperatives, which may influence the information available. Since the Internet has become a common source of information on medicines, it is important to identify the way consumers are using it. Therefore the aim of this study was to explore consumer use of Internet-based information on medicines. In particular, the objectives were to:
examine consumer attitudes to the availability and quality of Internet-based information on medicines;
explore consumer reasons for using this information;
explore consumer experiences in searching for and appraising information on medicines;
investigate the self-reported impact and application of this information.
This paper will present results from the broader study on consumer experiences in searching for and appraising Internet-based information on medicines.
Methods
Selection of Method
Focus groups were selected to address the study aims because they are useful for time-efficient, in-depth exploration of issues surrounding topics where there is little information [8- 10]. Since there is little known about how consumers use Internet-based information on medicines, focus groups were an ideal method for exploring this issue. The results of focus groups are not intended to be statistically generalizable, but are used to reveal the range of consumer opinions and attitudes.
Research Instrument
An interview guide consisting of general themes constructed from the literature was prepared (Table 1). This paper focuses on results ensuing from the exploration of themes 4, 5, and 6. The interview guide was composed of open-ended questions that addressed various issues pertaining to consumer use of Internet-based information on medicines; the questioning route was designed to stimulate discussion [11- 13].
Table 1
Themes for focus group interview guide*
General opinions about the Internet as a source of information on medicines.
Experiences in using the Internet to seek information on medicines.
Reasons for seeking information on medicines.
The methods and process of searching for information on medicines.
Opinions and critique of the information found.
Experiences in the evaluation of the quality of Internet-based information on medicines.
Feelings after reading the information.
Actions taken as a result of reading the information.
Perceived benefits and drawbacks of the Internet as a source of information on medicines
* This paper focuses on results ensuing from the exploration of themes 4, 5, and 6.
The interview guide and questioning route was pretested with a convenience sample of consumers (N = 13) to test for interpretation, appropriateness, and comprehensiveness, and to establish face and content validity. No significant changes were made to the interview guide or questioning route as a consequence of this pretest.
Participant Recruitment
After approval was granted by the Human Research Ethics Committee of The University of Sydney, participants were enlisted for the focus groups by a recruitment agency. Participants were recruited from the agency's database of consumers across metropolitan Sydney, Australia via telephone using a screening questionnaire.
Consumers were deemed to be eligible for this study if they had sought Internet-based information on medicines in the preceding 12 months. This bounded reference period was applied to allow for a suitable recall of past events [14] while also allowing enough time for consumers to have used the Internet for this purpose. Inclusion criteria required that participants were 18 years of age or over, did not require a translator to take part in focus group discussions, did not have training as a health professional, and did not have specialist Internet training. Participants were financially reimbursed for their time and travel expenses.
Study Design
Six focus groups were conducted in a number of locations around metropolitan Sydney in March to May 2003.
To approximate a representative cross section of consumers, participants were recruited with the intention of including subjects from both genders and across different age groups. Focus groups were age stratified to achieve a level of homogeneity within each group. The use of stratification may increase congruency between participants, thereby allowing a more comfortable discussion [11,15]. Eight persons were recruited for each focus group to ensure that groups were large enough to motivate a discussion, yet small enough allow for all opinions to be heard [11]. The number of groups needed was not determined beforehand because data was collected until saturation occurred (the point where no new themes emerged) [10]. In this study, saturation occurred by the sixth focus group.
The focus groups were facilitated by a skilled moderator while 2 assistant moderators observed and took notes. The group discussions were 1 to 1.5 hours in duration and were digitally sound recorded after permission was obtained from all participants. The recordings were transcribed verbatim. Participants also completed a demographics questionnaire that collected data on Internet usage.
Data Analysis
The verbatim transcripts were entered into NVivo qualitative software [16] and thematically content analyzed using a grounded theory approach. The grounded theory approach is an inductive approach to analyzing qualitative data, where ideas and emerging themes are systematically coded to generate theory [17].
Results
This paper presents participants' responses to themes 4, 5, and 6 (Table 1). Responses to other themes are currently unpublished.
Demographics
Forty-six consumers participated in this study. The age of the participants ranged from 18 to 67 years, with a median of 41 years (interquartile range, 21 years) and a mean of 41.7 years (standard deviation, 12.7 years). Fifty-seven percent of the participants were female. The majority of the participants were employed full-time (58.7%) and about a fifth were either retired or full-time homemakers. Almost half the sample (47.8%) had occupations that could be classified as managers, professionals, or associate professionals [18]. A high proportion of the sample (65.2%) had completed further educational qualifications beyond high school, and 23.9% of the sample had a bachelors or postgraduate degree.
Data on participant usage of the Internet is presented in Table 2. The majority of participants had a few years experience in using the Internet and over half had accessed it from both their home and workplace. In addition to using the Internet for information on medicines, most participants also used it for general health information and for services such as e-mail.
Data on participant usage of the Internet for information on medicines is presented in Table 3. In addition to using the Internet, many participants also reported using other media such as magazines for information on medicines. This variety of information sources has also been seen in another Australian study on consumer use of Internet-based general health information [19]. Even though most participants (82.6%) were seeking information for themselves, many reported also searching for other family members. This was also reflected in the aforementioned Australian study that showed that 63% of Internet-using consumers sought health information mainly for themselves [19].
Table 3
Participant usage of the Internet for information on medicines (N = 46 participants)
Characteristic
Usage
Frequency, Number of Participants
Relative Frequency, (% of Participants)
Media sources of information on medicines (more than one category could be selected)
Internet Magazines Television Books Radio
46 31 23 22 9
100.0 67.4 50.0 47.8 19.6
Person that Internet medicine information was used for (more than one category could be selected)
Self Spouse/partner Child Parent Another relative Friend
38 24 19 17 13 6
82.6 52.2 41.3 37.0 28.3 13.0
Health categories for which information on medicines had been sought for (more than one category could be selected)
Allergies Arthritis/joint pain Asthma Cancer Skin disorders Hormones Other miscellaneous Child health Diabetes High cholesterol Immunization Pain and injury High blood pressure Mental health Digestion/stomach disorders Infections Migraine Osteoporosis Alzheimer's disease Dementia
20 14 14 13 12 11 11 10 10 9 9 9 8 8 6 6 6 6 5 5
43.5 30.4 30.4 28.3 26.1 23.9 23.9 21.7 21.7 19.6 19.6 19.6 17.4 17.4 13.0 13.0 13.0 13.0 10.9 10.9
Table 2
Participant usage of the Internet (N = 46 participants)
Characteristic
Usage
Frequency, Number of Participants
Relative Frequency, % of Participants
Length of experience in the use of the Internet
More than 5 years 4 to less than 5 years 3 to less than 4 years 2 to less than 3 years 1 to less than 2 years Less than 1 year
17 13 7 4 4 1
37.0 28.3 15.2 8.7 8.7 2.2
Location of Internet access
Home and work Home only Work only
25 16 5
54.3 34.8 10.9
Activities that the Internet is used for (more than one category could be selected)
Information on medicines E-mail Health information Travel information/booking Banking/financial services News, weather, sport Job or study related research Real estate Shopping—product research Games and hobbies Chat or instant messaging Shopping—purchasing Purchasing medicines
46 45 43 40 36 34 34 33 33 26 22 19 7
100.0 97.8 93.5 87.0 78.3 73.9 73.9 71.7 71.7 56.5 47.8 41.3 15.2
Searching for Internet-Based Information on Medicines
Search Engines
All participants had used a search engine to find information on medicines. Most participants had a single favorite search engine that they would always use, but a few reported using more than one search engine to find the information they required.
The choice of search engines was determined by many different factors ranging from the default search engine on their browser to active selection based on self-developed criteria. Numerous participants were influenced by the search engine that was used by coworkers, for example:
Group 4, Participant 8
I saw it on this guy's computer and . . . I thought 'Oh, I'm going to use this'. That's how I started it at work.
Some participants also reported that their browser automatically defaulted to a certain search engine and a few participants were unable to identify the search engine they used, for example:
Group 3, Participant 6
Couldn't tell you [the search engine] really. I just log on and use whatever comes on.
Many participants used search engines recommended by family and friends.
There were certain determinants that led some participants to actively choose a specific search engine. These included perceptions of the credibility of the search engine, ease of use, relation with services such as e-mail, and a lack of advertising. These determinants did not necessarily include perceived quality of the information on medicines obtained through their use.
A few participants reported using AltaVista [20] because they thought it had an educational advantage, for example:
Group 1, Participant 1
It's got an educational edge, that's my experience. When I was at university doing my second degree, that was one that was sort of promoted as credible I suppose.
Some participants preferred to use Ask Jeeves [21] because they could enter the searches in a question or statement format rather than using search terms.
Many participants reported using Yahoo! [22] because it appeared as a default homepage, was used as a personal e-mail account, or was advertised through other media. Yahoo! and Google [23] were also said to be useful for Australian-only searches.
Google was undoubtedly the search engine the majority of participants used most and preferred. This was especially true of the younger participants. The common perception was that Google appeared to be straightforward and did not focus on advertising, for example:
Group 4, Participant 6
It's just got less [rubbish]. It seems to be direct to what you want. I think that other [search engines] always have these categories and they always have suggestions for buying things and stuff like that but Google's pretty much straight to the point. It's simple.
Participants also commented that this search engine was useful for suggesting spelling corrections when errors were made, as medicine names were sometimes difficult to spell. A few participants reported preferring Google as their search engine of choice specifically for health-related searches but were unable to explain reasons for their preference.
Other search engines used by participants were metasearch engine Dogpile [24], Australian metasearch engine Search66 [25], Australian-based search engine Web Wombat [26], and ninemsn [27], the Australian-based access to search engine MSN Search [28]. Many participants who used metasearch engines were unaware of the difference between these and normal search engines.
Generally, although a variety of search engines were used by participants when seeking information on medicines, the majority of participants used the same few dominant search engines. Participants generally preferred search engines with less advertising, and would continue to use the same search engine if they were successful in their searches. Most participants used the same search engine that they used for nonhealth information, and were usually influenced by what was used by friends, family, and colleagues.
Search Processes
Participants displayed a large variation in the process of searching for information on medicines.
Most participants found information by typing the name of the medicine (drug name or brand name) into the search engine. A few participants felt this was the only way of finding information on a medicine, for example:
Interviewer
How do you put in your searches?
Group 6, Participant 2
Medicines are really specific to just the name.
Other participants reported looking for broader information, for example:
Group 1, Participant 2
I often use a more general [search]. I might use something like 'women's health' or something. And I like to see a whole range of things . . . rather than targeting specifically . . . and then I choose within that.
The information found through this type of search was said to be less specific to one medicine and had more general or comparative information.
Some participants used more-advanced search techniques such as quotation marks, phrases, and extra words to narrow down their searches. They displayed an understanding of how these techniques helped to focus their searches, for example:
Group 4, Participant 6
If you type it in with quotation marks, it'll search for those words together whereas if you type them separately, it'll just search for them anywhere.
Participants reporting advanced skills were generally observed to be those who were younger or those who had greater experience of the Internet through work or study.
However, it was clear that search skills varied significantly. The following interchange illustrates the mixed levels of understanding as to how search engines work:
Group 5, Participant 4
[You need to] ask a specific question . . . 'What are the side effects?' rather than typing in 'penicillin'.
Participant 6
Yeah, you really have to do a whole sentence. A whole statement.
Participant 3
I would type in 'penicillin side effects'.
Participant 4
'Then it could hit on 'penicillin' or it could hit on 'side effects'.
The uninformed way in which some participants agreed upon what they considered to be optimal search skills was obvious in the group discussions. The majority of participants in this study who reported searching using less-than-optimal techniques—such as typing in whole questions—tended to be nonworkers, for example, full-time homemakers or retirees.
The search skills of participants varied widely and these differences may affect the resulting information that participants encounter. Searching via a search engine however, was not the only way of finding information on the Internet on medicines.
Other Methods of Finding Internet-Based Information on Medicines
Some participants mentioned ways of finding information on medicines in addition to using search engines.
A few participants said that they guessed the Web sites of medicines by typing the name of the medicine in the address bar in the format of www.[brand name or drug name].com.
Several participants found information on medicines from Web sites recommended by family and friends, and from seeing advertisements in seniors' and health publications. Some reported bookmarking favorite Web sites for future reference and a few subscribed to mailing lists at health-related Web sites.
One participant described searching for information on medicines using online journals. Although aware that the information was not aimed at consumers, this participant still chose to use this means to search for pertinent information on medicines:
Group 2, Participant 2
I actually searched via . . . the professional journals . . . And I guess that was a little bit harder to do it that way because . . . reading through the journals was quite difficult. I tend to just go to the abstracts.
Participants reported using a variety of search skills to obtain information on medicines. However, the important issue was how they selected and appraised the information.
Appraising Internet-Based Information on Medicines
Selecting Internet-based Information on Medicines
Participants described different ways of choosing which Web site to visit when selecting from the numerous results obtained from using a search engine. Some worked down the list of results from the first one while others looked for keywords in the Web site descriptions or for the Web site's recency. Often participants made a judgment based on the URL (Web page address) of the result, for example:
Group 4, Participant 1
I actually like looking at the actual web address, just seeing how professional it is. Like if it's some silly thing, I won't bother going into it.
Many participants also reported looking for indicators in the Web site address to determine whether it belonged to a government, a university, an official organization, or a pharmaceutical company.
Even though most participants said they would not go beyond the first page of the search results, one expressed the opinion that the best information was in the middle of the results and not on the first few pages. This participant had the erroneous opinion that the first pages of results are older and that results appeared mainly in the order in which the information had been created.
Many participants reported looking for the country of origin of the information and preferred information generated from their country of residence, for example:
Group 4, Participant 2
If I'm searching for a medication . . . and it brought up some things and I noticed it was in Australia, I click on that.
These participants felt that Australian information would be more applicable to them and professed an awareness of health-setting issues such as differences in the brand names and availability of medicines in different countries. However, others had more confidence in United States-based information because they believed that this was where most new research was undertaken.
It was clear that most participants did not pay conscious attention to how they selected Internet-based information on medicines, with one referring to the process as "a vibe" that you obtain through experience. Another described this as a feeling that "things have a look of credibility." Similarly, many participants had trouble in articulating their selection process, for example:
Group 5, Participant 3
I find that sometimes I get to a site and I think 'Gee, this is a good site, but I don't know how I got there.' You know what I mean? You fluke it.
Despite the inability of many participants to express how they selected information on medicines, many were able to express what they would not select. Participants reported quickly rejecting sites that were slow to load, sites that contained too many graphics, and sites that had pop-up advertisements.
The process of selecting information on medicines varied among the participants. It appeared that all participants had their own criteria for selecting and rejecting information which may or may not appear logical to others. Credibility of the source, however, appeared to be a common determinant in the criteria of all participants.
Credibility of the Source of Internet-Based Information on Medicines
Participants expressed conflicting opinions about the credibility of the source of Internet-based information on medicines. Many participants regarded information produced by pharmaceutical companies to be the "official" information on a medicine and therefore trusted this the most, while many others were suspicious of a possible information bias, for example:
Group 1, Participant 7
If you're looking at [a pharmaceutical company website], they've got factories throughout the world, they're a pretty good company so . . . you know that they've done so much research it's credible information.
Group 1, Participant 6
If it's a pharmaceutical company, they're gonna put a good stance on their drug.
Many other participants preferred information that originated from what they considered to be impartial and reputable sources such as government, professional, or disease-focused organizations, or university Web sites. A few participants also reported looking for credentials such as the author's qualifications when assessing the credibility of the information provider.
A small number of participants preferred information written by other consumers who had personal experiences in taking the medicine. However, most participants expressed that they would be less likely to trust information on medicines generated by other consumers, for example:
Group 1, Participant 6
There are chat rooms . . . if you've ever been prescribed such and such a medication; you'll get people from all around the world . . .
Participant 2
Do you not find that a bit dangerous because everything is rather specific to each person's body?
Participant 6
Oh yeah, but it would be comparable to having a chat with some of your friends.
Some participants felt that the authorship of Internet-based information on medicines should be regulated and feared the reliability of the information because there was "no watchdog" for the information published on the Internet while others regarded it as analogous to the way they would trust information given in common conversation and therefore felt comfortable using information in this context.
The credibility of the source of information on medicines was a strong determinant in the selection process. However, in addition to the source participants evaluated information using criteria described in the next section.
Evaluating Internet-Based Information on Medicines
Participants evaluated information on medicines using criteria such as the motive for the information, the language used, and the applicability to their needs.
Almost all participants were skeptical to some degree of Internet-based information on medicines. Many participants professed a universal need for consumers to inherently distrust this information, and to interpret it accordingly. One participant stated that it is important to also consider why the information is on the Internet:
Group 1, Participant 6
What are the motives? Are they conflicting, credible? Whoever has posted it, are they trying to make a profit?
Other participants described the obviously difficult-to-believe nature of some of this information and looked for signs of conspiratorial or misleading language when deciding whether to trust the information, for example:
Group 2, Participant 8
If it says 'hazard free' and 'completely no side effects', for example, I'm more likely to disbelieve than believe that
In addition to this awareness of unreliable information on medicines, many participants also expressed an understanding that the information they find may not necessarily be applicable to them and that the information should not be used at face value, for example:
Group 2, Participant 7
The thing with medicines is there's no sort of right or wrong . . . Everyone's different, everyone's going to have a different reaction.
Group 2, Participant 6
When you ask the doctor, they tell you 'well, [the side effects] happen but it's not like that', I think what happens is that the information is not tailored for myself. It's general information.
Pertinent to this appraisal was the information-filtering process described by participants:
Group 2, Participant 4
It's always better to try and take as much information and try and sift out what's useless
Group 1, Participant 6
When they're talking about people using this medicine, 'ninety-eight percent will die within five years' . . . you have to take that and filter it through a whole bunch of other variables . . . and whether [the information] is not terribly well informed or completely informed.
One common way in which some participants were able to filter information on medicines was to use other Web sites for comparison and cross-checking, for example:
Group 4, Participant 1
I always go to two or three sites.
Although participants reported methods of evaluating information, many expressed a difficulty in their evaluation, for example:
Group 2, Participant 4
How do you [figure] out what's useful?
Group 3, Participant 7
How do you know what's reliable and what's not?
Ultimately, despite an awareness of the shortcomings and difficulties in evaluating the quality of information on medicines, all participants saw the Internet as an important resource for this information, for example:
Group 1, Participant 1
I think as patients you expect immediate information and the Internet, whether it's credible or not, it's the fact that people can get it.
Discussion
The issue of consumer use of Internet-based information on medicines is important because it has been shown that written information on medicines can be interpreted by consumers in ways that may lead to anxiety or apprehension [7,29- 32], and a refusal of prescribed medicines [33]. Conversely, it has been shown that written medicine information increases consumer knowledge about their medicines [29,34- 36] and that well-informed consumers with an increased understanding of the purpose of their medicines may have improved compliance and satisfaction with their therapy [29,31,37- 40].
However, studies on consumer use of written information on medicines have evaluated standardized information on medicines such as that produced by pharmaceutical companies, government or professional bodies, or health care practitioners [7]. In contrast, this study explored Internet-based information, which is neither standardized nor subject to universal quality control. Furthermore, medicines in particular are subject to commercial considerations that may have an impact on the motives for and quality of information. Therefore, the impact of Internet-based information on consumer use of medicines may differ from that reported from consumer use of standardized written information on medicines.
The reported search skills of these participants were comparable to those of participants observed while searching for general health information [6] in that they mainly searched using simple strategies in a search engine and chose results primarily from the first page of search results. Although this similarity is not surprising, it does illustrate the overlap between appraising general health information and specifically medicines-related information. Indeed, it was not always possible for consumers in this study to speak on issues surrounding searching for and appraising information on medicines without speaking about other health-related issues.
Participants in this study searched for information on medicines using a range of search techniques from simple 1-word searches and advanced techniques to suboptimal techniques. However, although some participants had little understanding of how search engines worked and possessed suboptimal search skills, a few participants described proficient search skills. Contrary to findings where consumers were observed to use information not applicable to their health setting [6], participants generally reported a strong awareness of the limitations of non-Australian information due to health-setting limitations pertinent to medicines use.
Participants were conscious that there was an abundance of poor-quality information on medicines on the Internet. They were also predominantly aware that information on the use of medicines and on the incidence of side effects is often based on individual factors that should not be seen as applicable to everyone. Therefore, while consumer evaluation skills have been referred to as "meager" [41], the assumption that consumers believe everything they read does not take into account those participants who are savvy about issues such as bias, commercialism, and the lack of regulation of Internet-based information on medicines.
However, the fact that many participants searched for information on a medicine by typing the brand name into a search engine would indicate that it was highly likely that they encountered the Web site of a pharmaceutical company on the first page of results [42], which raises the matter of consumer ability to interpret information on medicines that may not be comparative and unbiased in nature and not aimed at an Australian audience. Even though results from this study would indicate that many participants were aware of these limitations, others still viewed a pharmaceutical company Web site as the official, and therefore exclusive, information on a medicine; this indicates that some consumers may be unaware of or uninterested in information on medicines produced by alternate sources. Nevertheless, it has been suggested that consumers are more likely to search for alternate sources, rather than relying on product brands, as they become more experienced using the Internet [43].
It is clear that there was a variety of skills among participants. Many had not been conscious of some of the issues surrounding the process of searching for and appraising information on medicines and did not undertake this process in the most-constructive way. Furthermore, there have been few studies in the literature that have sought to educate consumers on strategies for effective use of the Internet for health information [44- 47].
Limitations in This Study
There are several important limitations in this study.
First, as this information is self-reported, consumers may not actually search for and appraise information in the same way as they describe. Such a discrepancy was demonstrated when participants in an observational study were reported to be less likely to look for the sources of the information than was apparent from claims in focus groups [6]. However, participants in that observational study were not searching for information that they would personally use; this may have meant that they were less concerned about the quality of the information.
Second, the bounded period of 12 months in the inclusion criteria may be too long for consumers to correctly remember details of how they searched for and chose information. It might have been beneficial to actually perform a search as an activity to stimulate the participants' memories.
Third, participants in group situations may feel compelled to provide socially-desirable answers that are not necessarily accurate. In this study, we sought to minimize this by informing participants that their results would be confidential and that they were welcome to speak about anything they felt even if they disagreed with someone else. However, this does not negate the problem. Although the use of individual interviews may help to minimize this discrepancy, this method is more time-consuming and cannot use group interaction for the generation of ideas.
Last, certain actions are intuitive and therefore difficult to articulate. Most participants were not able to adequately describe their search and appraisal processes, which suggests that this process may largely be a form of tacit or implied knowledge.
Therefore, future research needs to take into account actual observed (rather than reported) search and appraisal skills of consumers who are seeking information on medicines for their own use.
Conclusion and Future Research
The results of this study show that consumers may benefit from greater awareness and education on the significance of good search and appraisal skills for information on medicines so that this process is deliberate and conducted with thought rather than being random and tacit. Furthermore, there is evidence that consumers may support education that shows them how to search for information on medicines on the Internet [48]. However, health promotion and education needs to take into account the variety of consumer skills in both searching for and critically evaluating information. Pharmacists are in an ideal position to provide consumer training as they frequently counsel consumers on medicines [49] and have consumers present them with information from the Internet [19]. However, to successfully deliver this program, pharmacists need to be trained in these skills . Furthermore, the impact of pharmacist education on consumers' searches for Internet-based information on medicines and appraisal of that information needs to be evaluated. Therefore, future research by this team will be on the development of a health-promotion program for pharmacists to train consumers to search for and appraise Internet-based information on medicines.

consumers
information
medicines
internet
focus groups
drugs
qualitative research


Variation analysis and gene annotation of eight MHC haplotypes: The MHC Haplotype Project

The human major histocompatibility complex (MHC) is contained within about 4 Mb on the short arm of chromosome 6 and is recognised as the most variable region in the human genome. The primary aim of the MHC Haplotype Project was to provide a comprehensively annotated reference sequence of a single, human leukocyte antigen-homozygous MHC haplotype and to use it as a basis against which variations could be assessed from seven other similarly homozygous cell lines, representative of the most common MHC haplotypes in the European population. Comparison of the haplotype sequences, including four haplotypes not previously analysed, resulted in the identification of >44,000 variations, both substitutions and indels (insertions and deletions), which have been submitted to the dbSNP database. The gene annotation uncovered haplotype-specific differences and confirmed the presence of more than 300 loci, including over 160 protein-coding genes. Combined analysis of the variation and annotation datasets revealed 122 gene loci with coding substitutions of which 97 were non-synonymous. The haplotype (A3-B7-DR15; PGF cell line) designated as the new MHC reference sequence, has been incorporated into the human genome assembly (NCBI35 and subsequent builds), and constitutes the largest single-haplotype sequence of the human genome to date. The extensive variation and annotation data derived from the analysis of seven further haplotypes have been made publicly available and provide a framework and resource for future association studies of all MHC-associated diseases and transplant medicine.
Introduction
The MHC has long been believed to be the most important region in the human genome with respect to infection, inflammation, autoimmunity and transplant medicine (Lechler and Warrens 2000). This was recently confirmed by the largest genome-wide association study carried out to date for seven common diseases, including two autoimmune diseases (type 1 diabetes and rheumatoid arthritis) and one inflammatory disease (Crohn’s disease). The highest associations were found between the MHC and these two autoimmune diseases (The Wellcome Trust Case Control Consortium 2007). The complex aetiology of MHC-associated disease coupled with high density, polymorphism, linkage disequilibrium (LD) and frequent non-Mendelian inheritance of gene loci have made it challenging to identify variations that cause or contribute to disease phenotypes. Additional limiting factors have been our incomplete knowledge of the allelic variation of genes and regions flanking the nine classical human leukocyte antigen (HLA) loci and the lack of a single haplotype reference sequence, the original reference sequence being a composite of multiple MHC haplotypes (Mungall et al. 2003; The MHC Sequencing Consortium 1999).
Recognizing that the future identification of variants conferring susceptibility to common disease is critically dependent on fully informative polymorphism and haplotype maps, the MHC Haplotype Consortium formed in 2000 with the aim to generate these critical data and to make them publicly available as a general resource for MHC-linked disease studies. Similar efforts, but with different experimental approaches, were also carried out in Japan (Shiina et al. 2006) and the USA (Smith et al. 2006). To develop the resource, eight HLA-homozygous MHC haplotypes were selected on the basis of conferring either protection against or susceptibility to two autoimmune diseases, type 1 diabetes and multiple sclerosis, and that represented common haplotypes in European populations. In the subsequent years, incremental data, materials and tools comprising this resource have been released (Allcock et al. 2002; Horton et al. 2004; Stewart et al. 2004; Traherne et al. 2006) and have contributed towards the construction of a high-resolution LD map and a first generation of HLA tag single nucleotide polymorphisms (SNPs; de Bakker et al. 2006; Miretti et al. 2005) and the identification of a second MHC susceptibility locus for multiple sclerosis (The International Multiple Sclerosis Genetics Consortium; Yeo et al. 2007). In this paper, we report the final account of this international effort, including, analysis of the last four of the eight haplotypes, up-to-date variation statistics, gene annotation, population-specific aspects and a detailed description of the databases and tools for viewing and accessing the data in the context of existing genome annotation.
Materials and methods
Variation analysis
The method previously reported for comparison of MHC haplotype sequences (Stewart et al. 2004; Traherne et al. 2006) was extended to cover all eight haplotypes. Briefly, the most suitable method proved to be a clone by clone comparison using the discrepancy-list option of the cross_match program (Green, unpublished; http://www.phrap.org/), an implementation of the Smith–Waterman sequence alignment algorithm (Smith and Waterman 1981), using the alignment of a haplotype clone sequence with the appropriate overlapping reference sequence from a PGF clone or clones. All variations were submitted to dbSNP using the submitter handle SI_MHC_SNP and user identifiers of the form [PGF BAC clone sequence version]_[position in PGF BAC clone sequence]_[variation change]. Thus, AL662890.3_6645_TC indicates a substitution in which the base T at base position 6645 in AL662890.3 (PGF BAC 308K3) was substituted by C in the other haplotype. In the case of indels, the ‘variation change’ consists of ‘i’ or ‘d’ (for insertion or deletion), followed by a numerical value for the length of the indel, in turn followed by the inserted or deleted sequence if this were of 12 or fewer bases. For longer indels, an X value is given, which refers to a look-up table (http://www.sanger.ac.uk/HGP/Chr6/MHC/Xfile). Thus, AL662890.3_7470_d8TACACACA indicates a deletion in AL662890.3 after base 7470 of the eight bases ‘TACACACA’. Further, AL662890.3_10559_i5ATATT indicates an insertion in AL662890.3 starting after base 10559 of the five bases ‘ATATT’. AL662890.3_7475_d14X1 indicates a 14-base deletion after base 7475 in AL662890.3 of a sequence coded as X1 which is ‘ATACACACACACAC’.
Major indel sequences, appearing as breaks in the cross_match discrepancy lists between two clones from difference haplotypes, were extracted and subjected to analysis by RepeatMasker to detect the presence of retrotransposible elements.
Gene annotation
The finished genomic sequence for each of the eight haplotypes was analysed using a modified Ensembl pipeline (Searle et al. 2004). CpG islands were predicted on unmasked sequence. Interspersed and tandem repeats were masked out by RepeatMasker (Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-3.0. 1996–2004, http://www.repeatmasker.org) and Tandem Repeats Finder (TRF; Benson 1999), respectively. The sequence was then BLAST searched (BLAST, basic local alignment search tool; Altschul et al. 1990) using a vertebrate set of complementary DNAs (cDNAs) and expressed sequence tags (ESTs) from the European Molecular Biology Laboratory (EMBL) nucleotide database (Kulikova et al. 2007), followed by the re-alignment of significant hits. Non-redundant proteins were aligned similarly. Protein domain matches were provided through alignment of Pfam to the genomic sequence using Genewise (Birney et al. 2004), thereby providing protein domain data to the annotator. Ab initio gene predictions were performed by Genscan (Burge and Karlin 1997) and Fgenesh (Salamov and Solovyev 2000), and potential transcriptional start sites were predicted by Eponine (Down and Hubbard 2002). Analysis results were displayed, and annotation was performed through an in-house annotation software system.
Genes were manually annotated according to the human and vertebrate analysis and annotation (HAVANA) guidelines (http://www.sanger.ac.uk/HGP/havana/) using evidence based on comparison with external databases as of August 2005. All gene structures are supported by transcriptional evidence, either from cDNA, EST, or protein. In general, annotations are supported by best-in-genome evidence. Haplotype-specific evidence is assigned where possible. As with previous MHC annotation (Stewart et al. 2004; Traherne et al. 2006), some olfactory receptors have been built upon protein homology alone because of their restricted expression.
Locus and variant types were annotated according to established standards (Harrow et al. 2006), with the modification that, within the MHC region, the artefact locus has been used to tag historically annotated structures that are no longer deemed valid.
HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, and HLA-DQB1 allele types were assessed by comparison against the IMGT/HLA database (http://www.ebi.ac.uk/imgt/hla/; Marsh et al. 2005).
Annotation status of haplotypes
The PGF, COX, and QBL haplotypes have already been annotated in detail (Stewart et al. 2004; Traherne et al. 2006). It was decided, however, to re-annotate and update this annotation to maintain consistency between all eight haplotypes with the current supporting evidence and pipeline analyses. The SSTO haplotype was manually annotated de novo. The new annotation from the PGF haplotype was projected through a DNA–DNA alignment to each of the remaining haplotypes (APD, DBB, MANN and MCF) where possible. This projection was checked thoroughly and non-alignable regions were manually adjusted (including the C4 and HLA-DRB1 hypervariable regions). Polyadenylation sites and signals were not annotated for haplotypes APD, DBB, MANN and MCF because of time constraints. In the main, however, these features may be assumed to correspond to the same positions as in the first four haplotypes.
Combination of variation and annotation data
By employing a series of Perl scripts, the array of haplotype variation was combined with the annotation of gene loci, repeat elements and microsatellites, extracted from the Vertebrate Genome Annotation (VEGA) database in general feature format (GFF; http://www.sanger.ac.uk/Software/formats/GFF/), to determine the variation status of all loci.
Distribution of sequenced HLA haplotypes in Europeans
To assess the distribution of sequenced haplotypes at the population level, 180 founder haplotypes were reconstructed using genotypic data from Centre d’Etude Polymorphisme Humain (CEPH) trios (de Bakker et al. 2006). A ~214 kb segment spanning the HLA–DRB1–DQB1 genes was selected for the analyses. This segment, represented by 54 SNPs, is delimited by rs2187823 and rs2856691, with NCBI build 36 chromosome 6 coordinates 32547486 and 32761413, respectively. Phased haplotypes with known HLA–DRB1–DQB1 alleles were then used to construct a neighbor-joining tree (Kumar et al. 2001) and a phylogenetic network (Bandelt et al. 1999).
Resources
All sequences presented in this paper have been submitted to the EMBL/GenBank/DNA Data Bank of Japan (DDBJ) database and allocated accession numbers. For clarity, all bacterial artificial chromosome (BAC) clones are referred to using their accession numbers. The annotation of each haplotype has been entered in the VEGA database and is accessible through its browser (http://www.VEGA.sanger.ac.uk). All variations from the study were submitted to dbSNP (http://www.ncbi.nlm.nih.gov/SNP) using the submitter handle SI_MHC_SNP.
BAC clones from the CHORI-501 (PGF) and CHORI-502 (COX) libraries can be requested from BACPAC resources (http://www.bacpac.chori.org/). Clones from the other libraries can be requested from john.elliott@ualberta.ca.
The web site for the MHC Haplotype Project provides links to various data resources (http://www.sanger.ac.uk/HGP/Chr6/MHC/).
DAS sources for all substitutions and indels are available from http://www.das.ensembl.org/das as follows:
ens_35_COX_SNP ens_35_COX_DIPens_35_QBL_SNP ens_35_QBL_DIPens_35_SSTO_SNP ens_35_SSTO_DIPens_35_APD_SNP ens_35_APD_DIPens_35_DBB_SNP ens_35_DBB_DIPens_35_MANN_SNP ens_35_MANN_DIPens_35_MCF_SNP ens_35_MCF_DIP
These can be accessed via the VEGA browser.
Results and discussion
Variation analysis
One of the main aims of the MHC Haplotype Project was to generate a comprehensive variation map of this most variable region of the human genome. To achieve this, eight haplotypes were sequenced and subjected to variation analysis. Table 1 details the lengths of the sequence contigs, the number of sequence gaps and the allelic types of major HLA loci for each haplotype. Of the eight haplotypes sequenced, three have already been described: PGF and Cox (Stewart et al. 2004) both of which formed single contigs of approximately 4.7 Mb, and QBL (Traherne et al. 2006), of approximately 4.2 Mb but with five gaps. The remaining haplotypes sequenced all contained gaps, their coverage ranging from 2.33 Mb (DBB with 28 gaps) to 4.19 Mb (MANN with 10 gaps).
Table 1Haplotype sequence contig length, number of gaps and HLA allele typesHaplotypeLength (bp)GapsHLA-AHLA-BHLA-CHLA-DQA1HLA-DQB1HLA-DRB1PGF47548290A*03010101B*070201Cw*07020103DQA1*010201DQB1*0602DRB1*150101COX47318780A*01010101B*080101Cw*070101DQA1*050101DQB1*020101DRB1*030101QBL42492725A*260101B*180101Cw*050101DQA1*050101DQB1*020101DRB1*030101APD416096516A*01010101––––−DBB233010128A*02010101–Cw*06020101DQA1*0201DQB1*030302DRB1*070101MANN419101410A*290201B*440301Cw*160101DQA1*0201DQB1*0202DRB1*070101MCF408741315[A*020101]B*15010101Cw*030401DQA1*0303DQB1*030101–SSTO370424922A*320101B*44020101Cw*050101DQA1*030101DQB1*030501DRB1*040301Sequence length (bp) and number of gaps in each haplotype sequence, together with the HLA gene types obtained by BLAST against the IMGT/HLA database. Dashes or data in square brackets indicate the absence or the partial presence, respectively, of a gene owing to a sequence gap.
For the variation analysis, each of the above haplotypes was compared with the PGF reference sequence, resulting in the identification of 44,544 variations (37,451 substitutions and 7,093 indels, Table 2), which have all been submitted to dbSNP. The success of this exercise is illustrated by the fact that examination of this public database (NCBI dbSNP build 127, March 2007) showed that there were only a further 19,598 variations, submitted by other laboratories, in this region which were not identified by this project. In accordance with the annotation that we also generated for each haplotype (see below), the variations shown in Table 2 were further classified as untranslated region (UTR), exonic, intronic, intergenic and eight more sub-categories (Table 3). Coding substitutions, which are of particular interest with respect to altered functionality, were further classified as synonymous, non-synonymous conservative, or non-synonymous non-conservative and grouped depending on whether they affected HLA or other genes (Table 4). The actual variations and affected amino acids can be viewed using the VEGA browser as illustrated in Fig. 1 and described in the corresponding section later on. In addition, we have analysed all haplotype sequences for inversions, which represent another important variation category that has been linked to genomic disorders (Shaw and Lupski 2004). Using Ssaha2 (Ning et al. 2001), we found no evidence of any inversion polymorphism within the generated sequences but could not exclude large-scale (e.g. involving entire MHC) inversions with breakpoints outside the MHC regions sequenced here.
Fig. 1Annotation and variation data in VEGA. VEGA ‘overview’ (a), ‘detailed view’ (b) and ‘basepair view’ (c) example of the variation in the OR2J1 locus in which a STOP codon is present in all haplotypes except MCFTable 2Distribution of substitutions and indels amongst haplotypesHaplotypeSubstitutionsIndelsALLCOX15,9672,39318,360QBL15,2822,36017,642SSTO14,9822,30017,282APD4,2306834,913DBB14,2551,97516,230MANN12,1021,65413,756MCF10,7901,54512,335Overall37,4517,09344,544Number of variations found by comparing the PGF haplotype sequence with each of the other haplotype sequences in turn.Table 3Distribution of substitutions and indels within different sequence regions amongst haplotypesSequence regionBase pairsCOXQBLSSTOAPDDBBMANNMCFSIDSIDSIDSIDSIDSIDSIDCoding247,5053538503193802740351640193482UTR155,960382344385933135389326393033530931Intronic1,283,4723,1415713,1355902,6585056021472,8975092,1853932,126404Total intragenic1,686,9373,8766134,0766683,3695427141563,5745542,8894372,783437Pseudogenic57,223235152262122719101819110109611310Pseudogenic intron63,1085075422027215181582025822981317913Transcript exon78,0921903020733119227181361788167015Transcript intron332,7051,2431971,1862161,05315585291,2451921,08116126853REPEATS:LINEs608,4292,1102212,0152402,388255755932,0972172,0841931,530164SINEs428,5671,3814281,3164011,3113853461341,229318928241936271Other repeats487,8632,6052072,5182292,514207925562,7481992,1981772,170169Total in repeats1,524,8596,0968565,8498706,2138472,0262836,0747345,2106114,636604Microsatellite15,18518616895852221981429607661719068All above3,297,59012,3331,93311,8591,92011,4181,8013,16953311,5381,6059,5361,3158,1391,200Other intergenic996,7203,6344603,4234403,5644991,0611502,7173702,5663392,651345Total4,754,82915,9672,39315,2822,36014,9822,3004,23068314,2551,97512,1021,65410,7901,545Variations shown in Table 2 ascribed to sequence regions identified during annotation. These included exonic, UTR and intronic regions of coding; pseudogenic and transcript loci; repeat elements, microsatellites and other intergenic regionsS Substitution, ID indelTable 4Codon variation caused by substitutions in HLA and other gene lociCodons variation by virtue of substitutionsCOXQBLSSTOAPDDBBMANNMCFHLAOtherTotalHLAOtherTotalHLAOtherTotalHLAOtherTotalHLAOtherTotalHLAOtherTotalHLAOtherTotalSynonymous498113071106177725712912425666913559791388052132Non-synonymousTotal Conservative12576201184121305164722361927461207619614491235147562036842110102721749239131111829674010777601378235117Non-conservative573491824913172331058917533689673198652186Total174157331255227482236129365205171186145331203170373227108335Coding substitutions analysed for their effects on protein sequences and listed in by haplotype for HLA genes (HLA-A HLA-B HLA-C HLA-DRB1 HLA-DRA HLA-DQA1 HLA-DQB1 HLA-DPA1 HLA-DPB1) and for all other genes according to the changes they induced in codons as either synonymous, non-synonymous conservative, or non-synonymous non-conservative changes.
Gene annotation
There have been several previous annotations of the gene content of the MHC (Horton et al. 2004; Mungall et al. 2003; Stewart et al. 2004; The MHC Sequencing Consortium 1999; Traherne et al. 2006). The maximum region annotated in this study extends from the telomeric ZNF452 gene in the MHC extended class I region (COX haplotype) to the centromeric ZBTB9 gene just telomeric of the MHC extended class II region (PGF and SSTO haplotypes). The PGF haplotype (Stewart et al. 2004) remains the longest complete MHC haplotype, encompassing 320 annotated loci with 1,267 variants. The number of variants ascribed to each locus-type is listed in Table 5. A comparison of the statistics for loci in each haplotype is shown in Table 6.
Table 5Splice-variant statistics for PGF annotationTypeNo.Total splice variants1,267Coding523Unprocessed_pseudogene50Processed_pseudogene41Expressed_pseudogene7Transcript271Putative71Retained_intron263Nonsense_mediated_decay30Artefact11Total loci320Splice variants annotated in the PGF haplotype.Table 6Gene annotation statistics for eight MHC haplotypesLocus typePGFCOXQBLSSTOAPDDBBMANNMCFCoding16515915013182146129150Transcript2828262619262722Putative181815156161214Pseudogenes total9895939859929575Unprocessed5048485336525342Processed4142403919343728Expressed75564655Artefact111110110000Total loci320311294281166281264261Total variants1,2671,1911,1551,0585681,1389601,115Annotation statistics for loci in each haplotype. For definitions of locus types see “Materials and methods”.
VEGA database and browser
The VEGA database provides access to gene annotation of the eight MHC haplotype sequences, a valuable public resource and a means of integrating annotation and variation data. The VEGA database also provides the facility to download nucleotide or peptide sequences for genes of interest, by selecting ‘export cDNA’ or ‘export peptide’ from the menu obtained by clicking on gene cartoons in the VEGA ‘detailed view’ or ‘basepair view’ window. From these, any desired alignments can be made. Variation data may be viewed in the browser linked to a distributed annotation system (DAS) source of any given variation (see “Materials and methods”). This is illustrated An example of the use of this browser to view a C to T substitution is illustrated for the OR2J1 locus (Fig. 1). An overview of the genomic environment is given in Fig. 1a, showing the gene within a cluster of olfactory gene loci on chromosome 6. The detailed view (Fig. 1b) shows OR2J1 with associated variations in all haplotypes. The basepair view (Fig. 1c) illustrates the presence of the C/T substitution in all haplotypes except MCF, and its positioning above the translated sequence, at the first position of a CAG codon, indicating the presence of a stop codon instead of glutamine.
Annotation changes
In addition to loci annotated in the previous studies, newly recognised with official Hugo Gene Nomenclature Committee (HGNC) symbols have also been annotated. These have included the mitochondrial coiled–coil domain protein 1 gene MCCD1 (Semple et al. 2003) and the related unprocessed pseudogenes MCCD1P1 and MCCD1P2, as well as the zinc-finger and BTB domain-containing protein gene ZBTB9, annotated at the very centromeric boundary of the sequenced region.
The C6orf21 gene (De Vet et al. 2003; XXbac–BPG32J3.17-001) of the MHC class III region was annotated as a separate locus from the adjacent centromeric locus LY6G6D (splice variants XXbac–BPG32J3.4-001 and XXbac–BPG32J3.4-002). There was, however, a further coding splice variant of LY6G6D (XXbac–BPG32J3.4-004), which spanned not only the other LY6G6D splice variants but also C6orf21, suggesting that this is a possible so-called chimeric transcript (Parra et al. 2006).
HLA-DRB1 hypervariable region
Of the five newly annotated MHC haplotypes, APD alone exhibited the HLA–DRBDR52 antigenic specificity found on DRB1*3, DRB1*05 (DRB1*11 and DRB1*012) and DR6 (DRB1*13 and DRB1*14) haplotypes and encoded by HLA–DRB3, whereas the remainder (SSTO, DBB, MANN and MCF) exhibited the DR53 specificity, encoded by HLA–DRB4, here annotated for the first time in genomic sequence. The HLA–DRB53 sequences included three known loci (HLA–DRB4, HLA–DRB7 and HLA–DRB8), as well as three novel pseudogenes (DASS–218M11.1, DASS–23B5.1 and DASS–23B5.2). DASS–23B5.1 corresponds to a pseudogene derived from the gene for the protein kinase, interferon-inducible double-stranded RNA dependent activator (Chida et al. 2001) for which the symbol PRKRAP1 has now been recognised. A further processed pseudogene, FAM8A5P (Jamain et al. 2001), was also annotated in the DR53 specificity.
HLA-V and HLA-P
Our analysis showed that the two unprocessed class I pseudogenes HLA-V and HLA-P ( previously HLA-75 and HLA-90, Geraghty et al. 1992) should in fact be merged together; individually they merely represented the 5′ and 3′ portions of a single unprocessed pseudogene, separated by repeat elements. According to our annotation guidelines (see “Materials and methods”), the newly merged locus was assigned the symbol from the 3′ component, in this case, HLA-P. Best-in-genome nucleotide evidence was found to support five transcript variants at the 5′ end, which, together with evidence for continued locus-transcription, led us to designate the locus as a transcribed pseudogene. Because transcription appears to still occur at this locus, it was, therefore, designated as a transcribed pseudogene. A further six expressed pseudogenes were identified in the MHC region (HLA–DPB2, HLA-J, CYP21A1P, HLA–DRB6, HLA–L and PPP1R2P1).
RCCX hypervariable region
This module within the MHC class III region, named for its gene content (RP-C4A/B-CYP21-TNXB), may be duplicated or triplicated (Chung et al. 2002), and the pseudogenes CYP21A1P, TNXA and STK19P contain the complement component gene, C4, in either or both of the two versions, C4A and C4B (Awdeh and Alper 1980). This gene may also be present in either long (C4AL, C4BL) or short (C4AS, C4BS) forms depending on the presence or absence of an inserted HERVC4 element in intron 9. Contrary to our previous annotation (Stewart et al. 2004) see also legend to (Fig. 2), the PGF haplotype now appears to possess an arrangement in which C4AL precedes C4BL, whereas COX has a single module with C4BS and QBL has a single module with C4AS (Traherne et al. 2006). For the new haplotype sequences reported in this paper, SSTO was bimodular with two copies of C4BL, whereas DBB was bimodular with C4AL followed by C4BS. Although a sequence gap was present in MCF, this haplotype appeared to be bimodular in that, although the telomeric copy of the C4 gene could not be identified, there was evidence for the pseudogenes CYP21A1P, TNXA and STK19P in a telomeric module. The second centromeric module in MCF contained C4AL. The RCCX region in the APD and MANN haplotypes was incomplete because of sequence gaps.
C6orf205
Variability in the C6orf205 gene has been reported to consist of extension of the minisatellite in exon 2 from 27 copies in PGF and COX to 31 copies in QBL (Traherne et al. 2006). In the newly annotated haplotypes, we found the minisatellite to extend to 29 in MANN. The APD, DBB and MCF possessed 27 copies. There was a sequence gap in this region in the SSTO haplotype.
MICA
The known allelic polymorphism of MICA reported for the DRB1*03 QBL cell line sequence, in which a four-base insertion (GCGT) extended the open reading frame in coding exon 5 haplotype (Traherne et al. 2006), was also present in the DRB1*07 MANN haplotype. The insertion was absent from PGF, COX and SSTO. No sequence was available in APD, DBB and MCF for this gene.
PPP1R2P1
The intronless pseudogene PPP1R2P1 reported to have a full-length open reading frame in the PGF, COX and QBL haplotypes (Stewart et al. 2004; Traherne et al. 2006) was found to have a similar open reading frame in the DBB and MANN haplotypes but to have the frameshift mutation seen in the original chromosome reference sequence (Mungall et al. 2003) in the SSTO, APD and MCF haplotypes.
PSORS1C1
The QBL haplotype remains the only one in which there was a single nucleotide deletion in a polyC tract of exon 5 (Traherne et al. 2006). DBB, MANN and MCF resembled PGF and COX. No sequence was available for this gene in SSTO or APD.
POU5F1
The PGF haplotype has been reported to have a disrupted start codon for alternative splice variant of POU5F1 (Traherne et al. 2006). This disruption was not present in COX or QBL nor was it present in the further haplotypes reported in this paper, namely SSTO, DBB, MANN and MCF. APD had no sequence in this region.
OR2J1
This olfactory receptor OR2J1 has been reported to have both functional and non-functional alleles (Ehlers et al. 2000), the latter the result of a premature stop codon at amino acid position 194 introduced by a substitution in the coding sequence. In our annotation, we found the PGF and MCF haplotypes to contain the full-coding sequence, whereas the COX, QBL SSTO, APD, DBB and MANN haplotypes to contain the truncated sequence as an unprocessed pseudogene (see above and Fig. 1).
Other annotation differences
Other loci included in the current but not the previous PGF annotation were HCG4P11, HCG4P8, HCG4P7, HCG4P5, HCG4P3 and the loci without symbols listed in Table 7. Previously annotated loci not annotated in this study or considered artefacts because they did not reach our current standards of annotation included HLA-X, C6orf215, HCG2P7, HCG8, HCP5P2, HCP5P3, HCP5P6, HCP5P12, HCP5P13, HCP5P14, HCP5P15, HCG8 and HCG26.
Table 7Other newly annotated lociLocusLocus typeXXbac-BCX196D17.5TranscriptXXbac-BPG116M5.14PutativeXXbac-BPG116M5.15PutativeXXbac-BPG116M5.16PutativeXXbac-BPG118E17.9PutativeXXbac-BPG126D10.10Processed pseudogeneXXbac-BPG126D10.11Processed pseudogeneXXbac-BPG13B8.10TranscriptXXbac-BPG13B8.9Unprocessed pseudogeneXXbac-BPG154L12.4PutativeXXbac-BPG181B23.4TranscriptXXbac-BPG181M17.4PutativeXXbac-BPG246D15.8TranscriptXXbac-BPG248L24.10Unprocessed pseudogeneXXbac-BPG248L24.9Processed pseudogeneXXbac-BPG249D20.9PutativeXXbac-BPG250I8.13TranscriptXXbac-BPG254F23.5PutativeXXbac-BPG254F23.6PutativeXXbac-BPG254F23.7TranscriptXXbac-BPG254F23.7PutativeXXbac-BPG27H4.7TranscriptXXbac-BPG27H4.8TranscriptXXbac-BPG294E21.7Processed pseudogeneXXbac-BPG296P20.14PutativeXXbac-BPG296P20.15PutativeXXbac-BPG299F13.14PutativeXXbac-BPG308J9.3TranscriptXXbac-BPG308K3.5PutativeXXbac-BPG308K3.6TranscriptXXbac-BPG309N1.15Unprocessed pseudogeneXXbac-BPG32J3.18PutativeXXbac-BPG8G10.2Unprocessed pseudogeneDAQB-12N14.5TranscriptDAQB-331I12.5PutativeDAQB-335A13.8TranscriptNewly annotated loci without HGNC symbols.
Non-canonical splice sites
Eight variants within six loci were shown to exhibit haplotypic variation at their splice sites (canonical to non-canonical motif; Table 8). These variations may affect the gene expression at the post-transcriptional level. Hoarau et al. (2004, 2005) have already described the differential splicing within the HLA–DQA1 locus, and this can clearly be seen by comparing the new HLA–DQA1 annotation through the VEGA genome browser.
Table 8Haplotype variation at splice sitesGeneVariantAffected exonsDonor*Acceptor*dbSNP cluster IDBest evidencePGFQBLCOXSSTODBBAPDMANNMCFTRIM3123/4ggttggrs28400887cDNANCNCNCCNDNCNCCTRIM3152/3ggttggrs28400887ESTNCNCNCCNDNCNCCC4B73/4ggtcgg–ESTNCNDNCCNCNDNDNDC4A73/4ggtcgg–ESTNCNCNDCNCNDNDNCHLA-DQA144/5ggtcggrs707947cDNACCCNCNCNDNCNCHLA-DQA154/5ggttaa/caars3667cDNANCNCNCCCNDCCHLA-DRB122/3gatcagrs9271083ESTNCCCCCNDCNDGene loci and variants that are affected by disruptive variations at splice sites. C Canonical splice site (donor = ngt; acceptor = nag), NC non-canonical, and ND no data (gene absent or gap). Donor and acceptor variable nucleotides in bold with equivalent dbSNP cluster ID number given in column to right. The C4A and C4B genes are, for these purposes, effective duplicates of each other. The two TRIM31 variants share the same splice site (but differ elsewhere in structure). The two HLA–DQA variants share the same donor but have alternative acceptors. Note the mutually exclusivity of these variants amongst the haplotypes (Hoarau et al. 2004; Hoarau et al. 2005).
Combination of variation and annotation data
The data for sequence contig length, gaps, variation rate within haplotypes and PGF coding gene annotation have been combined in the map in Fig. 2. This illustrates the concentration of variation around the HLA gene loci, specifically in 3 areas: around HLA-F, HLA-G and HLA-A; around HLA-C and HLA-B; and around HLA-DRB1, HLA-DQA1, HAL-DQB1, HLA-DQA2 and HLA-DQB2. The variation status of genes of the PGF haplotype is shown in Table 9.
Fig. 2Variation and annotation map of eight MHC haplotypes. The map represents the complete reference sequence (orange bar split into three 1.6 Mb sections) labelled PGF and marked with a scale (Mb) and approximate megabase positions on the NCBI36 build of chromosome 6 (grey milestones). Below the reference sequence are arrows representing gene positions and orientations colour-coded for variation status (invariable, black; with synonymous variation only, green; with non-synonymous, conservative variation, red; with non-synonymous, non-conservative variation, purple; see Table 8) and their symbols on a band denoting MHC class (extended class I, green; class I, yellow; class III, pale orange; class II, light blue; extended class II, pink; outside MHC, pale grey). Above the reference sequence, coloured bands represent the sequences of the other seven haplotypes (COX, orange; QBL, mauve; APD, yellow; DBB, green; MANN, light blue; SSTO, dark blue; MCF, purple) with sequence gaps in dark grey; the RCCX hyper-variable region shown with green (C4A block) and/or red (C4B block) or black (block absent), and the HLA–DRB hyper-variable region in shades of blue-green. Above each haplotype bar, a bar-graph represents total variation between the haplotype and the reference sequence (total variations/10 kb) in dark red. Re-examination of the sequence AL645922 from the PGF haplotype, which contains the RCCX region, has shown that the original assembly was erroneous. Correction of these errors leads us now to the conclusion that the C4A gene precedes the C4B gene in this clone sequence. This new gene order is reflected in Fig. 2Table 9Variation status of the main coding variant of each gene in the PGF haplotype annotationInvariableSynonymous variation onlyNon-synonymous variationConservative variationNon-conservative variationABCF1BAT1aAGERBAT2AGPAT1BAT5BRD2aBAT3AIF1C2BTNL2BAT4APOMCREBL1C6orf21C4AATP6V1G2DAXXC6orf27C4BB3GALT4DDR1aCFBC6orf10C6orf134GNL1DOM3ZC6orf100C6orf136aGPSM3DPCR1C6orf15C6orf26GTF2H4EGFL8C6orf205C6orf48HLA-DOAaEHMT2C6orf25CLIC1HSPA1BFKBPLC6orf47CSNK2BLY6G6CGABBR1CCHCR1CUTAMSH5HLA-DMACDSNCYP21A2PBX2HLA-DOBCOL11A2DDAH2POU5F1HLA-DQB2DHX16FLOT1PPP1R11HLA-DRAHLA-AHLA-DPA1PRR3HSPA1AHLA-BHLA-DRB5RING1LY6G6DHLA-CHSD17B8RNF5MCCD1HLA-DMBKIFC1aRXRBMOGaHLA-DPB1LSM2SYNGAP1OR11A1HLA-DPB2LST1TRIM10OR2H2HLA-DQA1LTBTRIM26OR2J1HLA-DQA2LY6G5CTRIM27OR2J2HLA-DQB1LY6G6ETRIM39aOR2J3HLA-DRB1MAS1LVPS52PHF1HLA-EMRPS18BZBTB12PSMB9HLA-FNCR3ZBTB9RPP21HLA-GNEU1ZNRD1SFTPGHSPA1LNRMSKIV2LIER3OR2B3SLC44A4KIAA1949OR2H1TAP2LTAOR2W1TRIM15LY6G5BPFDN6WDR46MDC1PPP1R10ZBTB22MICAPRRT1ZNF311MICBPSMB8bNFKBIL1RDBPNOTCH4RGL2OR10C1RPS18OR12D2SLC39A7OR12D3STK19OR5U1TNFOR5V1TUBBPPT2ZFP57PSORS1C1PSORS1C2RNF39TAP1TAPBPTCF19TNXBTRIM31TRIM40UBDVARSVARSLGene coding sequences may be invariable (no recorded variation), have synonymous variation only (variation at the nucleotide but not the peptide level) or have non-synonymous variation (variation at both the nucleotide and peptide level), which in turn, may be conservative or non-conservative variation according to the criteria of positive or negative values in the BLOSUM62 matrix. The main coding variant is that numbered 001 in the VEGA database except for LY6G6E and HLA-DPB2 where the main variant is not coding. C4A and C4B were excluded from calculation of variation because the order of these genes in the PGF sequence precluded alignment with other haplotype sequences. Nevertheless, alignment of the coding sequences for each gene separately showed that there were non-synonymous, non-conservative variations. HLA-DRB5 is present in this study only in the PGF haplotype and, therefore, here appears invariableaCoding genes where the main variant does not harbour non-conservative, non-synonymous variation but other variants do (BAT1 BRD2 DDR1 C6orf136 HLA-DOA MOG KIFC1 and TRIM39).bSimilarly, coding genes where the main variant does not harbour conservative non-synonymous variation but other variants do (PSMB8).
As well as the variations reported above, major indels revealed as breaks in cross_match discrepancy lists and analysed by RepeatMasker are given in Table 10. Many of these have been previously reported (Dangel et al. 1994; Dunn et al. 2003; Dunn et al. 2002; Gaudieri et al. 1999; Horton et al. 1998; Kulski and Dunn 2005; Stewart et al. 2004). These indels were most frequently but not exclusively associated with AluY elements.
Table 10Major indels in the form of retrotransposible elementsChr6 pos’nFlanking lociPresence in haplotypeDetailsPGFCOXQBLSSTOAPDDBBMANNMCF29002370TRIM27:C6orf100CCCC??CCComplex region (A)29440424OR5V1:OR12D3✓✓?✓??XXAluYa529784097C6orf40:HCP5P15✓X✓✓?XXXAluYa5/8 175..30429788451Within HCP5P15XX✓X?✓✓XAluYa5/8 176..31029794763HCP5P15:HLA-F✓XX✓?XXXSVA_E plus simple rpt.s29922942HLA-G:MICF✓X✓✓✓✓✓✓L1ME3B 5940..616529954495MICF:HLA-H✓XXXXXXXHERVK9 inserted in MER930008633HLA-K:HLA-21✓XX✓XX✓?SVA E/F plus simple rpt.30106475HCG8:ETF1P1X✓XX✓✓XXAluYb830547387SUCLA2P:RANP1XXX✓?XX?AluJb 1..283 and parts of MLT1D/L1PBa31079582C6orf205:HCG22XX✓XXX?XAluYb8 37..29731117638C6orf205:HCG22✓XX✓✓X✓✓AluY (whole & part) and MER63 1017..106231301931HCG27:HLA-C✓✓X✓?✓✓✓HERV3 part (6489...7339)31320352HCG27:HLA-C✓XXX?XXXSVA_F 349..850 plus GC rich rpt.31358220RPL3P2:WASF5PXX✓X?XXXAluY 35..30631400900WASF5P:HLA-B✓✓✓✓?XXXAluSp plus L1PREC2 part (3205...4617)31405648WASF5P:HLA-B✓X✓✓?XxxHERVIP10F (part) and AluSg (only cf CX DB)31418854WASF5P:HLA-B✓✓✓✓?✓✓XL1PA5 part (5503..5876)31530995MICA:HCP5✓X?✓??X?SVA B/F plus simple rpt.s32421915within C6orf10✓XX✓XX✓XAluYb832486228BTNL2:HLA-DRA✓✓✓✓✓XXXL1P1/L1HS parts32655545HLA-DRB1 intron 5✓xxX?✓✓?AluYa5 within more or less partial LTR1232660731HLA-DRB1 intron 1X/X✓/XX/X✓/✓?✓/✓✓/✓?Tigger4/AluSx32661119HLA-DRB1 intron 1CCCC?CC?Complex region (B)32663167HLA-DRB1 intron 1X/✓✓/✓✓/✓✓/X?✓/X✓/X?AluSq/AluY32669534HLA-DRB1:HLA-DQA1CCCC?CC?Complex region (C)32679461HLA-DRB1:HLA-DQA1✓XXX?XX?AluY32693271HLA-DRB1:HLA-DQA1✓✓✓✓?X✓?L1PA4 (parts)32697545HLA-DRB1:HLA-DQA1XXXX?✓✓?L1HS 7..603232701428HLA-DRB1:HLA-DQA1✓X✓✓?xXxL1PA2 part and from CX: MER2B and AluY32728179HLA-DQA1: HLA-DQB1CCCC?CCCComplex region (D)32739664within HLA-DQB1XX✓X?X✓XAluY32743646HLA-DQB1: MTCO3P1XXXX?✓XXLTR1332746780HLA-DQB1: MTCO3P1XXXX?✓X✓L1PA4 (parts)32751442HLA-DQB1: MTCO3P1XXXX?X✓XLTR5_Hs32753489HLA-DQB1: MTCO3P1✓✓✓✓?X✓XL1PA10 268..4888 around L1PA4 (part)32756020HLA-DQB1: MTCO3P1XXXX?X✓XLTR5_Hs32764047HLA-DQB1: MTCO3P1✓✓✓✓?X✓XAluSx32765930HLA-DQB1: MTCO3P1XXXX?X✓XAluYa532785062MTCO3P1:HLA-DQB3✓✓✓✓?XXXTigger4 (Zombi)/L1HS (parts) and T-rich32795150MTCO3P1:HLA-DQB3XXXXX✓X✓AluY32796573MTCO3P1:HLA-DQB3XXXXX✓X✓AluY32815974HLA-DQB3: HLA-DQA2X✓XX✓XXXAluYa532857369HLA-DQB2:HLA-DOB✓X✓✓XX✓XAluYg632881426HLA-DQB2:HLA-DOBXX?✓✓XX?AluYa532887265HLA-DQB2:HLA-DOB✓X?XXX✓✓LTR42 and parts of L1MC5 and AluSc 3..10533201559within HLA-DPB2✓XXX✓?X?AluYb833234360HCG24:COL11A2✓✓✓??✓✓XAluY (1..293) AluJb (26..306)Where there was a break in the cross_match discrepancy list match between two clones, the inserted sequence was extracted and subjected to analysis by RepeatMasker to assess the number of major indels that were a result of retrotransposible elements. Chromosome 6 position (NCBI35/36) of the inserted sequence was that of the midpoint where the sequence was an insertion in PGF or the position before the deletion in PGF. Flanking loci were retrieved from the annotation. Insertion in a haplotypes is indicated by ‘✓’, deletion by ‘X’, complex regions by ‘C’. Where there is a sequence gap in a haplotype corresponding to the indel, this is shown by ‘?’. Four complex deletion/insertion events are listed: A, B, C and D; for details, see text.
Four of these major indels were complex and designated as complex regions A, B, C and D in Table 10. They include three known regions from the comparison of the PGF and COX haplotypes (Stewart et al. 2004). Complex region A (involving MIR, MER41B, MER115, AluSx, Flam_C, AluSg, AluY, AluSx, L2 and MER38 elements) maps between TRIM27 and C6orf100 and was found to be deleted in COX but present in PGF, QBL, SSTO, MANN and MCF. Complex region B (involving L2 and AluY elements) maps to intron 1 of HLA–DRB1 and was also found by comparing PGF with COX, QBL, DBB, MANN and SSTO. Complex region C (SVA and low-complexity repeat elements) maps between HLA–DRB1 and HLA–DQA1 and was noted in COX as a deletion of the SVA and low-complexity repeats. Whereas, DBB, MANN and SSTO displayed the same deletion, as well as a telomeric deletion of AluSx/MIRb, QBL had both deletions plus that of an intervening 2.5 kb sequence containing Alu, L3 and MLT1A1 elements. Complex region D maps between HLA−DQA1 and HLA−DQB1 and is more complicated than previously reported. At the telomeric end, PGF lacks an L1PA4 fragment of >300 bp that is present in COX, QBL, SSTO and MCF and is also absent in DBB and MANN where it is interrupted by about 1.3 kb of SVA sequence. Centromeric to this PGF contains an AluSx, an AluY and an AluYd2, flanked by long interspersed nuclear element repeats, all deleted in the other haplotypes. Further towards the centromere there is an L1MA7 fragment, into which in PGF alone there are insertions of an AluSx followed by an AluY; a subsequent AluSg present in all haplotypes contains an insertion of 795 bp of SVA sequence in just COX and QBL. Finally, at the centromeric end of this region, PGF uniquely contains intact MER11C and LTR5 elements.
Representation of haplotypes within European populations
The eight haplotypes analysed in this study were selected on the basis of their association with type 1 diabetes and multiple sclerosis and their high population frequencies. To determine how representative these haplotypes are with respect to SNP haplotypic diversity in a population, we determined their distribution in the haplotypic tree space in the European population.
For this analysis, we selected a segment of ~214 kb, spanning the HLA–DRB1 and HLA–DQB1 genes in a population of European ancestry with known HLA allelic data (de Bakker et al. 2006). Phylogenetic analysis of 180 founder haplotypes derived from genotypic data (54 substitutions) shows that the eight haplotypes selected as part of the MHC Haplotype Project share identical HLA alleles over most of the tree space (Fig. 3a), representing almost the entire variation observed in the population assayed with the exception of two branches (DRB1*1103–DQB1*0301 and DRB1*0101–DQB1*0501).
Fig. 3Clusters of haplotypes in the European haplotypic diversity. Phylogenetic relationship of 180 founder SNP haplotypes from CEPH trios spanning a 214-kb segment of the MHC class II region, including the HLA-DRB1 and HLA-DQB1 genes (54 substitutions from rs2187823 to rs2856691). a Sequenced haplotypes are widely distributed in this NJ tree and represent the vast majority of the variation in the population sampled. Four-digit alleles are indicated for the corresponding DRB1 and DQB1 genes in each haplotype ID label to highlight the HLA haplotypic distribution based on the underlying nucleotide variation. The NJ tree was constructed using pairwise genetic distances considering the Kimura 2-parameters model without correction for rate variation among sites as implemented in the MEGA2 software (Kumar et al. 2001). b Each haplotype sequenced is associated to a single haplotype cluster. This phylogenetic network (Bandelt et al. 1999) also shows that clusters (shaded area) are constituted by one central haplotype and its derivatives. Circles represent individual haplotypes, and the size of the circle is proportional to the haplotype frequency. The length of the lines connecting nodes is relative to the distance between them, e.g. distances within shaded areas (clusters) never exceed three mutation steps. Cluster of haplotypes sharing HLA alleles with sequenced cell lines are named accordingly: COX and QBL: DRB1*0301 DQB1*0201–PGF: DRB1*1501 DQB1*0602–APD: DRB1*1301 DQB1*0603–MCF: DRB1*0401 DQB1*0301–DBB: DRB1*0701 DQB1*0303–SSTO: DRB1*0403 DQB1*0302–MANN: DRB1*0701 DQB1*0202. HLA haplotypes DRB1*1103–DQB1*0301 and DRB1*0101–DQB1*0501 indicate the two major haplotype clusters not represented in the MHC haplotype project data
Haplotype diversity in this sub-population is restricted to relatively few haplotype clusters (Fig. 3b). Each cluster consists of a founder haplotype, depicted by the most frequent and centrally located haplotype within the cluster. Recently derived haplotypes show lower frequencies and are connected to the central haplotype by relatively few mutation steps (in this case, up to three). This phylogenetic network clearly shows that all the sequenced haplotypes occupy central positions in their respective haplotypic groups. Inferences about phylogenetic relationships between haplotype clusters are, however, only approximate as a consequence of recombination events.
It should also be noted that SNP haploptypes derived from CEPH pedigrees of European ancestry by no means represent an exhaustive sampling of European diversity. Nevertheless, the sampling has been shown to represent the European population in the UK reasonably well (Ke et al. 2005). In conclusion, our analysis demonstrates that the HLA haplotypes selected for the MHC Haplotype Project are ancestral haplotypes, representative of MHC diversity in the European population.
Conclusion and outlook
The MHC Haplotype Project has succeeded in providing a new public resource for immune-linked disease and population genetic studies. First reports from studies using the resource indicate that it adds significant power to the identification and fine-mapping of disease-associated variations (Yeo et al. 2007). The data have also contributed to the recent identification of a first set of HLA tag SNPs, which hold great promise for future applications in clinical settings, e.g. to complement or replace classical HLA-typing in transplant medicine (de Bakker et al. 2006). While costs and other limitations of the current (capillary) sequencing technology have restricted our study to only few (eight) MHC haplotypes, the number of new variations found, combined with the fact that no variation plateau has yet been reached, indicates that there are many more variations to be discovered. The recent introduction of several new and massively parallel sequencing platforms (for review, see Bentley 2006) has created the opportunity to do just that by re-sequencing haplotypes and, eventually, entire genomes at the population level and as integral part of case control studies. Because of its wide-ranging medical importance, the MHC can be expected to be among the first regions of the human genome to be sequenced in this way. Such sequencing will provide the critical, and until now missing, data to identify causal variations and their underlying mechanisms on an unprecedented scale.

haplotype
major histocompatibility complex
polymorphism
population genetics
retroelement
genetic predisposition to disease