Strategies to work with HLA data in human populations for histocompatibility, clinical transplantation, epidemiology and population genetics: HLA-NET methodological recommendations

 

# Introduction
Our knowledge of the genetic diversity of the human species has expanded considerably in recent decades, thanks to the rapid progress in genomic research. The possibility of genotyping individuals at high resolution over the entire genome [bib_ref] A map of human genome variation from population-scale sequencing, Altshuler [/bib_ref] , and specifically the Major Histocompatibility Complex (MHC), through a thorough characterization of DNA sequence variation at human leukocyte antigen (HLA) genes [bib_ref] IMGT  HLA and IMGT  MHC: sequence databases for the study..., Robinson [/bib_ref] has been crucial in addressing major issues related to biomedicine and molecular biosciences, such as the assessment of genetic susceptibilities to diseases [bib_ref] Genetic susceptibility to infectious disease, Segal [/bib_ref] [bib_ref] A high-resolution HLA and SNP haplotype map for disease association studies in..., De Bakker [/bib_ref] , the control of haematopoieic stem cell and organ transplantation [bib_ref] The role of HLA matching in hematopoietic cell transplantation, Hansen [/bib_ref] [bib_ref] Tissue typing in support of unrelated hematopoietic cell transplantation, Petersdorf [/bib_ref] , the appraisal of the genetic structure of human populations and its meaning and the understanding of genomic evolution in relation to the environment [bib_ref] How selection shapes variation of the human major histocompatibility complex: a review, Meyer [/bib_ref] , for a review; [bib_ref] Distinct evolutionary strategies of HLA loci in pathogen-rich environments, Sanchez-Mazas [/bib_ref] , among other essential topics. A common goal of these studies is to estimate HLA genetic diversity within and among human populations and to describe it through the molecular typing of population samples.
In this context, a main challenge of tissue typing (or histocompatibility) laboratories involved in clinical research (donor-recipient matching) is to produce HLA molecular data of high quality. Such laboratories address a crucial health problem in modern societies, the need for haematopoietic stem cell transplantation involving the search for HLA compatible donors. Haematopoietic stem cell volunteer donors are generally recruited randomly in each country with an effort to constitute very large registries reflecting the HLA variation over different regions (e.g. [bib_ref] Assessment of optimal size and composition of the U.S. National Registry of..., Kollman [/bib_ref] [bib_ref] Regional differences in HLA antigen and haplotype frequency distributions in Germany and..., Schmidt [/bib_ref]. In addition, some registries specifically aim to improve recruitment from ethnic minorities (e.g. [bib_ref] Efforts of the United States' National Marrow Donor Program and Registry to..., Johansen [/bib_ref] to increase the HLA diversity and hence the probability of finding an appropriate donor for a given patient. In this context, knowledge of the distribution of alleles and haplotypes in many different population groups as determined by high-resolution typing may allow the design of more efficient recruitment algorithms.
The accurate description of allelic and haplotypic HLA profiles and the identification of rare HLA variants in human populations is not only crucial to recipient-donor matching and research projects on histocompatibility. In addition, researchers in at least two other disciplines share related objectives. Firstly, as HLA genes play an essential role in susceptibility or resistance to serious human diseases [bib_ref] HLA and Disease: The Molecular Basis, Svejgaard [/bib_ref] [bib_ref] HLA and infectious diseases, Blackwell [/bib_ref] , such as HIV [bib_ref] HLA and HIV-1: heterozygote advantage and B*35-Cw*04 disadvantage, Carrington [/bib_ref] [bib_ref] Adaptation of HIV-1 to human leukocyte antigen class I, Kawashima [/bib_ref] [bib_ref] The major genetic determinants of HIV-1 control affect HLA class I peptide..., Pereyra [/bib_ref] , their meticulous molecular analysis underpins epidemiological research. Statistically reliable compari-sons between case and control population samples are needed to assess the susceptibility (or resistance) conferred by specific HLA alleles. Knowledge of the prevalence of a susceptibility allele in a given population is crucial to evaluate the genetic risk provided by several different HLA alleles in autoimmunity, infectious diseases or allergic reactions to drugs.
Secondly, HLA genes are of particular interest from a population genetics point of view to study the genetic history of the human species and the mechanisms of molecular evolution [bib_ref] Signatures of demographic history and natural selection in the human major histocompatibility..., Meyer [/bib_ref]. Different human populations exhibit different HLA genetic profiles. This is partly explained by the geographic dispersal of modern humans throughout the world and partly by an effect of natural selection [bib_ref] Signatures of demographic history and natural selection in the human major histocompatibility..., Meyer [/bib_ref] [bib_ref] Balancing selection and heterogeneity across the classical human leukocyte antigen loci: a..., Solberg [/bib_ref] [bib_ref] Immunogenetics as a tool in anthropological studies, Sanchez-Mazas [/bib_ref]. Indeed, the evolution of HLA may be driven by an advantage of specific alleles but also by an advantage conferred to heterozygous individuals against a large variety of pathogens [bib_ref] Pathogen-driven selection and worldwide HLA class I diversity, Prugnolle [/bib_ref] [bib_ref] Distinct evolutionary strategies of HLA loci in pathogen-rich environments, Sanchez-Mazas [/bib_ref]. A precise knowledge of the distribution of allele frequencies in many different populations may help to understand human peopling history and the interaction of populations with their environment in a pathogenic context.
HLA-NET (http://hla-net.eu), a European network of laboratories involved in the study of HLA for histocompatibility, epidemiology and  or population genetics, was created in 2009 to achieve highly significant goals in the present research context. Despite their different objectives and applications, all laboratories involved in this network are united by a common research task, the description of HLA molecular diversity in human populations, to get accurate reference data for their own studies in different disciplines and to provide pan-European data to research groups working internationally. Moreover, these laboratories are concerned with similar types of methodological problems raised by the complexity of HLA polymorphism, i.e. how can a population sample for different applications be defined accurately? How can data be generated that are comparable to those of other laboratories? How can gene frequencies and other statistics with highly complex data be estimated? What legal and ethical rules should be followed to harmonize with national requirements? HLA-NET is designed to answer those questions via standardization of protocols and procedures and the development of an electronic platform to collect, handle, store and process HLA data and share information amongst European laboratories. Its final objective is to improve qualitatively and quantitatively the collection of HLA-typed population samples all over Europe and surrounding areas and to produce a consensual map of HLA molecular diversity for this broad geographic region.
This article reports the achievements and provides the main recommendations of HLA-NET at the mid-term of its activities. The results are presented by four work-ing groups (WGs) addressing crucial questions related to the main issues mentioned above: population definitions and sampling strategies for population genetics analyses (WG1), HLA typing standards for population genetics analyses (WG2), bioinformatics strategies for HLA population data storage and analysis (WG3) and ethical issues (WG4). A list of laboratories contributing to the HLA-NET project is also presented.

## Wg1 -population definitions and sampling strategies for population genetics' analyses

## Aims of group
Working group 1 (WG1) aims at improving the quality of population data used in HLA-related studies in terms of population definition and sampling and at coordinating the collection of HLA-typed population samples from Europe and surrounding areas.

## Population definition and sampling
The establishment of standardized procedures and questionnaires for collecting and databasing HLAtyped population samples is essential to fill in the current lack of comparability among different studies pursuing similar goals: a reliable estimation of HLA gene frequencies in samples of healthy individuals to compare with patients suffering from severe diseases (HBV, HIV, rheumatoid arthritis, etc.), a reliable estimation of HLA gene frequencies in ethnically or geographically well-defined populations to reconstruct human peopling history or a reliable identification of rare HLA alleles or multilocus haplotypes in distinct populations to optimize the search of potential donors in haematopoietic stem cell transplantation.
An important issue is the definition of populations from an 'anthropological' point of view. The group decided to avoid a priori misclassifications of racial and ethnic groups in both questionnaires and databases and to consider several levels of description related to geographic origin, language(s) spoken and any other relevant information on the ancestry of each studied population. Outdated racial or ethnic definitions like 'Caucasian' are to be replaced with ethically acceptable alternative names.
'Caucasian': a meaningless definition. HLA-NET recommends avoiding the term 'Caucasian', as well as its derivatives 'Caucasoid' and related terms. To understand the reasons of this recommendation, one has to bear in mind the complex history of European populations and their present biological and cultural diversity.
There have been difficult discussions among geneticists on the proportion of Palaeolithic, Mesolithic or Neolithic ancestry of European populations going back to very different periods of time (some 40 000, 18 000 or 10 000 years ago, respectively) (e.g. [bib_ref] A predominantly neolithic origin for European paternal lineages, Balaresque [/bib_ref] [bib_ref] Clines of nuclear DNA markers suggest a largely neolithic ancestry of the..., Chikhi [/bib_ref] [bib_ref] Y genetic data support the Neolithic demic diffusion model. Proceedings of National..., Chikhi [/bib_ref] [bib_ref] Tracing European founder lineages in the Near Eastern mtDNA pool, Richards [/bib_ref] [bib_ref] The Genetic Legacy of Paleolithic Homo sapiens sapiens in Extant Europeans: A..., Semino [/bib_ref] , and such controversies have also been raised by analyses on ancient DNA [bib_ref] Comment on ''Ancient DNA from the first European farmers in 7500-year-old Neolithic..., Ammerman [/bib_ref] [bib_ref] Population genetics: DNAs from the European Neolithic, Barbujani [/bib_ref]. Even the proportion of Neandertal contribution to the genetic pool of modern Europeans is currently disputed, ranging from no contribution to around 4% of interbreeding between Neandertals and modern humans [bib_ref] Modern humans did not admix with Neanderthals during their range expansion into..., Currat [/bib_ref] [bib_ref] No evidence of neandertal mtDNA contribution to early modern humans, Serre [/bib_ref] [bib_ref] A draft sequence of the Neandertal genome, Green [/bib_ref]. Although genetic studies do not yet provide firm conclusions to these issues, archaeological data show that the migrations of Neolithic farmers from the Near East led to major transformations in diverse aspects of European life styles . Also, the significant HLA genetic structure observed in presentday Europeans may possibly trace back to that period [bib_ref] HLA-C molecular characterization of a Lebanese population and genetic structure of 39..., Buhler [/bib_ref].
Europe has been subjected to heterogeneous climates in the past and is nowadays characterized by temperate to cold temperatures, marked seasons and highly variable environments. Present-day Europeans are characterized by a huge phenotypic diversity with pronounced differences, for example, in hair and eye colour and body height (with small and tall populations). Even skin colour varies from relatively dark in some southern populations to very light in the north. Such phenotypes were most probably shaped by adaptive selection to different environments [bib_ref] Genome-wide detection and characterization of positive selection in human populations, Sabeti [/bib_ref] [bib_ref] Molecular genetics of human pigmentation diversity, Sturm [/bib_ref] although the intensity of selection may have varied greatly among different traits. Some other phenotypic traits, which are not visible to the naked eye because they concern specific molecules involved in internal metabolic pathways, exhibit unusual patterns in Europe. This is the case for lactase persistence: most southern Europeans cannot digest milk in adulthood (like most people in the world) while northern Europeans are perfectly adapted to milk consumption, and this is because of loss of activity of the lactase enzyme after weaning in the former [bib_ref] Lactose digestion and the evolutionary genetics of lactase persistence, Ingram [/bib_ref]. This trait has evolved partly through natural selection, in coevolution with animal domestication and  or through an effect of climate, and partly as a consequence of the demographic expansions occurring during the Neolithic period [bib_ref] Impact of selection and demography on the diffusion of lactase persistence, Gerbault [/bib_ref] [bib_ref] Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality..., Gerbault [/bib_ref]. It also illustrates the high level of genetic diversity of European populations, with a frequency of the lactase persistence allele varying from 0 to almost 80% from south to north.
Europe also exhibits a high cultural complexity, reflected, for example, by the diversity of the languages that are spoken today in this continent. There are almost 50 languages belonging to a dozen families, some of which belong to unrelated linguistic phyla including Indo-European, Uralic and Basque (http://eth nologue.com). The origin of this diversity is not yet fully understood: for example, there are competing theories on the origin of Indo-Europeans (do they come from the Near-East or from the north of the Black Sea, or both?) [bib_ref] Farmers and their languages: the first expansions, Diamond [/bib_ref] [bib_ref] Language-tree divergence times support the Anatolian theory of Indo-European origin, Gray [/bib_ref] [bib_ref] Search for the Indo-Europeans, Balter [/bib_ref] and the origin of some isolated populations, such as Basques, is still uncertain.
The history of Europe and its surrounding areas is so complex and its population diversity so high that the use of a unique term, 'Caucasian', to describe all populations from Europe and its surrounding areas is a crude simplification, which is clearly not appropriate. Actually, the term 'Caucasian' was first used by the German naturalist Johann Friedrich Blumenbach at the end of the 18th century [bib_ref] Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality..., Gerbault [/bib_ref]. Blumenbach, during his journeys, found that the people, and more particularly the women, living in the Caucasus were exceptionally wonderful. In his famous book on The unity of the human genus and its varieties published in 1795, he thus described the European variety as the 'Caucasian' variety. Later on, the term 'Caucasian' (or its derivatives 'Caucasoid', 'Aryan', etc.) remained in the anthropological classifications to describe a prototype of Europeans (obviously influenced by a racist ideology, with dramatic consequences during world history).
For such reasons, the terms 'Caucasian' and its derivatives have to be deleted from the scientific vocabulary. HLA-NET proposes to replace them by the following substitutes, depending on each specific situation:
1 'Europeans', for populations of European origin living in Europe; 2 'populations of European descent', for populations of European origin not living in Europe; 3 'populations from (where they are from) living in Europe', for populations of non-European origin living in (where they live) in Europe; 4 'North Africans', 'West Asians', 'populations from the Near East' and other geographic names when populations from these areas surrounding Europe are concerned; 5 'pan-Europeans', if a general expression is needed to name at the same time the populations from Europe and those from its surrounding areas North Africa, the Near East and Western Asia.
'Black', 'Mongoloid' and other outdated and connoted terms. Because HLA-NET is a European Action focusing on the HLA molecular characterization of pan-European populations, we concentrated our discussion above on the biological and cultural diversity of Europeans and the misuse of the term 'Caucasian'. However, our network is also aware that other outdated terms are commonly used to name groups of populations from other continents and recommends avoiding them: 'Black' or 'African Black' (or even 'Negroid') are terms inherited from several centuries (18 th to 20th) of colonial (and racist) anthropology (see, for example, The Outline of History of Mankind, by polygenist Christoph Meiners, published in German in 1785). Nevertheless, they are still frequently used by researchers to name sub-Saharan Africans, because of the generally very dark skin of these populations. Here again, time has come to definitively abandon such appellations, which do not correspond to any scientific classification. Sub-Saharan African populations are highly diverse from a biological point of view, both in terms of genetic variation (as most genetic studies have largely demonstrated) and variation of some quantitative traits including, for example, cranial measurements and hair shape [bib_ref] Shape variability and classification of human hair: a worldwide approach, De La Mettrie [/bib_ref]. Although skin colour may also vary significantly in sub-Saharan Africa (e.g. between East and South Africans, Khoisan, Pygmies, etc.), this trait has followed a more peculiar evolution which has been strongly governed by latitude-dependent natural selection (see, for example, [bib_ref] Human pigmentation variation: evolution, genetic basis, and implications for public health, Parra [/bib_ref] [bib_ref] Understanding the evolution of human pigmentation: recent contributions from population genetics, Rees [/bib_ref] , explaining its unusual diversity pattern throughout the world . As a result, very dark-skinned people exist in all continents, from Africa to Australia via India, Southeast Asia and Melanesia. Taking language as a cultural marker, Africa is also highly diverse from a cultural point of view, grouping 30.5% of the total world languages (http:// www.ethnologue.com) and four main linguistic phyla, the dispersal of which reveals a complex history of this continent [bib_ref] Genetics and history of Sub-Saharan Africa, Excoffier [/bib_ref]. Similar to 'Caucasian', HLA-NET thus recommends using terms other than 'Black Africans' and derivatives, such as:
1 'sub-Saharan Africans', for populations of African origin living south of the Saharan Desert; 2 'North Africans', for populations of African origin living north of the Saharan Desert; 3 'West Africans', 'South Africans', 'East Africans', or even more detailed geographic names, for populations of African origin living in the respective geographic areas; 4 'populations of African descent', for populations of African origin not living in Africa.
'Mongoloid' is also used today in anthropology, although less frequently than 'Caucasian' and 'Black African'. It is based on apparent similarities of phenotypic traits (such as the epicanthic fold of the eye, very pronounced in populations from Mongolia) between all Asian populations, just as 'Black' refers to skin colour resemblances. Like 'Caucasian', 'Mongolian', which is actually correct to name the inhabitants of Mongolia, but not to name a human race, was used by Meiners and Blumenbach in their racial classifications. Both 'Mongoloid' and 'Mongolian' (taken in that sense) are again unfortunate relics of the reductionist views on human variation prevailing in the last centuries. In agreement with the most commonly used expressions today, we thus propose to replace these terms and their derivatives by the following appellations:
1 'Asians', for populations of Asian origin living in Asia; 2 'West Asians', 'South Asians', 'East Asians', 'Southeast Asians', 'Northeast Asians', or even more detailed geographic names, for populations of Asian origin living in the respective geographic areas; 3 'populations of Asian descent', for populations of Asian origin not living in Asia.
HLA-NET population data questionnaire. WG1 has worked on a standard questionnaire to characterize populations and the population samples collected for HLA typing, which have to be representative and statistically reliable. This questionnaire is available at http://hla-net.eu/population_questionnaire and shown in Appendix 1. Note that it has been used as a standard document for AHPD (Analysis of HLA Population Data), a project of the 16th International Histocompatibility and Immunogenetics Workshop (IHIW). Basically, one has to provide, for each sample tested (or to be tested) for HLA:
1 The type of study (i.e. origin of the sample): in principle, the population samples of interest for this project are to be defined on specific criteria based on anthropological field studies (see below points 1-4); however, for statistical reasons related to the number of available samples and of individuals per sample, bone marrow registry data can also be considered and used under clear-cut conditions. Also, collection of patients, although generally not used for studies in anthropology, may be useful at a later stage if a specific epidemiological project is undertaken. They are thus not excluded a priori. The information on the type of study is important to know whether a given sample may include individuals of diverse origins or who share some peculiar characteristics (e.g. to suffer from a given disease). Any deviation from Hardy-Weinberg equilibrium or other unexpected result may then be better understood. Other important information is the presence of close (first-degree) relatives in the sample, as this may impair the estimation of gene frequencies and Hardy-Weinberg equilibrium. The inclusion of more remote relatives (cousins, etc.) may also introduce some bias but cannot be avoided, in particular if samples are taken from isolated populations studied in the field, which are often highly endogamous. This is why we only require a priori the exclusion of firstdegree relatives.
2 The name of the population represented by the sample: we propose the Ethnologue as an excellent guide to find consensual and alternative names of the populations under study (although these are linguistic names, they most often correspond to the used ethnic names). Some alternative names (e.g. names given by the population to itself or by close neighbours) may only be known by investigators working in the field and should also be mentioned.
Of course, population names may be more difficult to assign in the case of samples of donors or patients. Then personal comments from the principal investigator are welcome. In any case, HLA-NET recommends avoiding outdated racial names like 'Caucasian', 'Black', 'Mongoloid' and their derivatives (see above). 3 The geographic location of the population: this has to be filled in detail (including latitude and longitude). A crucial issue is to know whether a population has been sampled in its 'original' location or not (e.g. Chinese living abroad). Of course this 'original' homeland may be traced back to only one or to many generations (e.g. back to the 15th century for Americans of European descent, etc.). Detailed information has to be provided in complicated cases. 4 The language spoken by the population: this should be filled with the help of the Ethnologue (http://www.ethnologue.com). Some redundancy may appear with the name of the population (see point 2 above), but here crucial information is required concerning the linguistic family.
The same questionnaire then asks information on the source of DNA samples and HLA typing, and on basic ethical issues. Detailed comments on these aspects will be found below in chapters WG2 -HLA typing standards for population genetics analyses and WG4 -Ethical issues. A delicate question is that of the number of individuals tested. We previously proposed a minimal threshold of 100 individuals and minimal sample sizes should be kept as close as possible to this threshold. Note, however, that more individuals per sample will allow detecting more alleles (eventually new ones) and will provide much better frequency estimates.

## Collection of population data
A final objective of HLA-NET is to create a consensual map of the HLA molecular diversity of European populations in a broad sense. The population data to include as part of the HLA-NET project thus concern in priority:
1 European populations; 2 Populations from surrounding areas, i.e. North Africa, West Asia, Near-East;
3 Populations from other regions of the world but related to Europe, i.e. local minorities of European countries such as Congolese in Belgium, etc.
However, HLA-NET is closely related to other projects conducted at the international level, like the 'Analysis of HLA Population Data (AHPD)' project of the 15 th [bib_ref] A bioinformatics approach to ascertaining the rarity of HLA alleles, Middleton [/bib_ref] and 16 th International Histocompatibility and Immunogenetics Workshop, where populations from all continents are investigated with the aim to reconstruct human peopling history. Therefore, population samples from all regions of the world may be considered by HLA-NET for further collaborations. A preliminary list of laboratories participating in the Action and providing population or registry samples was created on the HLA-NET website through a wiki for continuous updating. The project started with a total of 14 European samples: Austrian, Belgian, Bulgarian, Bulgarian Gipsy, Croatian, Finnish, French, Greek, Italian, Norwegian, Norwegian Sami, Portuguese, Slovenian and Swiss [fig_ref] Table 1: Preliminary list of population  registry samples available for HLA-NET [/fig_ref]. Updates of the list will be found at http://hla-net.eu. Last but not least, the group benefited from the help of the European Federation for Immunogenetics (EFI, http://www.efi web.eu/) to call for participation by using its services (mailing list, EFI newsletter) and by inviting HLA-NET to organize special sessions during its annual conferences (Florence, May 2010; Prague, May 2011).

## Wg2 -hla typing standards for population genetics analyses
Aims of group A major aim of Working Group 2 (WG2) is to define standards for producing high-quality data for HLA genotyping and set up criteria for typing methods used for each population, thus allowing population comparisons in meta-analyses. These tasks involve careful comparisons of genetic typing methodologies and their ability to produce results at comparable resolution levels; they also address the search for strategies to handle ambiguous data and interpret heterogeneous HLA genotypes because of the very high level of complexity of this polymorphism and the adoption of universal and user-friendly formats.

## Reporting typing ambiguities
The group worked on the issue of reporting typing ambiguities in a format that is best suitable for haplotypic and allelic frequency estimation, intra-and interpopulation genetics analyses.
While the gold standard is exon 2 + 3 (class I) and exon 2 (class II) sequencing, populations may be analysed by other methods, such as reverse SSO hybridization on microbeads arrays (luminex technology). This latter method also targets exons 2 + 3 (class I) and exon 2 (class II) polymorphisms, although it can be extended to type for exons 4-7. It is ideally suited for typing large numbers of samples, but it leads to typing ambiguities in most cases, because of the ever increasing allelic polymorphism. Similarly, bi-allelic sequencing also leads to ambiguities that may be resolved using additional primers for the sequencing reactions when polymorphisms are located within the amplicon [bib_ref] Reanalysis of sequence-based HLA-A, -B and -Cw typings: how ambiguous is today's..., Voorter [/bib_ref]. Ambiguities involving polymorphisms located outside exons 2 + 3 (class I) or exon 2 (class II) require longer range PCR and additional sequencing reactions. Whatever the technique used it is recommended that all the ambiguities are reported. This is generally achieved using the National Marrow Donor Program (NMDP) coding system, i.e. abbreviation codes for the so-called ambiguous allele groups. Although certainly helpful for its original purpose of simplifying the identification of matched bone marrow donors, its use in practice increases artificially the number of allele pairs for a given genotype prior to haplotypic and  or allelic frequency estimation. To retain maximal information, it is strongly recommended to provide the list of allele pairs required to explain the genotype, as this will not include spurious allele pairs resulting from the expansion of the abbreviation codes. An example of the importance of defining an adequate ambiguity notation as a standard procedure is provided in for two alternative outputs proposed by the reverse SSO microbead array typing. Based on the above considerations, guidelines and recommendations of WG2 for reporting HLA typing ambiguities are given in Appendix 2 and can be found at http://hla-net.eu/reporting_HLA_typings_guidelines.
Based on the IMGT  HLA database, a list of ambiguities that comprise polymorphisms outside exons 2 + 3 for class I and exon 2 for class II at each HLA locus has been generated. In a second step each of these alleles differing outside the sequence defining the peptide-binding site was screened for its occurrence in available population databases. While a majority of these belonged to the rare or very rare allele groups, several alleles were identified as occurring at signifi-cant frequencies in different populations. A list of such alleles is shown in [fig_ref] Table 2: List of alleles [/fig_ref].
Whether the discrimination of these alleles has an input on population comparisons remains to be elucidated. Some data are already available showing that the relative frequencies of the DRB1*14:01 and 14:54 alleles differ widely among populations, with the DRB1*14:01 extremely uncommon in American populations from Asian descent but more frequent (up to 15%) in spanish speaking American populations. In Europe a recent survey of 106 German donors with DRB1*14:01  14:54 ambiguous ty- Abbreviated notaon  user friendly  but results in a big loss of informaon . Illustration of the importance of defining an adequate and standard notation procedure for ambiguities in two alternative outputs proposed by the reverse SSO microbead array typing method.
pings found 87.9% to be DRB1*14:54 [bib_ref] The frequency of DRB1*1454 in South German Caucasians, Furst [/bib_ref].

## Reporting rare alleles
As a 15th IHIW project, data were collected on the frequency of supposedly rare HLA alleles, and the final analysis showed that 40.8% of the 2977 HLA alleles (Release 2.23.00, Oct. 2008) have been sequenced only once and should therefore be considered as very rare [bib_ref] A bioinformatics approach to ascertaining the rarity of HLA alleles, Middleton [/bib_ref]. In a previous ASHI study, 27-30% of the HLA-A, B, C, DRB1 alleles have been classified as common (frequency >0.0001) or well-documented (observed at least three times) [bib_ref] Common and well-documented HLA alleles: report of the Ad-Hoc committee of the..., Cano [/bib_ref]. Through HLA-NET, rare alleles have been submitted to Derek Middleton and Faviel Gonzalez-Galarza and included in the http://www.allelefrequencies.net database . In total, 193 distinct alleles have been submitted [fig_ref] Table 3: Rare alleles contibuted to the http [/fig_ref] and 70 of these submissions allowed the confirmation of an allele which had never been reported after its initial submission to IMGT  HLA [bib_ref] IMGT  HLA and IMGT  MHC: sequence databases for the study..., Robinson [/bib_ref].

## Available population samples
A list of available HLA-typed European population samples has been provided by WG2 members and will be available for the project. As shown in [fig_ref] Table 1: Preliminary list of population  registry samples available for HLA-NET [/fig_ref] , 14 different populations were initially provided, with various typing techniques and number of individuals, but with high resolution typing for most populations. The list is currently being updated (see chapter WG1 -Population definitions and sampling strategies for population genetic analyses, point 2: Collection of population data).

## Wg3 -bioinformatic strategies for hla population data storage and analysis

## Aims of group
The complexity of the HLA polymorphism is due to the existence of hundreds or thousands of different alleles at various loci and because new alleles are constantly discovered. As a consequence, HLA population data are neither stored nor analysed in a standard way in different laboratories, which makes comparisons very difficult. To use the large amount of data produced by different laboratories, in an optimized and comparable way, public access to specific computer facilities, continuously updated in relation to the everincreasing HLA allelic record and new developments in data analysis, is required. Working group 3 (WG3) was charged of two types of tasks: first, to provide the computer infrastructure to HLA-NET and the minimal tools required to support the work of the other working groups and second, to develop the databases and computer tools required for storing and analysing the HLA data, in particular the statistical methods and computer programs necessary to validate and report data with the highest level of reliability.

## Hla-net infrastructure
The website of HLA-NET (http://hla-net.eu) is a wiki that is used to support all activities, e.g. scheduling meetings, reporting results, publishing documents, providing access to computer programs and disseminating information, among others. The wiki simplifies the participation of HLA-NET members to the project, making coordination possible for both small and large contributions, such as correcting typos or creating new sections of the site, respectively. In a further step, this integrated web platform will be connected to the databases that Derek Middleton and Alicia Sanchez-Mazas' groups are currently harmonizing . This harmonization is conceived in a way to include a number of computer programs for routine validations and analyses of HLA and other immunogenetics data. In this way, new data implemented in the future will be automatically processed according to HLA-NET standard recommendations. To understand such recommendations, we review below some crucial questions that we had to face in this part of the project.

## Dealing with heterogeneous, ambiguous and low sample-sized data
The data collections are of diverse types, i.e. both frequency and genotypic data, and the level of resolution is quite diverse. We believe that to maintain an acceptable balance between financial cost and precision of typings, most laboratories will continue to type HLA at intermediate resolution levels including ambiguities, at least until next generation sequencing is routinely used. Therefore, considerations related to the treatment of ambiguous data are not only of interest in the present but also in the foreseeable future. One aspect of this issue relates to the above-mentioned standardization and reporting HLA typing results (including the identification of kits, potentially typed and untyped alleles, and possible ambiguities), which is mainly a scientific task of WG2. Here, the role of WG3 is to provide computer facilities for the application of the corresponding recommendations. The programs being set up by WG3 are built on the GENE[RATE] tools (http://geneva.unige.ch/generate/), the formal specifications of which have been published by [bib_ref] A bioinformatics approach to ascertaining the rarity of HLA alleles, Middleton [/bib_ref] [bib_ref] Allele frequency estimation from ambiguous data: using resampling schema in validating frequency..., Nunes [/bib_ref]. At this level, two GENE programs are particularly useful:
1 phenotype to interpret raw phenotypes based on a given reactivity data file and a kit description file; and 2 transliterate to perform allele substitutions (e.g. to recode 2nd field (protein level, formerly 4-digit) alleles into 1st field (allele group level, formerly 2-digit) alleles) within a given dataset.
Another tool, uniformate, allows one to check the validity of the data format before using any other GENE[RATE] tool. In this vein, some work has been devoted to the adaptation of input formats to the guidelines recommended by WG2, and this adaptation now allows running programs for standard one-locus analyses (frequency estimation, Hardy-Weinberg equilibrium and neutrality testing). On the other hand, the feasibility of a fully automatic recoding (through the GENE transliterate tool) of more complex (i.e. multilocus) datasets is still progressing, as it faces the problem of the identification of the potentially typed and untyped alleles at each locus and their combination across distinct loci, as well as the standardization of the procedure across multiple samples during the same run.
An aspect of this issue relates to the use of heterogeneous and  or ambiguous data. Distinct samples collected at different times or typed with distinct techniques will not allow detecting the same alleles or specificities. Thus, to compare samples of distinct sources, the first step is to define the common set of alleles over which to work. For each allele, we face two extreme situations: the use of the unspecific first field (formerly 2-digit) allele group and the use of the precise allele defined at the highest resolution. Careful scrutiny of the data generally provides an intermediate solution where the most common allele pool between several samples includes both 'broad' lineages for some alleles and highly precise definitions for others. Actually, within the framework of a HLA-NET-related research project, we produced a set of programs ('SPLIT-TEST') that provide help in screening the raw data and setting the common allele pool of a collection of samples. Recently, this 'broad-split' computer tool has been applied successfully to study the HLA molecular diversity of the Swiss bone marrow donors' registrees .
A challenge of this kind of work is to use information as detailed as possible at the allelic level without compromising statistical power and without making too many false-positive identifications. These two problems have become significant with the advent of high-resolution typing because in this case, the vast majority of the samples tested are too small in size to allow an accurate identification of all existing alleles. This situation is even worse when typing ambiguities are taken into account (see discussion on the use of NMDP codes on paragraph 2.1). In this context, WG3 is thus also tackling the important issue of 'sample size and number of alleles'. We are currently adapting a tool that will make easy to estimate sample size thresholds and which will complete the efforts of WG1 working on population sampling. It is worth stressing that in general low allele or haplotype frequencies are poorly estimated when sample sizes are small and should be considered with caution. Even detected alleles may actually be 'nonsignificant' from a statistical point of view, depending on the sample size. A very rough number for the minimal frequency of a 'significant allele' is given by the confidence interval of the allele frequency obtained by normal (either two-tail or one-tail) approximation (which is a standard statistical practice) or binomial distribution. For instance, for a sample size of 50 individuals, all frequency estimates smaller than 3.85% are 'nonsignificant' frequencies, i.e. not significantly different from zero, because zero is inside the two standard deviations' confidence interval ; in the same way, a 1% frequency is only 'significant' for a sample larger than 200 individuals. Therefore, because of the existent sampling conditions where low sample sizes are usually the rule, HLA-NET strongly recommends to avoid discussion on the 'number of alleles present' or 'the presence or absence of . Allele frequency thresholds (in %) below which the 95% confidence interval contains 0, as a function of sample size (N) and sampling model: I) standard normal two-tail; II) normal one-tail; III) exact binomial. Alleles exhibiting these and smaller allelic frequencies have probabilities larger than the usual 5% of being missed (0 alleles) in samples of the corresponding sizes given alleles' in the populations where the samples were drawn. Also, rather than to fix a minimal sample-size threshold, a reasonable advice that can be given is to use samples as large as possible.

## Population genetics with ambiguous data
Having mentioned the efforts to determine the actual allele pool that can be used in a study, we now briefly report the adaptation of the population genetic methods used for routine analyses. The former (15th) IHIW workshop's AHPD project held in Brazil in 2008 provided the framework to develop and test the GENE tools and their adequacy to the treatment of ambiguous data, and these tools were further expanded and generalized within the context of HLA-NET. Besides their specific abstractions (data structures) used to capture ambiguous genetic data and the definition of probability vectors to represent each individual's data, the main characterization of these programs is the use of resampling schemas to identify the sampling distribution of each statistic (e.g. homozygosity and linkage disequilibrium) of interest [bib_ref] Allele frequency estimation from ambiguous data: using resampling schema in validating frequency..., Nunes [/bib_ref]. Currently, it is possible to estimate allele frequencies, report frequencies graphically in the form of bar charts with colour codes, test for Hardy-Weinberg equilibrium and test for selective neutrality on data containing any number and kind of ambiguities (of course, if there are too many ambiguities the results may be meaningless but that can generally be controlled) by using the frequency estimation GENE tool (and haplotype to estimate haplotype frequencies on multiple loci). Two other programs are very useful in this context: file conversion allows one to convert a file into different formats (e.g. from Excel to the uniformate format used by GENE[RATE]), and, as described above, uniformate allows one to check the validity of the data format before using any other GENE[RATE] tool. All details are given by [bib_ref] A bioinformatics approach to ascertaining the rarity of HLA alleles, Middleton [/bib_ref] [bib_ref] Allele frequency estimation from ambiguous data: using resampling schema in validating frequency..., Nunes [/bib_ref] and the programs are available at http://geneva.unige.ch/generate.

## Practical issues for population analyses
Although not yet definitive (ongoing work), the following HLA-NET WG3 recommendations correspond, to our view, to the most important aspects of a population analysis:
1 Genotypic data for given population samples (either anthropologically defined or registry data) should be complete and include all ambiguities; the format used should be well known or explicitly described (e.g. uniformat); NMDP codes should be avoided. 2 Data used for analyses should be retrieved from genotypic data by recoding distinct sets of alleles depending on the allelic pool of interest for a given analysis (e.g. by using GENE[RATE] transliterate tool).
3 Sample sizes and the corresponding significant levels of allele frequencies (based on standard deviations) should be stated; the interpretation of the frequencies should take into account these significances and should avoid comparisons of populations based on the presence or absence of low-frequency alleles. 4 Reports of allele or haplotype frequencies should mention the program and, possibly, the algorithm used for estimation. Ideally, details about the initial conditions and environment of the algorithm used should also be included (e.g. for an expectation-maximization (EM) algorithm: the number of starting points, the number of distinct solutions, and the convergence criteria, i.e. either on likelihood or frequency values). 5 Assessment of Hardy-Weinberg equilibrium (HWE)
is mandatory for any use of allelic frequencies describing the genetic profile of a population in comparison to other data (otherwise phenotypic frequencies should be used). Testing for HWE using chi-square, G or exact tests on contingency tables should only be done in the absence of ambiguities and blank-like alleles. Otherwise, methods explicitly accommodating ambiguities should be used, like the method using nested likelihood ratios implemented in the GENE[RATE] frequency estimation program. 6 Selective neutrality should be assessed at least by reporting expected and observed homozygoties; a formal test (e.g. the GENE[RATE] algorithm of Ewens-Watterson test implemented in the frequency estimation program) is however preferable. 7 One should use bar-chart graphics to represent frequencies, rather than pie charts that are prone to many errors for comparisons (see [bib_ref] The Visual Display of Quantitative Information, Tufte [/bib_ref]. 8 Proper studies should also include an account of ethics as per WG4 recommendations.
An experiment is currently being made to accommodate this kind of meta-information described above in the context of the AHPD project of the 16 th IHIW workshop. The WG3 group will evaluate the results afterwards. The issues mentioned above show that WG3 is fulfilling its goals by providing a fully operational implementation of the guidelines emerging from HLA-NET. Furthermore, given that the GENE[RATE] programs are formally described, it will be easy to implement them in other platforms developed for population genetic analysis. The applicability of WG3 work thus extends beyond its current GENE implementation in the HLA-NET platform.

# Wg4 -ethical issues

## Aims of group
The role of working group 4 (WG4) is to provide support to the other working groups such that their actions are undertaken with sound ethical and legal considerations. Much work has already been undertaken to address ethical issues relating to genetic analyses taking into account the interests of all the parties involved in the study, i.e. researchers, participants and society [bib_ref] Shape variability and classification of human hair: a worldwide approach, De La Mettrie [/bib_ref] [bib_ref] Ethical and legal issues in genetic biobanking, Robertson [/bib_ref]. It must be stressed that population analysis of HLA types is not equivalent to genetic screening for a mutation predictive of disease and therefore the outcome of the HLA analysis is less likely to have any impact on the participants donating to the study.
It is not the aim of WG4 to reproduce (inter)national legislation and professional recommendations that have been made elsewhere [bib_ref] Ethical and legal issues in genetic biobanking, Laberge [/bib_ref] but to look at the application of such recommendations to HLA typing population studies specifically. In achieving our goals we aim to gather information related to legal and ethical regulations in different countries and to compile the information gained to obtain a consensus on practice for European countries.

## Study plan
Overall, a thorough 'study plan' is key to the success of any HLA population study, and this care must be taken to ensure that the study conforms to national and international legislation or recommendations  guidelines, where legislation is not in existence.
The study plan must be produced to provide information required for approval by institutional review board (IRB) or ethics committee. Even if the study is already covered by existing ethics approval, it is recommended that complete documentation of the study plan is produced.
The study plan must address the other following aspects:

## Study aims
The aim of the population genetics study must be well defined and documented prior to the study taking place, i.e. which population will be studied and which genetic loci will be analysed.

## Sampling
The following questions should be addressed:
1 Are there any risks and  or benefits to the subjects participating in the study? 2 Is the collection of new samples required or will DNA or other biological material already collected be used? 3 Will the samples be anonymized? If yes, will this happen at the point of collection or afterwards and will the link between subject and sample be reversible or irreversible. If reversible (also referred to as 'identifiable', 'linked' and 'coded'), who is responsible for the linking information?
4 How and where will linking information be stored?
Ethical issues relating to sampling individuals and populations for genetic analysis have been reviewed elsewhere and these apply to population studies of HLA [bib_ref] Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality..., Gerbault [/bib_ref].

## Samples already in collection
For samples that have already been collected, the consent given at the time of collection must be reviewed to see if the new proposed study qualifies. For example, samples taken for clinical testing are unlikely to have consent for unrelated HLA typing studies. Depending on what consent has been given it may be necessary to obtain additional consent and  or IRB  ethics committee approval for the HLA population genetics study. It is important to know whether the samples can be identified or not. A recent case in the USA highlighted that usage of previously collected DNA from an Amerindian tribe was not undertaken with appropriate consent from the participants (Couzin-Frankel, 2010).

## Samples to be collected
Informed consent is required and ethical committee approval must be sought. For informed consent to be given, subjects must be deemed as competent to give consent. Consent may be taken verbally or in writing depending on local legislation and guidelines but in all cases must be documented by the investigator. The consent process must inform the subject, usually through the issuing of an information sheet, of the following [bib_ref] Informed consent in genomics and genetic research, Mcguire [/bib_ref] :
1 the nature and goals of the research study 2 the type of sample to be taken from the participant 3 what sort of tests will be performed on their sample 4 whether the samples are to be made available for future undetermined studies 5 how data will be shared 6 what samples will be stored (intact cells, DNA) 7 length of storage, will this be limited 8 whether samples will be anonymized 9 freedom to withdraw from the study at any time 10 potential benefit or lack of benefit to participant 11 whether samples may be made available for other ethically approved studies
If the collection of material is from a well-defined population, it is appropriate to gain consent from appropriate authoritative members of the community and involve public consultation making use of local media prior to embarking on collection. Successful sample collection requires the concomitant gathering of predetermined subject information (e.g. demographic and clinical data). The format of the data to be collected for each subject donating a sample must be predetermined and compatible with international nomenclature and downstream data analysers.

## Examination process including data analysis
All biological material donated for research is extremely valuable and maximum effort must be taken to ensure that the material is tested by optimum procedures that will ensure maximum benefit from the data generated. Therefore the study plan must consider the methods that will be utilized and whether these methods will be undertaken by qualified and experienced personnel, e.g. HLA typing to be undertaken by an EFI  ASHI-accredited laboratory that participates in appropriate external proficiency testing for HLA typing. As the number of HLA alleles continues to increase with time, all HLA typing population studies must record the HLA allele database that has been used to assign HLA types to subjects such that future analysis can be undertaken should a new allele be found that may have been masked by previous typings.
The analysis of the data must also be undertaken using secure and proven software and should include application of Hardy-Weinberg.
Decisions must be taken at the time of study design to determine when resampling and  or retesting samples would be necessary.
For HLA typing data to accurately reflect the population under study, care is required to minimize unknown analysis of samples from individuals that are related to one another; this may be more difficult to determine for samples that are already in collection and therefore the numbers of samples to be analysed must be taken into consideration depending on whether knowledge is available on relatedness within population for optimum statistical evaluation.

## Data sharing
Consideration must be given to the following:
1 Data sharing with not-for-profit and for-profit organizations. Control of who has access to the data is irrelevant if the data are made available via open access data sharing. There is always the possibility that the data obtained from the study could be used to ultimately provide financial benefit, e.g. use of HLA population data by commercial companies. A risk assessment regarding this should be made for each study and appropriate information given in the study information guide given to participants. 2 It is also important to determine prior to embarking on the sample collection whether the data should be shared with the participants. If the samples are to be anonymized then this is not possible and participants should understand this [bib_ref] Patients' views on identifiability of samples and informed consent for genetic research, Hull [/bib_ref].
The issue over whether it is ethical to deny genetic research participant-individualized results have been discussed by others [bib_ref] Is it ethical to deny genetic research participants individualised results, Affleck [/bib_ref]. If the sharing of research results with participants is to be undertaken this could be via a newsletter or a website. This would allow a continued relationship with the participants which may be important should subsequent research studies be proposed with the participants samples [bib_ref] Prospective biorepository participants' perspectives on access to research results, Beskow [/bib_ref]. 3 If samples are not fully anonymized, the identifiable material  data must be kept in a secure location by the principle investigator only. Coded data should only be shared.

## Sample handling and sample and data storage
It is crucial that sufficient finances are available to cover secure storage of samples and data and that it is clearly defined who has responsibility for samples, their derivatives and the data generated. There must also be secure procedures in place to allow monitoring of the movement of data and samples [bib_ref] Data storage and DNA banking for biomedical research: informed consent, confidentiality, quality..., Gerbault [/bib_ref].

# Conclusion and perspectives
In this study, each working group has made a number of suggestions that can be taken as consensual HLA-NET methodological recommendations. These preliminary recommendations will of course be refined during the last period of the Action until their final publication. Compared to other proposals aiming at normalizing methodological issues in immunogenetic studies involving HLA data [bib_ref] Definitions of histocompatibility typing terms: harmonization of histocompatibility typing terms working group, Nunes [/bib_ref] , the present HLA-NET guidelines are the results of a large collaborative effort aiming at coordinating the whole suite of steps necessary to analyse HLA molecular data in human populations or registries, i.e. from population and  or sample definition (WG1) to ethical considerations (WG4) via the reporting of typing results (WG2) and the statistical analysis of the data (WG3). Also, it proposes very concrete and immediately applicable solutions to common problems (e.g. formatting data, estimating frequencies with ambiguities) by opening the access to userfriendly and continuously developing computer tools to the whole community of researchers working with this kind of data either at the population or at the donor-registry level. Overall, following the HLA-NET methodological recommendations given in this study will help to synchronize the work done by different laboratories to obtain comparable data and facilitate both European and international collaboration in histocompatibility, clinical transplantation, epidemiology and population genetics. At the end of the HLA-NET Action, all final documents and guidelines will be uploaded on a user-friendly HLA-NET public platform, which will also offer direct access to data-bases linked to useful computer programs for HLA data analysis. A joint effort with other consortiums will further be undertaken to provide widely consensual solutions at the international level.

## Institution providing the sample
[table] Table 1: Preliminary list of population  registry samples available for HLA-NET [/table]
[table] Table 2: List of alleles (nonexhaustive) that were usually not taken into account in the past but may affect population genetic statistics because of significant frequencies [/table]
[table] Table 3: Rare alleles contibuted to the http://www.allelefrequencies.net database [/table]
[table] 1 1: Name of the Institution:______________________________________________________ _______________________________________________________________________ 1.2. Contact person (e.g. principal investigator): o Name: _______________________________________________________________ o Telephone number:_____________________________________________________ o E-mail address: ________________________________________________________ 2. Sample tested (Please, fill one full questionnaire per sample) 2.1 Type of study: Field study in a well-defined population? YES / NO If NO, please indicate which type of sample: o blood donors YES / NO o stem cell donors (registries) YES / NO o organ donors YES / NO o cord blood donors YES / NO o healthy controls for disease studies YES / NO o p a t i e n t s Y E S / N O Does the sample include first degree relatives? YES / NO / UNKNOWN Is the information on relatedness available? YES / NO 2.2. Name of the population and any alternative names known (if necessary use Ethnologue): Main name (s): ___________________________________________________________ Other possible name(s): ____________________________________________________ ________________________________________________________________________ Other specific characterization that would help to differentiate this population to any other (e.g., specific cultural trait like endogamy, specific religion, isolated location, etc): ________________________________________________________________________ ________________________________________________________________________ 2.3. Geographic location of the population: Is the location of the sample the same as the location of the population? YES / NO If YES, fill the questionnaire only for the sample (1.3.1). If NO (e.g. answer "NO" for Chinese living in US, etc), please fill the corresponding information for both the sample (1.3.1) and the population (1.3.2) from where the sampling was made. [/table]