Talking Points Thursday - Havasupai Indians and the Ethical Use of Genetic Data

So the idea for today's theme is to discuss a current topic in anthropological or medical genetics. I was originally going to discuss Gene Set Enrichment Analysis (GSEA) and the modern use of transcriptomic data to infer biological pathways but instead came across a very important article on the use of genetic data and the Havasupai Indians in the New York Times by Amy Harmon. The basic gist of the article is that in the early 1990s, researchers from Arizona State University collected blood from the Havasupai for a study on diabetes, which is in high frequencies in the community. This research didn't lead to anything but it was used for a number of other studies regarding population history. The problem arises is that population history (added 4/24/2010) is not what the individuals who provided the samples consented too and they were never informed regarding these changes. This led to ASU settling for 700,000 dollars and agreeing to furnish the Havasupai with a number of items.

This of course raises a number of issues from both an ethical and research standpoint but a couple of the more pertinent ones are what does this mean for people who have biological samples still in their freezer and how does that impact the modern day geneticists ability to do research with indigenous populations. Last week at the AAPA meetings, I was actually pondering how many geneticists actually do fieldwork with the populations they study. The most salient feature of anthropology is that is about people, and when you establish trust with individuals through interpersonal contact you are better able to meet both yours and their needs. However, when we talk about populations, this a fairly abstract expression of a group's identity. While it sounds like the researcher in this case did do fieldwork - she didn't establish a long-term relationship with these people and this led to the current situation. This is a big difference between cultural and biological anthropologists, where cultural anthros will stay years in the same community but biological anthropologists may only stay days and it may be the only time they visit the community. I suppose the lesson to learn from this case is to establish a dialogue with the community and always remember the table should be round so you can address their concerns if they arise.

The second issue that needs to be addressed is how does this affect individuals who keep biological samples in their freezer and have used them for other purposes than they were originally collected for. Will this cause a backlash against these researchers and their institutions? This decision may have far-reaching implications regarding access to samples. There has already been some backlash against genetic ancestry research in places such as Colombia, where analysis of genetic material is not allowed to occur outside the country, without express permission of the community. This harks back to a case in the late 1990s, where an individual sent some samples to be developed as immortal cell lines, without the permission of the populations they were collected from.

The problem becomes when you have a fairly stable item (like DNA) that can be used for several different purposes. A number of labs have genetic material that are often used for purposes beyond what they were originally collected for. This finding however indicates that researchers are going to need to be very specific in what they put in their Informed consent form. Also, it is quite likely IRB proposals are going to need to be more stringent and protective of individuals in the future and that all research will need approval.

Friday Five #15 - Migrating to Kansas.

I've been busy this week preparing for a presentation I'm giving at a conference in Kansas, next week. The theme of the conference is on Why do Human Migrate? and how can we better understand the wanderlust that has driven our ancestors to and fro. The conference program looks pretty interesting and I'm on two of the abstracts, which are listed here.

1) Mennonite migrations: Demographic and genetic consequences: This is a summary of some work I did some time ago and will be published in a forthcoming article in the journal Human Biology.

Throughout history, religious persecution has often led to human migration. Prime examples of these demographic events are those that impacted populations belonging to the Anabaptist movement (Amish, Hutterites, and Men-nonites) after the Reformation in 16th century Europe. Each of these groups went through persecutions and demographic events that led to population movement in Europe and eventual emigration to the Americas. This study focuses on maternal molecular genetic diversity in six Mennonite communities from Kansas (Goessel, Lone Tree, Garden View, Meridian, and Garden City) and Ne-braska (Henderson) in order to determine their genetic structure and its relationship to understanding the biologi-cal impact of these demographic events. The Mennonite samples exhibited eight western European mtDNA haplogroups – H, HV0, I, J, K, T, U, and X. Comparable to other populations of European descent, haplogroup H was the most frequent in all six communities and ranged from 35% in Lone Tree to 75% in Old Order Mennonites from Garden City, Kansas. Molecular genetic variation was ex-amined and compared to classical genetic markers. The Goessel and Henderson communities demonstrate a shared biological relationship with a comparative population from the Netherlands. Additional analyses of molecular data indicate these Anabaptist communities formed new con-gregations by fissioning along familial lines. This popula-tion subdivision of into congregations support previously documented patterns of fission-fusion from classical genetic data but demonstrates a more accurate reflection of these communities into their modern day congregations.

2) Understanding the Genetic Structure and Migration of the Rama Indians from the Caribbean Coast of Nicaragua: Some of the results for this article were drawn from my dissertation and further fieldwork that my co-author conducted a couple of years later on the coast of Nicaragua. The picture on the cover of the program is from the Rama.

This study investigated genetic and cultural data in order to understand the population history and migration of the Rama Indians in relation to neighboring Central American indigenous groups. The Rama belong to the Chibchan linguistic family (Votic sub-family) and inhabit the coast and rivers of southern Nicaragua. Due to limited ethno-historical and ethnographical information, the origins and population dynamics of the Rama remain unclear, both prior to and after the colonization of the Caribbean coast of Nicaragua. We analyzed maternal (mtDNA) and paternal (Y-chromosome) molecular genetics from 75 Rama partici-pants in order to assess the provenience of the Rama to neighboring Native American populations. These data were analyzed using median-joining networks, multi-dimensional scaling plots, and analysis of molecular vari-ance (AMOVA). Ethnographic and genealogical data were used to better understand both cultural ecology and inter-nal migration of the Rama within their ancestral territory. The results of this study indicate that the Rama are diver-gent from comparative Chibchan populations and are char-acterized by a high frequency of mtDNA haplogroup B (92%) and low frequency of haplogroup A (8%). This re-duced haplogroup diversity characterize the Rama and in-dicate maternal genetic drift. In addition, Y chromosome data indicate paternal gene flow between the Rama and neighboring Mesoamerican groups. Together, these data challenge previous assumptions regarding the relation-ships of the Rama with neighboring Central American groups, and suggest an early divergence from other Votic populations as well as a pattern of seasonal movements within their territory.

3) New type of genetic variation discovered

4)Potential for Revealing Individual-Level Information in Genome-wide Association Studies. An interesting article from JAMA regarding the high level sequencing data and privacy protection.

5) Identification of functional modules that correlate with phenotypic difference: the influence of network topology: a interesting article that looks at comparing pathways between phenotypes.

One of the important challenges to post-genomic biology is relating phenotypic alterations to the collective alterations in genes underlying observed state changes. Current inferential methods, however, invariably omit large bodies of information on the relationships between genes. We present a method that takes account of such information - expressed in terms of the topology of a correlation network - and we apply the method in the context of current procedures for gene set enrichment analysis.

Friday Five #14: Ancient Eskimos, Complete Africans, and some Cardiovascular Genetics

OK, so I know I missed last week but I haven't spent the last two weeks learning a number of useful new programs such as R and Photoshop. Specifically, how to make really nice figures in photoshop and how to make nice GWAS Manhattan Plots in R as well as survival analysis so I missed out, plus last weekend was overtaken by a mass house cleaning. Here is the top five from the last two weeks.

1) Ancient human genome sequence of an extinct Palaeo-Eskimo: This was all over the news last week and it is very interesting study as it does get to the ability to look at ancient human genomes. However, if I'm being honest I wasn't overly surpised by the conclusions as they are pretty much the same as they had done in a previous paper on mtDNA and the presence of haplogroup D2a, which is found in Eskimos, Na-dene, and Aleuts. Here is the abstract. Also, my grad advisor was a co-author on the paper, which is pretty cool.

We report here the genome sequence of an ancient human. Obtained from ~4,000-year-old permafrost-preserved hair, the genome represents a male individual from the first known culture to settle in Greenland. Sequenced to an average depth of 20×, we recover 79% of the diploid genome, an amount close to the practical limit of current sequencing technologies. We identify 353,151 high-confidence single-nucleotide polymorphisms (SNPs), of which 6.8% have not been reported previously. We estimate raw read contamination to be no higher than 0.8%. We use functional SNP assessment to assign possible phenotypic characteristics of the individual that belonged to a culture whose location has yielded only trace human remains. We compare the high-confidence SNPs to those of contemporary populations to find the populations most closely related to the individual. This provides evidence for a migration from Siberia into the New World some 5,500 years ago, independent of that giving rise to the modern Native Americans and Inuit.

2) Complete Khoisan and Bantu genomes from southern Africa: So once again we have some entire genomes sequenced, which seems to come about everytime someone does that. Perhaps eventually we will get around to doing some actual population genetics of the variation that has been detected, maybe once the 1000 genomes project reaches completion. Anyway, this just demonstrates the direction that human genetics is heading over the next couple of year. Here is the abstract

The genetic structure of the indigenous hunter-gatherer peoples of southern Africa, the oldest known lineage of modern human, is important for understanding human diversity. Studies based on mitochondrial and small sets of nuclear markers have shown that these hunter-gatherers, known as Khoisan, San, or Bushmen, are genetically divergent from other humans. However, until now, fully sequenced human genomes have been limited to recently diverged populations. Here we present the complete genome sequences of an indigenous hunter-gatherer from the Kalahari Desert and a Bantu from southern Africa, as well as protein-coding regions from an additional three hunter-gatherers from disparate regions of the Kalahari. We characterize the extent of whole-genome and exome diversity among the five men, reporting 1.3 million novel DNA differences genome-wide, including 13,146 novel amino acid variants. In terms of nucleotide substitutions, the Bushmen seem to be, on average, more different from each other than, for example, a European and an Asian. Observed genomic differences between the hunter-gatherers and others may help to pinpoint genetic adaptations to an agricultural lifestyle. Adding the described variants to current databases will facilitate inclusion of southern Africans in medical research efforts, particularly when family and medical histories can be correlated with genome-wide data.

3) Association Between a Literature-Based Genetic Risk Score and Cardiovascular Events in Women: This is an article from JAMA, that really is a little bit of a mess if you ask me. So a little background the area on chromosome 9p21 has been implicated in a number of cardiovascular disorders using GWAS but to date no one is really sure, what gene or genes in the region is having this impact. This study looked at a paltry 101 SNPs that had shown association with a litanly of end points or intermediate phenotypes and of course found nothing significant. For a good critical review of the paper see this blog post at Eurogene. Here is the abstract.

Context While multiple genetic markers associated with cardiovascular disease have been identified by genome-wide association studies, their aggregate effect on risk beyond traditional factors is uncertain, particularly among women.

Objective To test the predictive ability of a literature-based genetic risk score for cardiovascular disease.

Design, Setting, and Participants Prospective cohort of 19 313 initially healthy white women in the Women's Genome Health Study followed up over a median of 12.3 years (interquartile range, 11.6-12.8 years). Genetic risk scores were constructed from the National Human Genome Research Institute's catalog of genome-wide association study results published between 2005 and June 2009.

Main Outcome Measure Incident myocardial infarction, stroke, arterial revascularization, and cardiovascular death.

Results A total of 101 single nucleotide polymorphisms reported to be associated with cardiovascular disease or at least 1 intermediate cardiovascular disease phenotype at a published P value of less than 10^–7 were identified and risk alleles were added to create a genetic risk score. During follow-up, 777 cardiovascular disease events occurred (199 myocardial infarctions, 203 strokes, 63 cardiovascular deaths, 312 revascularizations). After adjustment for age, the genetic risk score had a hazard ratio (HR) for cardiovascular disease of 1.02 per risk allele (95% confidence interval [CI], 1.00-1.03/risk allele; P = .006). This corresponds to an absolute cardiovascular disease risk of 3% over 10 years in the lowest tertile of genetic risk (73-99 risk alleles) and 3.7% in the highest tertile (106-125 risk alleles). However, after adjustment for traditional factors, the genetic risk score did not improve discrimination or reclassification (change in c index from Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults [ATP III] risk score, 0; net reclassification improvement, 0.5%; [P = .24]). The genetic risk score was not associated with cardiovascular disease risk (ATP III–adjusted HR/allele, 1.00; 95% CI, 0.99-1.01). In contrast, self-reported family history remained significantly associated with cardiovascular disease in multivariable models.

Conclusion After adjustment for traditional cardiovascular risk factors, a genetic risk score comprising 101 single nucleotide polymorphisms was not significantly associated with the incidence of total cardiovascular disease.

4) APOA1 and APOA4 Gene Polymorphisms Influence the Effects of Dietary Fat on LDL Particle Size and Oxidation in Healthy Young Adults: More Cardiovascular Genetics stuff.

We investigated whether APOA1 and APOA4 genotypes interact with diet to determine changes in LDL size and their susceptibility to oxidative modifications. A total of 97 healthy volunteers each consumed 3 diets for 4 wk: a SFA diet (38% fat, 20% SFA) followed by a low-fat and high-carbohydrate (CHO) diet (30% fat, 55% carbohydrate) or a monounsaturated fatty acid (MUFA) diet (38% fat, 22% MUFA) following a randomized crossover design. For each diet, we determined susceptibility to oxidative modifications and LDL size. To investigate the combined effects of the APOA1 G-76A and APOA4 Thr347Ser single nucleotide polymorphisms (SNP), we defined 4 combined genotype groups: GG/ThrThr, GG/ThrSer, GA/ThrThr, and GA/ThrSer. After participants consumed the CHO diet, there was a significant decrease in LDL size with respect to high-fat diets in GG homozygotes for the APOA1 G-76A SNP. However, LDL size did not differ in GA carriers among participants consuming the 3 diets. Carriers of the A allele for this polymorphism had smaller LDL size as well as increased susceptibility to oxidation after the SFA diet than the GG homozygous. Moreover, the interaction between the APO A1 and APOA4 genotypes revealed that individuals with the GA/ThrSer genotype had larger LDL particle size during consumption of the MUFA diet than when they consumed the CHO diet. No differences in LDL oxidation were found in this analysis. Our study supports the concept that SNP in APOA1and APOA4 genes influences atherogenic characteristics of LDL particles in response to diet.

5) Genetic Effects on Carotid Intima-Media Thickness: Systematic Assessment and Meta-Analyses of Candidate Gene Polymorphisms Studied in More Than 5000 Subjects: Yet even more CVG stuff

Background— Carotid intima-media thickness (CIMT) is highly heritable and associated with stroke and myocardial infarction, making it a promising quantitative intermediate phenotype for genetic studies of vascular disease. There have been many CIMT candidate gene association studies, but no systematic review to identify consistent, reliable findings.

Methods and Results— We comprehensively sought all published studies of association between CIMT and any genetic polymorphism. We obtained additional unpublished data and performed meta-analyses for the 5 most commonly studied genes (studied in at least 2 studies in a total of >5000 subjects). We used a 3-step meta-analysis method: meta-analysis of variance; genetic model selection; and random effects meta-analysis of the mean CIMT difference between genotypes. We performed subgroup analyses to investigate effects of ethnicity, vascular risk status, and study size. We accounted for potential reporting bias by assessing qualitatively the possible effects of including unavailable data. Polymorphisms in 3 of the 5 genes (apolipoprotein E, angiotensin I converting enzyme, and 5,10-methylenetetrahydrofolate reductase) had an apparent association with CIMT, but for all these, we found evidence of small study bias. Apolipoprotein E 2/3/4 was the only polymorphism with a persistent, statistically significant but modest association when we restricted analysis to larger studies (>1000 subjects).

Conclusions— Of the most extensively studied polymorphisms, apolipoprotein E 2/3/4 is the only one so far with a convincing association with CIMT. Larger studies than have generally been performed so far may be needed to confirm the associations identified in future genome-wide association studies, and to investigate modification of effect according to characteristics such as ethnicity and vascular risk status.

Enjoy :)

A sequence of errors

An upcoming article (early view available here) by Wall and Kim (2007) in PLOS Genetics questions the validity of the nuclear Neanderthal sequence data that was published by Green et al. (2006) and Noonan et al. (2006). Aside from an abstract light on the detail and the misuse of the word data as singular rather than plural (I've been nailed for this so many times, I'm like a Pavlovian puppy with it).

This article is really a metadata analysis of the previous sequence analysis presented in these articles. The authors obviously favor Noonan's data more than Green's. They imply that data presented in the Green article may have been seriously contaminated by modern human DNA or sequencing errors and this led to the more recent convergence time and some of the other results from the article. Both of these latter occurences are common laboratory pratfalls, which are often overlooked in the literature.

Contamination with modern humans occurs because PCR is really non-discriminatory and willl amplify anything that gets into the sample. So in the case of any ancient DNA a number of precautions need to be underaken. Sequencing errors are more numerous in the scientific literature. If you look at mtDNA studies, which is by far the most extensively (some might say excessively) used genomic research in humans you find an error rate around 60% due to human error (Forster 2003) or phantom mutations (Brandstatter et al. 2005). This is generally easily resolved just by investigating the transistion to transversion ratio. In humans transistions are more common than transversions and so just by analogy you could assume the same to be true for Neanderthals. Sequences can also be misread by the sequencer when the laser misdetects a base pair and so on the chromatogram the basepair may be mislabeled. This is more common at the front and back ends of the sequence. You can then investigate this by hand reading the chromatogram, however if you are dealing with 1,000s to 100,000s of base pairs this is probably not feasible. The alternative is to have multiple labs investigate the same stretch and come to a consensus, which is costly and means you have to share the glory.

Overall, I agree with the conclusion of the article, that the amplification of Neanderthal DNA is a major scientific breakthrough but that these resulting data need to be carefully verified before they are rushed to publication.

References Cited

Brandstatter A, Sanger T, Lutz-Bonengel S, Parson W, Beraud-Colomb E, Wen B, Kong QP, Bravi CM, Bandelt HJ. 2005. Phantom mutation hotspots in human mitochondrial DNA.
Electrophoresis 26:3414–3429.

Forster P. 2003. To err is human. Ann Hum Genet 67:2–4.

Green RE, Krause J, Ptak SE, Briggs AW, Ronan MT, et al. (2006) Analysis of one
million base pairs of Neanderthal DNA. Nature 16: 330-336.

Noonan JP, Coop G, Kudaravalli S, Smith D, Krause J, et al. (2006) Sequencing and analysis of Neanderthal genomic DNA. Science 314: 1113-1118.

Wall JD, Kim SK (2007) Inconsistencies in Neanderthal genomic
DNA sequences. doi:10.1371/journal.pgen.0030175.eor

Stephen Colbert's genetic roots

We are all mutants. I'm currently working on teleporting right now. You gotta love Stephen! (via Yann Klimentidis)