SNPs genotyping analysis using Haploview

1. What is the name of haploview format to use in this analysis?

Answer: HapMap Project Data Dumps

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2. Please show us the marker and individual quality control of the genotype data use in the analysis?

Answer: To analyze the SNP genotyping result from the question, we have to create the file which contain only “HapMap Project Data Dumps” information and then analyze it in Haploview software.
We can do so by copy the data provided in the question, but select only the SNP genotyping data. (Starting at “rs#” through to the end of last SNP marker)

Paste the data in a blank Notepad text file and saved it. (The file name shown here is “HapMap_Assignment.txt” ).

 At the Open new data menu, select “HapMap Format”. Then browse to the filed you saved. After that click OK.

The software will analyze the data and show the result in a windows with 4 tabs at the upper left corner. Select “Check Markers” tab, it will show the marker and individual control information. The marker datas are in the table, and the individual quality controls are at the bottom of table.

The marker’s details are:

• #  is the marker number.
• Name  is the marker ID specified (only if an info file is loaded).
• Position  is the marker position specified (only if an info file is loaded).
• ObsHET  is the marker’s observed heterozygosity.
• PredHET  is the marker’s predicted heterozygosity (i.e. 2*MAF*(1-MAF)).
• HWpval  is the Hardy-Weinberg equilibrium p value, which is the probability that its deviation from H-W equilibrium could be explained by chance.
• %Geno  is the percentage of non-missing genotypes for this marker.
• FamTrio  is the number of fully genotyped family trios for this marker (0 for datasets with unrelated individuals).
• MendErr  is the number of observed Mendelian inheritance errors (0 for datasets with unrelated individuals).
• MAF  is the minor allele frequency (using founders only) for this marker.
• Alleles  are the major and minor alleles for this marker.
• Rating  is checked if the marker passes all the tests and unchecked if it fails one or more tests.

The individual quality controls’ details are:

• Hardy-Weinberg p-value cutoff: >0.0010 (per population)
• Minimum genotype %: < 75% (per population)
• Maximum # Mendel errors: < 1 (per population; only applies to YRI, CEU, ASW, MEX, MKK)
• Minimum minor allele frequency: < 0.0010

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3. Please show us the LD map then explain what do you get from the LD map?

Answer: The linkage disequilibrium (LD) map shows the strength of linkage disequilibrium between each SNPs. The more intensity of red color, the higher linkage disequilibrium value it is.

(Please click on the image to see its original size) 

 This LD map obtained from the question has 30 markers. By using “Confidence Intervals” algorithm, there are 3 haplotype blocks in the map. The first block, 18 kb in size, consists of marker number 8 and 9. The second block, 80 kb in size, consists of marker number 13 – 17. Finally, the third block, 48 kb in size, consists of marker number 24-29. (See answer of the 4th question for the detailed on Haplotype block)

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4. How many haplotype blocks in this region of Chromosome X, then explain how to interpret them?

Answer: The haplotype display shows each haplotype in a block with its population frequency and connections from one block to the next. This represents the level of recombination between the two blocks (value of multiallelic D’). Above the haplotypes are marker numbers.

 To summarize, there are 3 haplotype blocks in the map.

• The first block consists of marker number 8 and 9.
• The second block consists of marker number 13,14,15,16 and 17.
• The third block consists of marker number 24, 25, 26, 27, 28 and 29.

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5. Could you find out the tagging SNP in each haplotype block, then explain what the tagging SNPs?

Answer: A tagging SNP is a representative single nucleotide polymorphism (SNP) in a region of the genome with high linkage disequilibrium. It is possible to identify genetic variation by genotyping just only the tagging SNPs in the haplotype without genotyping every SNP.

To display tagging SNP, go to “Display” > select “Show tags in blocks”

Notice the grey triangles which will appear at the location of tagging SNPs in each haplotype block.

To summarize, tagging SNPs in each haplotypes are:

• The first block has tagging SNPs at marker number 8 and 9.
• The second block consists of marker number 13 and 15.
• The third block consists of marker number 24 and 27.

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That’s the end of assignment 2.

If you have any quesion please do not hesitate to contact me or leave a comment. ^.^

Haploview

Step 1 : Download  JRE จาก http://www.java.com และ Haploview 4.1 จาก http://www.broadinstitute.org/haploview/haploview-downloads และทำการติดตั้งบนเครื่องคอมพิวเตอร์

Step 2 : ทำการคัดลอกข้อมูลจากใน Virtual Classrom มาวางใน Notepad และทำการเก็บข้องมูลโดย คลิก File แล้วเลือก Save As… จากนั้นให้ตั้งชื่อไฟล์ และกดปุ่ม Save ในทีนี้ตั้งเป็น Data.txt

Step 3 : ทำการเปิดโปรแกรม Haploview 4.1

Step 4 : ที่แถบ HapMap Format ให้กด Browse แล้วเลือกไฟล์ Data.txt ที่ได้ทำการสร้างไว้ใน Step 2 แล้วกด OK

Step 5 : จะได้ดังภาพ ซึ่งแสดง marker และ individual quality control ในแถบ Check Marker

Step 6 : กดเลือก แถบ LD Plot จะได้ดังภาพ

Step 7 : สามารถ Save LD Plot ในรูปแบบของรูปภาพได้โดย เลือก File แล้วตามด้วย Export current tab to PNG แล้วจัดการบันทึกให้เรีบร้อย

Step 8 : จะได้ไฟล์ดังภาพ

Step 9 : เลือกแถบ Haplotype เพื่อดู haplotype blocks ดังภาพ

Step 10 : เลือกแถบ Tagger และกดปุ่ม Run Tagger

Step 11 : จะได้ Result ดังภาพ และในส่วนของช่อง Alleles captured by Current Selection ภายในนั้น จะมี tag SNPs

ตอบคำถาม

1. What is the name of haploview format to use in this analysis?

- HapMap Format. (Step 4)

2. Please show us the marker and individual quality control of the genotype data use in the analysis?

- Marker และ individual quality control ถูกแสดงในแถบ Check Markers (Step 5)

3. Please show us the LD map then explain what do you get from the LD map?

-  LD map แสดงในแถบ LD plot (Step 6-8)

- LD map แสดงการคำนวณจับคู่กันของแต่ละ marker ว่ามีความเป็นlinkageกันมากแค่ไหน โดนคำนวณแบบ pairwise ถ้าคู่ไหนมีความเป็นLDกันมาก ก็จะได้ LD score ที่แสดงในช่องสูง นำไปสู่การจัดกลุ่มmarkerเข้าด้วยกัน โดยถ้ามีคะแนน LD มาก ช่องนั้นจะเป็นสีแดง ส่วนคู่ไหนที่มีความเป็น LD กันน้อย จะสีอ่อน จึงทำให้จัดกลุ่ม haplotype ได้

4. How many haplotype blocks in this region of Chromosome X, then explain how to interpret them?

- มี 3 blocks ของ haplotype (Step  9)

5. Could you find out the tagging SNP in each haplotype block, then explain what the tagging SNPs?

- Tag SNPs จะถูกโชว์ในกล่อง Alleles captured by Current Selection ของแถบ Tagger (Step 10-11)

- Tag SNPs คือ SNPs ที่สามารถแทน SNPs อื่นได้ เพราะว่าเป็น linkage disequilibrium กันที่สามารถเป็นตัวแทนกันได้

Alzheimer’s disease (AD) remains the most common form of dementia, particularly the late-onset version which typically develops in patients aged over 65. Although there is believed to be a strong genetic basis to the disease, the only gene previously identified as a susceptibility factor in all version of the disease was APOE, coding for Apolipoprotein E. In addition, genes for Amyloid precursor protein (APP), Presenilin 1 (PSEN1) and Presenilin 2 (PSEN2) have been noted as factors in the less common early-onset form of AD, which has a strong pattern of familial inheritance. Other attempts to find genes influencing the more common late onset form of AD have been ‘under-powered’, i.e. have involved insufficient individuals (≤1,100) to reveal any further statistically-significant correlations.

In October 2009, however, two independent studies published “back-to-back” in the journal Nature Genetics identified a number of other genes in which Single Nucleotide Polymorphisms (SNPs) seem to be associated with development of late-onset AD.

Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease
Harold et al (2009) Nature Genetics 41:1088-1093

The authors of the first study (n = 86, headed by Julie Williams from Cardiff University) began by examining SNPs in about 19,000 research subjects (reduced to about 16,000 after quality control). These genotypes were drawn from seven different primary studies, hence the fairly stringent application of quality control leading to the exclusion of a significant number of genomes.

The investigation was conducted in two stages. In the first part, over 500,000 SNPs from nearly 12,000 individuals (4000 known AD cases, 7850 controls) were investigated. Analysis was limited to autosomal chromosomes (except for one SNP on the X-chromosome already known to be associated with AD). In addition to the established link to APOE, the study suggested involvement of two further genes CLU and PICALM. CLU (on chromosome encodes clusterin, another significant brain apolipoprotein . The specific SNP identified lies within an intron.  PICALM (chromosome 11) encodes phosphatidylinositol-binding clathrin assembly protein (aka clathrin assembly lymphoid-myeloid leukaemia gene).  The specific SNP identified is 88.5 kb 5’ to the gene. The authors of this study had prior notice of the study of Lambert et al (see below) and note that CLU is also the novel gene demonstrating the strongest association with AD in their work.

During the second stage, the researchers looked at the two newly identified SNPs in five additional European cohorts, drawn from Belgium, Greece, Bonn, the Medical Research Council, UK and the Alzheimer’s Research Trust. These included just over 2,000 AD cases and about 2,300 age-matched, cognitively screened controls.

Armed with the data of Amouyel et al, these authors also re-examined their own results looking for evidence of associations identified in the other paper. They confirm “suggestive evidence” for association with CR1, the gene encoding complement receptor 1. They also report the genes for bridging integrator 1 (BIN1) and disabled homolog 1 (DAB1) as being “noteworthy”.

Finally, the authors consider the physiological implications of the various mutations.

• APOE acts as a chaperone for Ab, influencing the latter’s conformation, conversion to an insoluble form, aggregation and toxicity.
• Clusterin is a molecule that occurs in a fairly wide number of tissue types and with several likely functions. The gene product is a 449 AA protein which is later glycosylated.  It binds soluble Ab in a specific and reversible manner; the resulting complex has been demonstrated to cross the blood-brain barrier.
• PICALM is ubiquitously in all tissues, but particularly so in neurons. It is involved in clatharin-mediated endocytosis (as too is BIN1). The role of PICALM in trafficking of VAMP2, a protein involved in synaptic vesicle fusion to presynaptic membranes, may underlie its role in AD.

Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease
Lambert et al (2009) Nature Genetics 41:1094-1099

In the second paper, the authors (n = 50, plus unnamed members of the European AD Initiative Investigators, and headed by Philippe Amouyel in Lille) conducted an initial study of about 2000 French cases of AD and about 5,300 controls, also from France. As was also seen in the separate study by Harold et al, this analysis identified the CLU gene as having genome-wide significance in AD.

A number of further markers across several chromosomes had “suggestive evidence of association”. Therefore in a second stage, analogous to the methodology used by Harold et al, the researchers looked to confirm the observed association with CLU and probe more deeply the correlation with other markers in the major loci identified. The individuals in this stage (approx 4000 AD cases and 3,300 controls) came from Belgium, Finland, Italy and Spain. This second stage confirmed the significant association with CLU, and also identified a second gene CR1 (for complement receptor 1, the main protein interacting with complement protein C3b).

These authors review several previous experiments that support a suggestion that CR1 is involved in clearance of Ab. It is therefore their contention that whereas the gene errors implicated in early-onset (familial) AD result in Ab overproduction, those involved in standard late-onset AD may lead to inadequate Ab clearance.

None of the genes identified in the recent studies guarantee the development of AD – in this paper it is estimated the risk factors are 25.5% for APOE, 8.9% for CLU and 3.8% for CR1.

Two further points of note. One is the care taken to ensure results are genuine – when aware that a Belgian dataset had been examined in both this study and in the work by Harold et al the present authors recalculated their results excluding the shared data lest it introduced any unwarranted significance by being counted twice. Secondly, this paper labels the gene for phosphatidylinositol-binding clathrin assembly protein as PILCAM. Looking at the literature it seems that this is a typo, not an alternative name.

If you’ve read this far, you may also be interested in the assessment of this research published on the excellent NHS Choice Behind the headlines website.

I’m updating my CV, and that reminded me that I meant to promote this cool clustering technique that I was a little bit involved in, Clustering With Shallow Trees.

This goes way back to about half-way through my post-doc at MSR, when statistical physicist Riccardo Zecchina was visiting for a semester, and was teaching me about all of the “intractable” optimization problems that he can solve using his panoply of propagation algorithms. In particular, he was working on algorithms for certain types of steiner tree optimization, and he had discovered that adding an extra constraint on the depth of the tree didn’t make the problem harder. (All variants of the problem he considers are NP-hard, but some are NP-harder than others.)

On the bus to work the next day, this depth constraint clicked with some complaints I had heard recently about the failures of single-linkage clustering in practice, that the algorithm produces long, stringy clusters, which are very sensitive to noise. Could having  a knob to tune the depth of the spanning tree could be a way to address this? Riccardo worked hard on it for a long time, and brought a bunch of collaborators into the mix, and eventually they figured out how to make it work really well. They also proved that this approach interpolates between single-linkage (when the depth is unbounded) and the popular new affinity propagation technique (when the depth bound is 2.

It turns out that something between SL and AP is the thing to do in many instances. Here is the home-run example from the paper, clustering people based on their SNPs:

Compare with the results of single linkage and affinity prop:

(What use is clustering people based on their genetic information? It’s important and scary to think about that…)

I got them to try applying it to a public health dataset as well, and the results are promising, but it needs more careful attention to be useful.

That reminds me: Riccardo and Team Survey Propagation, is the code for this available? We need to let other researchers try it on their own data if we want to give the technique a chance to take off.

It’s been several weeks now since I started working at 23andMe, a personal genomics company located in Mountain View, CA. Perhaps not coincidentally, it’s also been several weeks since I last blogged. The transition hasn’t been difficult, but it did take some getting used to, mentally and physically. I mean, leaving for work by 8:30am? Regular hours? Commuting??

Ok, so I really have nothing to complain about. 8:30 isn’t that early, and I could shave half an hour off each end of my commute if I didn’t choose to take advantage of bike-friendly roads, good weather, and a company-sponsored free train pass (OMG benefits!?). All in all, things are pretty much fantastic. The work environment is friendly, flexible, and laid-back; we have plenty of food and drink to keep us fueled throughout the day, and regular workouts/yoga if we need to get fired up or mellowed down (and to keep the “Free Food 15″ at bay). Plus, personal genomics is a super interesting and rapidly evolving industry, so there’s really never a dull moment.

So what is personal genomics, anyway? We’ve known for a while that genetics – the sequence of DNA inside our cells – plays an important role in our form and functioning. Many diseases are caused by changes in DNA (often in genes, parts of DNA that code for proteins) that alter the normal functioning of cells, though not all genetic differences lead to negative changes. (Genetics can also tell us about ancestry – who is related to whom and the history of populations – but I won’t be addressing that in this post.) Where it gets personal is when you apply it to individuals, such as when someone gets a genetic test to determine whether they have or are at risk of developing or passing on a particular disease. Where it gets genomics is when we use high-throughput technologies to do what is essentially thousands of genetics tests at once. Put them together, and you get personal genomics.

How do we know what genetic “pieces” correspond to what conditions or diseases? The general strategy is to compare the DNA of a whole bunch of individuals that have that condition (cases) to a whole bunch of individuals that don’t (controls). As long as both groups are similar save for their case-control status, any significant genetic differences between them should have something to do with that condition. We call this a genetic association.

It turns out that there are millions of single locations in the human genome where the exact sequence of the DNA might differ between two people, and these places, called single nucleotide polymorphisms, or SNPs, can contribute to differences we can observe, such as whether you flush when you drink alcohol or how easily you put on weight. 23andMe personal genomics kit determines what your sequence is for a representative subset of SNPs. Many are already known to be associated with certain conditions, and new research is being done every day to uncover more and more of these associations.

So what exactly do I do at 23andMe? My official job title is “Scientist, Content Curation”. Curation, I’ve found, is not very familiar to most people. Most people probably know that there is such a thing as a museum curator, but might not know what they do. Hardly anyone has ever heard of scientific curation. (And I thought explaining what I was studying as a grad student was hard! Biomedical informatics, anyone?)

But it’s really not that complicated. The essence of curation is almost always the same: the selection, acquisition, and management of content. What that content is differs depending on the field – for example, an art curator might look for and organize artwork for exhibition in a gallery, while a curator in the “Ancient Civilizations” department of a museum may be in charge of acquiring, managing, and presenting archaeological artifacts.

In science, curation involves organization of scientific knowledge and data. An area where this has been especially important is the life sciences, as the amount of information being generated by high-throughput experiments, large-scale projects, and scholarly publishing has skyrocketed. In order to manage this information and render it useful to others, the field of biocuration was born. Any database that organizes scientific knowledge – UniProt (the Universal Protein resource), FlyBase (database for that very important model organism, Drosophila), PharmGKB (a database focused on how genes and drugs interact), etc – depends on curators to keep the information up to date and easy to use.

And so it is with 23andMe. The genetic testing kit is one part of the product, but the other part is information – what knowledge is there about associations between the SNPs on our platform and health traits or conditions? What does your particular data mean? The science is far from exhausted on this subject, and in order to stay up to date with the research, 23andMe spends a lot of effort on curating the scientific literature for new genetic associations and presenting the information on our website for our customers.

Day to day, this means that we keep track of papers recently published in scientific journals, skim through to find ones that may have promising findings, and then vet these more thoroughly to see if they pass our stringent scientific standards. If they do, we extract the bits of information we need and put the bits together in reports that will eventually become part of the content on the website. It’s a job that definitely benefits from an organized system and an eye for detail – as well as a sense of curiosity.

After three weeks on the job, I think I’m starting to get the hang of the day to day work. Since my work is even more directly tied to the literature than it was as a graduate student in academia, I’m also developing an enhanced awareness of issues surrounding scientific publishing – those related to standardization and metadata, publication bias towards positive results, and closed vs. open access. The hardest aspect of transitioning from academia to industry hasn’t been the regular schedule, or the work environment, or the work itself, it’s been getting used to being on the other side of the pay-wall of scientific journals.

But that’s a rant for another time.

This video provides a complete hands-on workflow demonstration of the TaqMan® OpenArray™ Genotyping System.

Single nucleotide polymorphisms (SNPs, pronounced ’snips’) are single base changes within an individual’s DNA sequence.  They represent the most simple and common type of mutation, in which one nucleotide is accidentally substituted for another during DNA replication.  An example would be adenine being added to a chain where a guanine should be. 

SNPs are rather common, occuring once every 300 bases, on average.  They are typically harmless, either falling between functional genes, or not impacting the gene’s performance.  On occasion, the SNP may alter the function of the resulting protein enough to be of clinical significance.  This is often the cause for gene variations that result in disease.  SNPs can also be used in DNA fingerprinting.

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In the early 1990s when I began to research smart foods, a new book piqued my interest titled, The Metabolic Basis of Inherited Disorders, 6th ed. McGraw-Hill, New York: 2649-2680, 1989. The idea is that your metabolic type (based on chemical, electrical, and genetic tags that switch on and off)  is connected to what you eat. The notion led me to begin a reading list. This arena now is a branch of nutritional epigenetics. Within human ecology, it compares the latest research in nutritional genomics/epigenetics to how smart foods (foods tailored to your genetic signature) influence risk of chronic disease. What  you eat shows up in your genetic signature.The longer science studies the entire genome (rather than the specific SNPs for certain chronic diseases) the more information will be forthcoming on how food and lifestyle influence your health based on the genes you inherited.
According to the National Institutes of Health, “Your lifestyle, the food you eat, and where you live and work can all affect how you respond to medicines. But another key factor is your DNA, which contains your genes. Scientists are trying to figure out how the make-up of your DNA can contribute to the way you respond to medicines, including pain-killers with codeine like Tylenol®#3, antidepressants like Prozac®, and many blood pressure and asthma medicines.Scientific discoveries made through this research may provide information to guide doctors in prescribing the right amount of the right medicine (or foods, lifestyles, and supplements) for you. According to the National Institutes of Health , the institute “aims to improve the health of all Americans through medical research that solves mysteries about how the human body normally works—and how and why it doesn’t work, when disease occurs. One goal of this research is to help improve the good effects of medicines while preventing bad reactions.”How do your genes respond to what you eat? Are you getting tired of the slogan “smart foods for intelligent people?” How many diet-by-DNA book titles are there? Books on smarter foods? Tailored menus? Extracts of plants? DNA tests for ancestry? Ancestry and eating? According to Dr. Fredric D. Abramson, Ph.D, S.M., President and CEO of AlphaGenics, Inc., “Genes are distributed, function, and work in such ways that nearly every reasonable diet could work well in about six percent of the population.”There is a strong connection between nutrition and genotype, especially in regards to your cardiovascular and central nervous system health. So you need to tailor foods intelligently to your genetic expression. The media buzz about ‘intelligent’ foods or ‘smart’ foods really means eating clean, safe, whole foods based on what your individual genes need to thrive. Not all your genes would be tested.

Or instead of a test, you could go by your body measurements, as outlined in naturopathic doctor, Peter D’Adamo’s book, the Genotype Diet.  If you’re interested in some free food information research, you might start at Food Resource, an online source of science-based and business savvy information for the food industry at Oregon State University.

What happens when diet books for your condition aren’t working for you? Maybe salt restriction isn’t working but exercise is for your condition. How do your genes respond to nutrition and nourishment? Are your genes intelligent, conscious, and communicating with you about their nutritional needs? If they are, so are the foods you eat. Your genes interact and collaborate as a team.

The language of communication is written in the human genome, in your individual genetic signature—in your DNA, in particular SNPs, and in all your genes and cellular material. Even your blood type is expressed in all the cells of your body. How does all this information signal you about what ‘smart’ foods and nutraceuticals to choose in order to help prevent or delay chronic disease for which your genes may put you at risk?

Nutritional genomics  and epigenetics are buzz words in the news. Tiny tags switch good genes on and bad genes off based on what you eat or the supplements you take, according to documentaries on resveratrol and green tea extract (acting as metholizers).

Testing DNA for ancestry and DNA-driven nutrition also bridge gaps in regard to customizing smarter foods to your genotype. Phenomics is about customized healthcare and medicine tailored to your genetic profile. Pharmacogenomics is about tailoring your medicine dosages to your genetic profile, but not all your genes are tested. The nutrition angle remains to ask the question: how smart do you want your food to be? And what should you know about tailoring your food to your genes or metabolic body type? We all eat on the molecular level, the chemical level, and yes, the atomic level.

Resveratrol is big news. Many people take resveratrol capsules and decaffeinated green tea capsules daily. The Genotype Diet book also mentions resveratrol. Check out some of the reviews on resveratrol and decide for yourself which product meets your requirements for standardization. Look for validation.  When you read a review of resveratrol or any other supplement, ask whether it is an objective review or is the review made by someone selling a product? Check it all out.

Single nucleotide polymorphisms (SNPs) are 1 base pair differences in the genetic code when compared to same sequence from another individual. Many population geneticists who study human genetics compare and contrast SNPs between different populations to understand ancestry and genaology. A new database of non-human primate SNPs, MonkeySNP, has been recently released, and was announced in the journal Bioinformatics.

I don’t regularly announce such news, but I consider this a pretty significant tool for any researchers who are studying primate diversity. As you may know many primate species are severely endangered and any successful conservation effort requires an understanding of the genetic diversity of the surviving population. This database will help currate this genetic diversity.

But the database is rather limited right now. Only 827 SNPs are listed, and are only macaque SNPs. I’m hopeful that as the genes and genomes of more primates species and individuals are sequenced this database will grow. In the mean time, I suggest you bookmark this site and keep an eye on it.

S. Khouangsathiene, C. Pearson, S. Street, B. Ferguson, C. Dubay (2008). MonkeySNP: a web portal for non-human primate single nucleotide polymorphisms Bioinformatics, 24 (22), 2645-2646 DOI: 10.1093/bioinformatics/btn493

I’m sure you have heard it a few time, “I have X, Y, Z condition…It’s genetic!” The fact is that only a few conditions such as Huntington’s Disease, Tay-Sachs Disease, and Down Syndrome are considered genetic disorders.  “What about the rest? I just know that my diabetes, cholesterol issues, sleeping problems, high blood pressure, and the tumor I had removed last week are genetic…my mom had it!”

It’s not that you have a certain gene but it’s the variation of that gene that makes it easier for you to develop a dysfunction that leads to disease. These genetic variations are called Single Nucleotide Polymorphisms or SNP’s. In essence, they are what make you biologically different from your neighbor. When your cells divide they must copy their DNA. They should look exactly the same, however, variations commonly occur that ultimately determines how humans develop diseases and respond to pathogens (things that cause disease), chemicals, drugs, vaccines, and other agents.

Here is an example of a SNP:  Let’s say that a particular gene’s purpose was to process Folic Acid. If you have a genetic variation that causes the way you process Folic Acid, you may need to supplement 500x more Folic Acid just to function correctly. Your run-of-the-mill multi-vitamin or prenatal vitamin won’t cut it. Folic Acid is needed for proper cell division/repair which is a huge problem in cancer patients and women planing to become pregnant. Those with SNP’s that disturb Folic Acid  may also be prone to anemia, cause neurological problems in developing fetuses, and disrupt behavior such as ADD, anxiety disorder, and depression.  Thanks to the Human Genome Project we were able to identify thousands of SNP’s that play a role in a person’s chances to develop diseases.

SNP’s can effect:
•    Hormonal/Endocrine System- Estrogen Positive Cancers, Endometriosis, Pre/Post Menopause  Complications, Osteoporosis, Cardiovascular Disease, Inflammation
•    Cardiac system- Cardiovascular Diseases, Cholesterol, Blood Pressure, Oxidative Stress
•    Detoxification System- Cancers, Chronic Fatigue, Adverse Drug Reactions, Allergies
•    Energy Production System- Cancers, Chronic Fatigue, Fibromyalgia,
•    Neurological System- Alzheimer’s, Parkinson’s Disease, Depression, Anxiety, Insomnia, Seizures, ADD, Autism
•    Structural System- Osteoporosis, Inflammation, Vitamin D Abnormalities
•    Immune System- Allergies, Autoimmune Disease, Cancers

Testing for these genetic variations, or SNP’s, can be done in adulthood as well as childhood. This means that we can now develop a life-long nutritional plan that will offset these potentially deadly SNP’s.  This is a breakthrough, a breakthrough that can accurately show risk of a future disease BEFORE IT EVER STARTS.

True Health and Dr. Brady Hurst now offers testing for SNP’s. With this information, Dr. Brady develops a personalized plan that will give your body what it needs to override these genetic differences and live a long health life with the lowest risk of developing disease.

If you mention this blog post, we will grant a %10 discount off your initial consultation.

Dr. Brady is a certified Chiropractic Neurologist and an authority in field of Functional Medicine. People from all over the world call on him for their nutritional management and prevention of diseases.

Dr. Brady Hurst
True Health Center for Advanced Alternative Medicine and Chiropractic.
www.TrueHealthDC.com

Mutations in genes involved in the insulin/IGF-1 signaling pathway (IIS) improve longevity in animal models, but there is minimal evidence that mutations in human homologues of genes in this pathway are associated with a long-lived phenotype.

In order to find such evidence, one could take a traditional forward genetics approach and study populations of centenarians. A recent paper in PNAS by Willcox et al. looked at a population of long-lived Japanese men. DNA samples were taken from men enrolled in the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS). These men enrolled in the study throughout 1991-1993, and had a mean age of 77.9. From this population of volunteers, “long-lived” participants were defined as those who survived to 95 years old or older (n= 213) and “average-lived” controls as those who died before 81 years (n=402).  

The researchers then used a candidate approach to select the genes to analyze as potential “longevity genes” from the insulin/IGF-1 pathway. They selected this pathway to focus their candidate search because insulin signaling is conserved through evolution; furthermore, from mutation analysis and knockout models in many model organisms, we know that decreasing insulin/IGF-1 signaling increases lifespan.  These phenotypes occur through regulation of FOXO transcription factors and their homologues. The authors hypothesized that single nucleotide polymorphisms (SNPs) in FOXO related genes could be responsible for the differing longevity phenotypes between the long-lived and average-lived cohorts. Five genes, ADIPOQ, FOXO1A, FOXO3A, SIRT1, and COQ7 were selected as candidates, and three SNPs were analyzed per gene. Despite the small number of genes and SNPs analyzed, the researchers identified FOXO3A as being significantly associated with the long-lived phenotype. Genotype analysis revealed that long-lived study participants had one or more copies of the “G” allele in the FOXO3A gene. The authors state that this finding is especially exciting because the FOXO family of proteins are closely related to the C. elegans protein, DAF-16, which has been shown to protect cells from oxidative stress, which could be a “plausible mechanism of action for modification of human aging.”

Additionally, these long-lived men also had significantly lower plasma insulin, low cancer incidence, cardiovascular disease, had good self reported health, and had high physical and cognitive function at the time of their enrollment regardless of their genotype. The decreased incidence of cardiovascular disease and cancer was also associated with the presence of the “G” allele. It is possible that remaining healthy phenotypes could be regulated by alternative genes not analyzed in this study.

As the authors address in their introduction, many previously published searches for longevity genes do not show reproducibility across populations (“with the exception of APOE”), so it will be intriguing to determine if this FOXO3A “G” allele will transcend various populations of centenarians. 

These are some of the downgrades or cautious analyst calls we are seeing this Thursday morning:

• AerCap (AER) Cut to Hold at Citigroup.
• Amerigroup (AGP) Started as Neutral at B of A.
• Lehman (LEH) estimates cut at Citigroup.
• Novo Nordisk (NVO) Started as Neutral at UBS.
• Salesforce.com (CRM) Cut to Neutral at Piper Jaffray.
• Synopsys (SNPS) Cut to Sell at Citigroup.

Jon C. Ogg
August 21, 2008