Posts Tagged ‘Translational Medicine’

LIVE 3:15PM – 5:00PM US-India BioPharma & Healthcare Summit, June 2, 2016, Marriott Cambridge, MA



Leaders in Pharmaceutical Business Intelligence (LPBI) Group

will cover in Real Time using Social Media the

10th US-India BioPharma & Healthcare Summit,

June 2, 2016

Aviva Lev-Ari, PhD, RN will be streaming LIVE from the 

Marriott Cambridge, MA







3-15 PM – 4-05 PM Panel Discussion: Cardiovascular and Metabolic Diseases- Matters of the heart and the body

Dr. Michael Rosenblatt, Executive Vice President & Chief Medical Officer, Merck & Co.

  • What are the trends
  • Hoe Genomics affects
  • Effect behavior by data from Devices like FitBit
  • Gene Editing
  • COllaboration potential with India


  • Neil McDonnell, PharmD, Chief Executive Officer, Metacrine
  • Dr. Anthony Muslin, Vice President, Head of the Cardiovascular & Fibrosis Unit, Sanofi
          1. Area of study: insulin sensitizers
          2. NASH — effects of insulin sensitizers on
          3. CVD — effects of insulin sensitizers on
          4. CNS — effects of insulin sensitizers on
          5. Genomic in Metabolic: Blocking hormone
          6. Two drugs that showed effect on CVD
          7. Antidiabetics inn NASH
          8. Diabetes and Renal
          9. Collaboration with India: VC, Pharma, Seizened Management Teams
  • Dr. Robert Plenge, VP and Head of Translational Medicine, Merck Research Labs
  1. Cholesterol – gene editing vs drug for the mutation
  2. Data analytics of big data — build Teams with al capabilities
  • Dr. Arthur Tzianabos, President & CEO, Homology Medicines
  1. Genomics and Genetics – few have been studied yet,
  2. genes to be targeted for editing
  3. In CVD – Gene Editing – cure disease at the origin: repair the gene transcription
  4. Ittalia, Editas
  5. In vivo genetics: Viruses and nanoparticles
  • Dr. Murali Vemula, Founder & President, Nivarta
  1. CVD
  2. Small molecule

Questions from the Floor

  • Burden of disease is high in India and CHina, Lovostatin, Metformin, Anti-Hypertensive — Decrease the burden og the disease
  • India has capabilities in IT — Harness that to Genomics?
4-05 PM – 4-55 PM Panel Discussion: Regulatory Policies to foster R&D Innovation

Dr. William Chin, Executive Vice President, PhRMA

  1. Regulatory – Roadblock and barriers
  2. Regulatory – Catalist
  3. This is a Bias: Partners with Academia, Industry Cell therapy, CRISPR
  4. Biomarkers – Approval by Regulator is not forthcoming


  • Dr. Ariz Ahammed IAS, Joint Secretary, Department of Pharmaceuticals, Govt. of India
  • Dr. Christopher Corsico, Chief Medical Officer, Boehringer Ingelheim GmbH
  1. If benefit is clear – Regulatory will act fast to accelerate
  2. Gov’t vs Regulators that are TOO much partnering with Companies applying for approval
  • Rajiv Kaul, Portfolio Manager, Fidelity Investments
  1. Intellectual Capital from all over the WOrld arrive to Cambridge
  2. Gov’t and industry to come together otherwise the Cost of Capital is too high
  3. Regulatory is necessary for Public Safety
  4. Regulatory is a barrier if a company stock depends 100% on Approval  – Colatico – approved in 3 Month
  5. Speed is important to Patients and to investors and to Patients
  6. New classification Noval Inventions: Fast approval – investors like this class of drugs
  • K.L. Sharma, IAS, Joint Secretary, Ministry of Health & Family Welfare, Govt. of India
  1. Public Health is of concern
  2. safety and efficacy is of great importance
  3. sociopolitical factors affect various States in India – Harmonization need be accomplished across States
  • Dr. Tsutomu Une, Corporate Advisor, Daiichi Sankyo Co., Ltd.
  1. Understanding he Patient and the family
  2. Priorities for patints vs Industry selfishness

Questions from the Floor

  1. Drug – device not fast approve – WHAT need to be done to accelerate this process
  2. Exploratory Development Submission —  New Scientific Concept  – concept and the Science behind and value – educate the Regulator – harmonize among regulatory agencies
  3. Concept presenented to Investors EARLY before we have the data
4-55 PM – 5-25 PM Participants comments
5-25 PM – 5-30 PM Closing Remarks

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The Need for an Informatics Solution in Translational Medicine

Curator: Larry H. Bernstein, MD, FCAP




White paper



Informatics Designed for the Translational Scientist Developing treatments that take individual variability into account (“personalized medicine”) has given rise to a new discipline in science: translational research or translational medicine. Scientists in this field work to translate biological phenomena into targeted, evidence-based medicines that improve health and treat disease by more optimally matching drugs and individuals. Currently, at least 95 percent of pharmaceutical companies are performing translational research and the translational efforts are driving many of the new therapies entering the clinic today. But those advances don’t come for free. According to the National Center for Advancing Translational Science, translational medicine has “increased research costs and complexity,” and is on par with more traditional clinical challenges of recruiting, study design, and regulatory burdens in driving clinical study costs.


1 It enables translational researchers to easily search, access, and integrate complex, multivariate data, leading to proof or refutation of hypotheses and new questions and discoveries.

2 It’s designed and built from the ground up to serve translational scientists; an out-of-the-box solution, not a generic solution topped off for translational purposes.

3 The universe of supported data types is flexible and ever-expanding as new data types are identified as useful for translational research.

4 It leverages the cloud to improve productivity and collaboration while lowering total costs.


Current tools do not enable the translational researchers to engage directly and intuitively with the available data to affirm or refute a hypothesis. There is no easy means for scientists to search for and access integrated data so they can better identify and characterize biomarkers and develop the most efficient drug to treat a specific disease. Even the types of questions they can ask of the data are limited.

To gain the computational and bioinformatics power to analyze all the data, translational scientists most often call on IT counterparts or biostatisticians and data scientists to create custom applications. This creates its own problems. First, it can restrict the type of inquiry researchers can pose, inadequately focusing on the aftermath of an instrument run, for example. Secondly, it can take several iterations (not to mention days or weeks) before IT is able to serve up what the researcher needs – even if they deliver exactly what the researcher asked for.

New science needs new information solutions – self-service solutions that enable any scientist to engage directly with data more quickly and at a lower cost. These new solutions must address a different type of workflow, one that starts with a scientific question rather than the outcome of an experiment.

“Unless you can start harnessing data and making sense of it, in an automated way, with systems that are engineered to solve big data problems, you’ll be overwhelmed by the data very quickly,” says Nicolas Encina, vice president of the Innovation Lab at PerkinElmer. “You can no longer effectively manage this data manually and you certainly can’t analyze or process it manually either.”


“Too often, people think about data oriented from the informaticist’s or technologist’s point of view,” says Daniel Weaver, senior product manager for translational medicine informatics. “PerkinElmer Signals™ for Translational presents the data in a way a regular scientist will be able to understand. It’s organized around concepts a scientist gets, around the subjects of clinical trials, patient visits, samples collected, etc.”

Before PerkinElmer Signals™ for Translational, most scientists would query data, for example, based on results from a certain day or sample run. To glean more knowledge required manual analysis of multiple data sets layered in Excel spreadsheets. With the growth of data from R&D and clinical research, this task became even more challenging. The new self-service PerkinElmer Signals™ for Translational platform, however, automatically gathers disparate data to answer more open-ended questions, such as, “Do elderly female patients with KRAS mutant breast cancer have increased localization of protein ‘X’ to the nucleus?”

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The Relation between Coagulation and Cancer affects Supportive Treatments

Demet Sag, PhD


Coagulation and Cancer

There are several supportive therapies for cancer patients. One of the most important one is controlling the blood intake. This is sometimes observe keeping the blood cell count at certain levels, or providing safe blood/blood products to avoid any contaminations or infections,

The relation between cancer and coagulation was known for a long time but it was becoming clear recently.  Having coagulapathies also reduce the survival of patients since they can’t response to given treatments. Thus, it is necessary to give supportive therapies to control the coagulation. Problems in coagulation may develop from inherited (genetics), or acquired due to given therapies that cause varying abnormalities towards bleeding or thrombose at many levels.  The thrombotic events are important since they are the second leading cause of death in cancer patients (after cancer itself).  The presence of these coagulopathies determines the survival rate, length of survival either short-term or long-term, as well as relapses.

Cancer and Coagulation from start to finish:

Thrombotic risk factors in cancer patients

  1. Patient related
  2. Cancer related
  3. Treatment related


  1. Patient Related:
  • Older age
  • Bed rest
  • Obesity
  • Previous thrombosis
  • Prothrombotic mutations
  • High leukocyte and platelet counts
  • Comorbidities
  1. Cancer related:

a. Site of cancer:

  • brain,
  • pancreas,
  • kidney,
  • stomach,
  • lung,
  • bladder,
  • gynecologic,
  • hematologic malignancies

b. Stage of cancer:

  • advanced stage and
  • initial period after diagnosis
  1. Treatments:
  • Hospitalization
  • Surgery
  • Chemo- and
  • hormonal therapy
  • Anti-angiogenic therapy
  • Erythropoiesis stimulating agents
  • Blood transfusions
  • CVC, central venous catheters
  • Radiations

Thromboembolic events can be venous or arterial.

Venous events include

  • deep vein thrombosis (DVT),
  • pulmonary embolism (PE)

together categorized as venous thromboembolism (VTE).

Arterial events, include

  • stroke, myocardial infarction and
  • arterial embolism.

increase in the rate of VTEIncrease in the rate of venous thromboembolism (VTE) over time. Results are presented as annual rates of deep venous thrombosis (DVT), pulmonary embolism (PE) without deep venous thrombosis, and both between 1995 and 2003. Significant trends for increasing rates were observed for all 3 diagnoses (P < .0001). The rate of increase was found to be greater in the subgroup of patients who received chemotherapy. Error bars represent 95% confidence intervals.

There is an increase in both venous and arterial eventsrecently with “unacceptably high” event rates documented in the most contemporary studies:

There are significant consequences to the occurrence of thromboembolism in this setting:

  • requirement for long-term anticoagulation,
  • a 12% annual risk of bleeding complications,
  • an up to 21% annual risk of recurrent VTEand
  • potential impact on chemotherapy delivery and patient quality of life.


Therapeutic interventions enhance the risk of VTE in cancer.

  • Cancer patients undergoing surgery have a two-fold increased risk of postoperative VTE as compared to non-cancer patients, and this elevation in risk can persist for a period up to 7 weeks
  • Hospitalization also substantially increases the risk of developing VTE in cancer patients (OR 2.34, 95% CI 1.63 – 3.36)
  • The use of systemic chemotherapy is associated with a 2-to 6-fold increased risk of VTE compared to the general population.
  • Anti-angiogenic agents, particularly thalidomide and lenalidomide, have been associated with high rates of VTE when given in combination with dexamethasone or chemotherapy.
  • Bevacizumab-containing regimens have been associated with increased risk for an arterial thromboembolic event (hazard ratio [HR] 2.0, 95% CI 1.05- 3.75) but the data for risk of VTE are conflicting
  • Sunitinib and sorafenib, agents targeting the angiogenesis pathway, have also similarly been associated with elevated risk for arterial (but not venous) events [RR 3.03 (95% CI, 1.25 to 7.37)]

Anticoagulants and Cancer Coagulopathies

There are many studies on coagulation and use of anti-coagulants yet the same patient may also thrombose at any given time so the coagulant therapies should be under close surveillance.  The study (PMID:111278600) by Palereti et all in 2000 to many  compared this issue.


Palereti et al. showed that:

“The outcome of anticoagulation courses in 95 patients with malignancy with those of 733 patients without malignancy. All patients were participants in a large, nation-wide population study and were prospectively followed from the initiation of their oral anticoagulant therapy.

Based on 744 patient-years of treatment and follow-up, the rates of major (5.4% vs 0.9%), minor (16.2% vs 3.6%) and total (21.6% vs 4.5%) bleeding were statistically significantly higher in cancer patients compared with patients without cancer.

Bleeding was also a more frequent cause of early anticoagulation withdrawal in patients with malignancy (4.2% vs. 0.7%; p <0.01; RR 6.2 (95% CI 1.95-19.4). There was a trend towards a higher rate of thrombotic complications in cancer patients (6.8% vs. 2.5%; p = 0.058; RR 2.5 [CI 0.96-6.5]) but this did not achieve statistical significance”.

They concluded that “patients with malignancy treated with oral anticoagulants have a higher rate of bleeding and possibly an increased risk of recurrent thrombosis compared with patients without malignancy.”



Cancer and Coagulation in more detail at Molecular Level:

Cancer is a complex disease from its initiation to its treatment. In the body the response to drugs generates side effects for being foreign (immune responses and inflammation), toxic, or disturbing the hemostasis of the coagulation system. In addition, activation of oncogenic pathways cab also be activated that may not only effect the development of the cancer but also may induce oncogenes to activate dormant cancer cells. In the coagulation system the balance is important to keep anti-coagulant state, with oversimplification, such as having certain number of tissue factor (TF) that is a receptor determines the anticoagulant state. However, certain pro-oncogenic genes like RAS, EGFR, HER2, MET, SHH and loss of tumor suppressors (PTEN, TP53) change the gene regulation so they alter the expression, activity and vesicular release of coagulation effectors, as exemplified by tissue factor (TF). As a result, there is a bridge between the coagulation-related genes (coagulome) and specific cancer coagulapathies, such as in glioblastoma multiforme (GBM), medulloblastoma (MB), etc. Therefore, these coagulome can be a great target not only to inhibit angiogenesis and tumor growth but also prevent any coagulopathies, use in single genomics/circulating cancer cells as well as grading the level of cancer specifically.

Here in this figures Tumor-hemostatic system interactions http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f1.gif?v=1&t=ifxvwlxk&s=62da078fc1c8d85d58c256e83954181a16f7463b

and Microparticle (MP) production and activities in cancer are well summarized http://onlinelibrary.wiley.com/store/10.1111/jth.12075/asset/image_n/jth12075_f2.gif?v=1&t=ifxvwlzv&s=13f9b775d7417f12e3ae5f879c09ac8825918d61

coagulation and cancer




Tumor-hemostatic system interactions. Tumor cells activate the hemostatic system in multiple ways. Tumor cells may release procoagulant tissue factor, cancer procoagulant and microparticles (MP) that can directly activate the coagulation cascade. Tumor cells may also activate the host’s hemostatic cells (endothelial cells and platelets), by either release of soluble factors or by direct adhesive contact, thus further enhancing clotting activation.


 tumor and coagulation cascade



Microparticle (MP) production and activities in cancer. Tumor cells actively release MP but also promote MP formation by platelets. Tissue factor (TF) and phosphatidylserine (PS) expression on the surfaces of both platelet- and tumor-derived MP are involved in blood clotting activation and thrombus formation. On the other hand, the elevated content of proangiogenic factors in platelet-derived MP (VEGF, vascular endothelial growth factor, FGF, fibroblast growth factor, PDGF, platelet-derived growth factor), render these elements also important mediators of the neangiogenesis process. Finally, intracellular transfer of MP may occur between cancer cells, leading to a horizontal propagation of oncogenes and amplification of their angiogenic phenotype.


Immune Response and Cancer with Coagulopathies:

  1. I. Goufman et al also suggested that plasma level of IgG autoantibodies to plasminogen changes during cancer coagulopathies.

Their data based on ELISA measurements of their patients:

  • with benign prostatic hyperplasia (n=25),
  • prostatic cancer (n=17),
  • lung cancer (n=15), and
  • healthy volunteers (n=44).

High levels of IgG to plasminogen were found

  • in 2 (12%) of 17 healthy women, in 1 (3.6%) of 27 specimens in a healthy man,
  • in 17 (68%) of 25 specimens in prostatic cancer,
  • in 10 (59%) of 17 specimens in lung cancer,
  • in 5 (30%) of 15 specimens in benign prostatic hyperplasia.

Comparison of plasma levels of anti-plasminogen IgG by affinity chromatography showed 3-fold higher levels in patients with prostatic cancer vs. healthy men.

Structure and function of platelet receptors initiating blood clotting.

There is a missed or overlooked concept about coagulation and cancer. In their article they mainly focused on the structure and function of key platelet receptors taking role in the thorombus formation and coagulation.

At the clinical level, recent studies reveal the link between coagulation and other pathophysiological processes, including platelet activation, inflammation, cancer, the immune response, and/or infectious diseases. These links are likely to underpin the coagulopathy associated with risk factors for venous thromboembolic (VTE) and deep vein thrombosis (DVT). At the molecular level, the interactions between platelet-specific receptors and coagulation factors could help explain coagulopathy associated with aberrant platelet function, as well as revealing new approaches targeting platelet receptors in diagnosis or treatment of VTE or DVT. Glycoprotein (GP)Ibα, the major ligand-binding subunit of the platelet GPIb-IX-V complex, that binds the adhesive ligand, von Willebrand factor (VWF), is co-associated with the platelet-specific collagen receptor, GPVI. The GPIb-IX-V/GPVI adheso-signaling complex not only initiates platelet activation and aggregation (thrombus formation) in response to vascular injury or disease but GPIbα also regulates coagulation through a specific interaction with thrombin and other coagulation factors.

Clinical Data and Some Samples of Biomarkers:

Development of biomarkers and management of cancer coagulapathies are underway since there are times this coagulapathies may be as deadly as the cancer itself.

The sample study and data from Reference: Alok A. Khorana, M.D. Cancer and Coagulation. Am J Hematol. 2012 May; 87(Suppl 1): S82–S87. Published online 2012 Mar 3. doi:  10.1002/ajh.23143 PMCID: PMC3495606. NIHMSID: NIHMS386379

Resource: PMC full text: Am J Hematol. Author manuscript; available in PMC 2013 May 1.

Published in final edited form as:

Am J Hematol. 2012 May; 87(Suppl 1): S82–S87.

Published online 2012 Mar 3. doi:  10.1002/ajh.23143

Copyright/License ►Request permission to reuse

Table 1

Selected Clinical Risk Factors and Biomarkers for Cancer-associated Thrombosis

Patient-associated risk factors
 Older age
 Medical comorbidities
 Prior history of thrombosis
Cancer-associated risk factors
 Primary site
 Cancer histology (higher for adenocarcinoma than squamous cell)
 Time after initial diagnosis (highest in first 3-6 months)
Treatment-associated risk factors
 Anti-angiogenic agents
 Hormonal therapy
 Erythropoiesis-stimulating agents
 Indwelling venous access devices
Currently widely available
 Platelet count (≥350,000/mm3)23
 Leukocyte count (> 11,000/mm3)23
 Hemoglobin (< 10 g/dL)23
Investigational and/or not widely available
 Tissue factor (antigen expression, circulating microparticles, antigen or activity)3133



Table 2

Predictive Model for chemotherapy-associated VTE23

Patient Characteristics Risk Score
Site of cancer
 Very high risk (stomach, pancreas) 2
 High risk (lung, lymphoma, gynecologic, bladder, testicular) 1
Prechemotherapy platelet count 350000/mm3 or more 1
Hemoglobin level less than 10g/dl or use of red cell growth factors 1
Prechemotherapy leukocyte count more than 11000/mm3 1
Body mass index 35kg/m2 or more 1

High-risk score ≥ 3

Intermediate risk score =1-2

Low-risk score =0



Rates of VTE According to Risk Score

Study Type, f/u N Low-risk (score=0) Intermediate–risk (score =1-2) High-risk (score≥3)
Khorana et al23, 2008 Development cohort, 2.5 mos 2701 0.8% 1.8% 7.1%
Khorana et al23, 2008 Validation cohort, 2.5 mos 1365 0.3% 2% 6.7%
Kearney et al67, 2009 Retrospective, 2 yrs 112 5% 15.9% 41.4%
Price et al68, 2010 Retrospective, pancreatic, NA 108 – * 14% 27%
Ay et al36, 2010 Prospective, 643 days 819 1.5% 9.6% (score= 2) 17.7%
3.8% (score=1)
Khorana et al69, 2010 Prospective**, 3 mos 30 – *** 27%
Moore et al2, 2011 Retrospective, cisplatin-based chemotherapy only 932 13% 17.1% 28.2%
Mandala et al37, 2011 Retrospective, phase I patients only, 2 months 1,415 1.5% 4.8% 12.9%

NA=not available

*Pancreatic cancer patients are assigned a score of 2 based on site of cancer and therefore there were no patients in the low-risk category

**included 4-weekly screening ultrasonography

***enrolled only high-risk patients

Table 4

ASCO and NCCN Recommendations for Treatment of VTE in Cancer

Initial treatment
LMWH is the preferred approach for the initial 5-10 days LMWH, UFH or factor Xa antagonists according to patient’s characteristics and clinical situation
Long term treatment
LMWH for at least 6 months is preferred. LMWH is preferred
VKA are acceptable when LMWH is not available. Indefinite anticoagulation in patients with active cancer or persistent risk factors
Indefinite anticoagulation in patients with active cancer.
Thrombolytic therapy in initial treatment
Restricted to patients with life- or limb-threatening thrombotic events Restricted to massive or submassive PE with moderate or severe right ventricular enlargement or dysfunction
Inferior vena cava filters
Restricted to patients with contraindications to anticoagulation or recurrent VTE despite adequate long-term LMWH Restricted to patients with contraindications to or failure of anticoagulation, cardiac or pulmonary dysfunction severe enough to make any new PE life-threatening or multiple PE with chronic pulmonary hypertension
Treatment of catheter-related thrombosis
NA LMWH or VKA for as long as catheter is in place or for 1 to 3 months after catheter removal
 Soluble P-selectin (> 53.1 ng/mL)65
 Factor VIII66
 Prothrombin fragment F 1+2 (>358 pmol/L) 26




Genome Analysis at the crossroads of Coagulation and Cancer

, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disordersGenome Medicine, 2015, 7,1

Phenotype similarity clustering of cases according to HPO terms. Heat map showing pairwise phenotypic similarity among affected members of pedigrees, cases with classical syndromes and cases with variants in ACTN1. The groups are ordered through complete-linkage hierarchical clustering within each class and P values of phenotypic similarity are shown in a scatterplot superimposed over a histogram showing the distribution of P values.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

Phenotype clusters 18 and 29. Illustrative subgraphs of the HPO showing terms for the phenotype clusters 18 (15 cases) and 29 (16 cases). Arrows indicate direct (solid) or indirect (dashed) is a relations between terms in the ontology. DMPV: decreased mean platelet volume; PA: phenotypic abnormality; Plt-agg: platelet aggregation abnormality.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5
Download authors’ original image

s13073-015-0151-5-5 s13073-015-0151-5-6

Rare variants identified inACTN1
Case Transcript variant ENST00000394419 Protein variant ENSP00000377941.4 HGMD variant Classification PLT, ×109/L MPV, fL, and/or presence of macrothrombocytes Bleeding phenotype
B200726 14:69392385 A/C F37C No LPV 57 18.1, macrothrombocytes None
B200207 14:69392358 C/T R46Q Yes PV 53 >13, macrothrombocytes None
B200209 PV 76 >13, macrothrombocytes Mild
B200212 PV 98 >13, macrothrombocytes None
B200254 PV 34 >13, macrothrombocytes None
B200735 PV 52 12.0, macrothrombocytes None
B200746 14:69392359 G/A R46W No LPV 96 15.2, macrothrombocytes None
B200197 14:69392344 G/C Q51E No LPV 113 >13, macrothrombocytes Mild
B200836 14:69387750 C/T V105I Yes PV 53 NA, macrothrombocytes None
B200837a PV 75 NA, macrothrombocytes None
B200671 14:69371375 C/T E225K Yes PV 97 13.7, macrothrombocytes Mild
B200716 PV 82 15.0, macrothrombocytes None
B200398 14:69369274 C/T V228I No LPV 31 15.4, macrothrombocytes Mild
B200280 14:69358897 C/T R320Q No LPV 108 15.1, macrothrombocytes Mild
B200281a LPV 111 13.9, macrothrombocytes None
B200835 14:69352254 C/T A425T No VUS 50 10.0, no macrothrombocytes Mild
B200283 14:69349768 A/G L547P No LPV 91 13.3, macrothrombocytes Mild
B200048 14:69349648 G/A A587V No VUS 390 NA, no macrothrombocytes Mild
B200284 14:69346749 G/T T737N No LPV 60 16.1, macrothrombocytes Mild
B200285a LPV 48 16.8, macrothrombocytes Mild
B200741 14:69346747 G/A R738W Yes PV 94 12.9, macrothrombocytes None
B200745 PV 70 14.5, macrothrombocytes None
B200750 14:69346746 C/T R738Q No LPV 106 14.0, macrothrombocytes None
B200414 14:69346704 C/G R752P No LPV 121 11.4, macrothrombocytes Mild

aAffected family member.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Rare variants identified inMYH9and validated by Sanger sequencing
Case Transcript variant ENST00000216181 Protein variant ENSP00000216181 HGMD variant Classification PLT, ×109/L MPV, fL and/or presence of macrothrombocytes OtherMYH9-RD characteristics
B200760 22:36744995 G/A S96L Yes PV 180 Macrothrombocytes None
B200771 22:36705438 C/A D578Y No VUS 184 10.1 None
B200423 22:36696237 G/A A971V No VUS 262 10.2 None
B200024 22:36691696 A/G S1114P Yes VUS 164 NA None
B200245 VUS 53 11.1, Macrothrombocytes None
B200243 22:36691115 G/A R1165C Yes PV 22 Macrothrombocytes None
B200594 PV 46 Macrothrombocytes None
B200595a PV 61 Macrothrombocytes None
B200614 22:36688151 C/T D1409N No VUS 319 9.8 None
B200752 VUS 149 10.1, Macrothrombocytes None
B200855 VUS 95 16.8, Macrothrombocytes None
B200208 22:36688106 C/T D1424N Yes PV 99 13.6 None
B200010 22:36685249 G/C S1480W No VUS 244 NA None
B200244 22:36678800 G/A R1933X Yes PV 26 Macrothrombocytes Döhle inclusions

Other MYH9-RD characteristics sought were the presence of Döhle inclusions, cataract, deafness or renal pathology.

aFather of B200594.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Pathogenic and likely pathogenic variants identified in genes associated with autosomal recessive and X-linked recessive bleeding and platelet disorders
Case Position Gene Ref Alt Genotype HGMD Effecta Haematological HPO terms Other HPO terms Classification:
Variant Phenotype
B200286 3:148881737 HPS3 G C C|C Yes Abnormal splicing Bleeding with minor or no trauma, subcutaneous haemorrhage, menorrhagia, postpartum haemorrhage, impaired ADP-induced platelet aggregation, impaired epinephrine-induced platelet aggregation, epistaxis, prolonged bleeding after surgery, prolonged bleeding after dental extraction, increased mean platelet volume. Hypothyroidism, visual impairment, nystagmus, albinism. PV Explained
B200412 3:148858819 HPS3 T TA T|TA No Frameshift Impaired epinephrine-induced platelet aggregation, bleeding with minor or no trauma, subcutaneous haemorrhage, epistaxis, menorrhagia, prolonged bleeding after surgery, abnormal dense granules. Ocular albinism. LPV Possibly explained
3:148876539 HPS3 G A G|A No W593a LPV
B200068 10:103827041 HPS6 C G C|G No L604V Increased mean platelet volume. Congenital cataract, strabismus, maternal diabetes. LPV Possibly explained
10:103827554 HPS6 C G C|G No L775V LPV
B200196 X:48542673 WAS C T T Yes T45M Thrombocytopenia, abnormal bleeding, decreased mean platelet volume, abnormal platelet shape. Recurrent infections. PV Explained
B200725 X:48544145 WAS T C C Yes F128S Monocytosis, neutrophilia, thrombocytopenia, leukocytosis, subcutaneous haemorrhage, gastrointestinal haemorrhage. PV Explained
B200443 X:138633272 F9 G A A Yes R191H Reduced factor IX activity, impaired ADP-induced platelet aggregation, bleeding with minor or no trauma, spontaneous haematomas, abnormal number of dense granules. PV Partially explained
B200452 X:154124407 F8 C G G Yes S2125T Reduced factor VIII activity, persistent bleeding after trauma, prolonged bleeding after surgery, prolonged bleeding after dental extraction, bleeding requiring red cell transfusion, impaired collagen-induced platelet aggregation, bleeding with minor or no trauma, joint haemorrhage, abnormal platelet shape, abnormal number of dense granules. PV Partially explained
B200772 X:154176011 F8 A G G No F692S Reduced factor VIII activity, bruising susceptibility, impaired ADP-induced platelet aggregation, impaired collagen-induced platelet aggregation, impaired thromboxane A2 agonist-induced platelet aggregation, impaired ristocetin-induced platelet aggregation, impaired arachidonic acid-induced platelet aggregation, impaired thrombin-induced platelet aggregation, abnormal platelet granules, bleeding with minor or no trauma. LPV Possibly partially explained

Alt: alternative; Ref: reference.

aEffect considered relative to the Consensus Coding Sequence (CCDS) for each gene.

Westbury et al.

Westbury et al. Genome Medicine 2015 7:36   doi:10.1186/s13073-015-0151-5

Table 2

TFPI and TF tumor mRNA expression across clinicopathological breast cancer subtypes

  mRNA expression (tumor) Protein levels (plasma)
Characteristic Groups Total TFPI (α + β) P TFPIα P TFPIβ P TF P Total TFPI P Free TFPI P TF P
T-status T1 −0.146 0.054 −0.135 0.257 −0.084 0.201 −0.023 0.652 72.01 0.013 10.82 0.997 4.14 0.125
T2-T3 0.085 0.018 0.060 0.054 65.02 10.82 4.66
Grade G1-G2 −0.022 0.850 −0.005 0.424 −0.033 0.743 0.271 0.003 71.04 0.082 10.66 0.682 4.63 0.557
G3 −0.045 −0.113 0.004 −0.229 66.12 10.97 4.14
N-status Negative −0.109 0.091 −0.136 0.127 −0.082 0.104 0.005 0.881 69.93 0.183 10.77 0.869 4.95 0.282
Positive 0.104 0.078 0.110 0.032 66.00 10.90 4.14
ER status Positive −0.067 0.317 −0.082 0.557 −0.056 0.183 0.001 0.784 69.42 0.240 10.91 0.671 4.42 0.409
PR status Negative 0.076 0.011 0.123 0.057 65.44 10.52 5.28
Positive −0.131 0.021 −0.145 0.075 −0.112 0.014 0.085 0.244 69.81 0.195 11.19 0.175 4.32 0.246
HER2-status Negative 0.161 0.108 0.182 −0.127 65.92 10.08 5.04
Negative −0.072 0.054 −0.101 0.073 −0.041 0.154 0.004 0.731 68.45 0.893 10.68 0.287 4.47 0.428
Positive 0.313 0.301 0.228 0.103 69.09 12.05 4.78
HR status Yes 0.076 0.326 0.007 0.587 0.114 0.221 0.016 0.991 64.78 0.161 10.41 0.568 5.26 0.470
No −0.066 −0.080 −0.052 0.014 69.57 10.94 4.47
Triple-negative status Yes −0.051 0.886 −0.110 0.718 0.041 0.635 −0.158 0.326 63.21 0.072 10.06 0.345 5.23 0.969
No −0.029 −0.048 −0.027 0.055 69.73 10.99 4.57

Median values for TFPI and TF mRNA expression in tumors and protein levels in plasma according to clinically defined groups. Corresponding P-values (unadjusted) are shown. Significant P-values in bold. TFPI, tissue factor pathway inhibitor; TF, tissue factor; HER2, human epidermal growth factor receptor 2.Abbreviations: T, tumor; G, grade; N, node; ER, estrogen receptor; PR, progesterone receptor; HR, hormone receptor.

Table 3

Significant association between TFPI single nucleotide polymorphisms (SNPs) and clinicopathological characteristics and molecular subtypes

Characteristic SNP Risk allele Odds ratio 95% CI P False discovery rate
T status
T1 Reference Reference Reference Reference
T2 to T3 rs10153820 A 3.14 1.44, 6.86 0.004 0.056
TN status (ER-/PR-/HER2-negative)
No Reference Reference Reference Reference
Yes rs8176541a G 2.62 1.11, 5.35 0.026 0.092
rs3213739a G 2.58 1.34, 4.99 0.005 0.033
rs8176479a C 3.10 1.24, 7.72 0.015 0.071
rs2192824a T 2.44 1.39, 4.93 0.002 0.033
N status
Positive Reference Reference Reference Reference
Negative rs10179730 G 3.34 1.42, 7.89 0.006 0.083
Basal tumor subtype
Non-basal Reference Reference Reference Reference
Basal rs3213739a G 2.23 1.15, 4.34 0.018 0.107
rs8176479a C 2.79 1.12, 6.96 0.028 0.107
rs2192824a T 2.41 1.24, 4.65 0.009 0.107
rs10187622a C 5.20 1.17, 23.20 0.031 0.107
Luminal B tumor subtype
Non-luminal B Reference Reference Reference Reference
Luminal B rs16829086a T 2.09 1.03, 4.25 0.041 0.191
rs10179730a G 3.53 1.47, 8.46 0.005 0.066
rs10187622a T 2.73 1.24, 6.03 0.013 0.091
Normal-like tumor subtype
Non-normal-like Reference Reference Reference Reference
Normal-like rs5940 T 22.17 4.43, 110.8 0.0002 0.003

aSNPs representing a haplotype effect. SNPs are listed by ascending chromosome positions. TFPI, tissue factor pathway inhibitor; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor 2.

Table 4

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and TFPI mRNA expression in breast tumors

Probe SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
TFPIα rs2192824b Intronic C:T 0.490 −0.209 −0.180 0.029 0.200
TFPIα rs7594359b Intronic C:T 0.483 −0.219 −0.184 0.025 0.200
TFPIβ rs3213739b Intronic G:T 0.417 0.187 0.213 0.010 0.032
TFPIβ rs8176479b Intronic C:A 0.238 0.184 0.192 0.021 0.049
TFPIβ rs2192824b Intronic C:T 0.490 −0.267 −0.273 0.001 0.011
TFPIβ rs12613071b Intronic T:C 0.158 0.284 0.208 0.011 0.032
TFPIβ rs2192825b Intronic T:C 0.466 −0.251 −0.249 0.002 0.012
TFPIβ rs7594359b Intronic C:T 0.483 −0.248 −0.247 0.002 0.012
TFPIα + β rs2192824b Intronic C:T 0.490 −0.168 −0.161 0.050 0.187
TFPIα + β rs12613071b Intronic T:C 0.158 0.238 0.164 0.048 0.187
TFPIα + β rs7594359b Intronic C:T 0.483 −0.190 −0.178 0.030 0.187

aMajor:minor. bSNPs representing a haplotype effect. mRNA expression was assayed by the Agilent Human V2 Gene Expression 8x60k array, and probes for tissue factor pathway inhibitor (TFPI)α, TFPIβ and total TFPI (TFPIα + β) mRNA were analyzed. Alleles for the positive DNA strand (UCSC annotated) are shown, and SNPs are listed by ascending chromosome positions.

“Eight TFPI SNPs were found to be correlated to total TFPI protein levels in patient plasma (Table 5). The A-T-A-C-T-A-C-G haplotype composed of these eight SNPs (rs8176541-rs3213739-rs8176479-rs2192824-rs2192825-rs16829088-rs7594359-rs10153820) represented a common haplotype (frequency 0.19) with quite strong correlation to total TFPI protein; r = 0.481 (B = 14.62, P = 6.35 × 10−10). No correlation between TFPI SNPs and free TFPI protein, or between TF SNPs and TF protein in plasma was observed (P >0.05, data not shown). Adjusting for age had no effect on the correlation (data not shown).”

Table 5

Significant correlations between TFPI single nucleotide polymorphisms (SNPs) and total TFPI protein levels in plasma

Protein SNP Region Alleles a Minor allele frequency Beta r P False discovery rate
Total TFPI rs8176541b Intronic G:A 0.283 15.64 0.571 7.69 × 10−14 1.08 × 10−12
Total TFPI rs3213739b Intronic G:T 0.417 11.35 0.488 5.38 × 10−10 3.77 × 10−9
Total TFPI rs8176479b Intronic C:A 0.238 12.22 0.480 1.20 × 10−9 5.62 × 10−9
Total TFPI rs2192824b Intronic C:T 0.490 −9.88 −0.404 3.81 × 10−7 1.07 × 106
Total TFPI rs2192825b Intronic T:C 0.466 −7.55 −0.301 2.40 × 10−4 5.30 × 10−4
Total TFPI rs16829088b Intronic G:A 0.250 11.23 0.424 1.00 × 10−7 3.51 × 10−7
Total TFPI rs7594359b Intronic C:T 0.483 −6.90 −0.275 6.90 × 10−4 0.001
Total TFPI rs10153820b Near 5UTR G:A 0.125 −7.79 −0.215 0.009 0.016

aMajor:minor. bSNPs representing a haplotype effect for total tissue factor pathway inhibitor (TFPI). Alleles for the positive DNA strand (UCSC annotated) are shown.

In sum, combination of molecular physiology and genomics will improve the conditions of the patients not only to diagnose early or to monitor the disease but also to streamline the current drugs to be more efficient and therapeutic.


·         PMID: 25480646, Gardiner EE1, Andrews RK. Structure and function of platelet receptors initiating blood clotting. Adv Exp Med Biol. 2014;844:263-75. doi: 10.1007/978-1-4939-2095-2_13.


Further Reading:

Mari Tinholt, Hans Kristian Moen Vollan, Kristine Kleivi Sahlberg, Sandra Jernström, Fatemeh Kaveh, Ole Christian Lingjærde,Rolf Kåresen, Torill Sauer, Vessela Kristensen, Anne-Lise Børresen-Dale, Per Morten Sandset, Nina Iversen, Tumor expression, plasma levels and genetic polymorphisms of the coagulation inhibitor TFPI are associated with clinicopathological parameters and survival in breast cancer, in contrast to the coagulation initiator TFBreast Cancer Research, 2015, 17, 1

 Chaabane, L. Tei, L. Miragoli, L. Lattuada, M. von Wronski, F. Uggeri, V. Lorusso, S. Aime, In Vivo MR Imaging of Fibrin in a Neuroblastoma Tumor Model by Means of a Targeting Gd-Containing PeptideMolecular Imaging and Biology, 2015,

Daniela Bianconi, Alexandra Schuler, Clemens Pausz, Angelika Geroldinger, Alexandra Kaider, Heinz-Josef Lenz, Gabriela Kornek, Werner Scheithauer, Christoph C. Zielinski, Ingrid Pabinger, Cihan Ay, Gerald W. Prager, Integrin beta-3 genetic variants and risk of venous thromboembolism in colorectal cancer patients, Thrombosis Research, 2015,

Olumide B Gbolahan, Trista J Stankowski-Drengler, Abiola Ibraheem, Jessica M Engel, Adedayo A Onitilo, Management of chemotherapy-induced thromboembolism in breast cancerBreast Cancer Management, 2015, 4, 4, 187

Ami Schattner, Meital Adi, Mobile menace- floating aortic arch thrombusThe American Journal of Medicine, 2015,

Chuang-Chi Liaw, Hung Chang, Tsai-Sheng Yang, Ming-Sheng Wen, Pulmonary Venous Obstruction in Cancer Patients,Journal of Oncology, 2015, 2015, 1

Esther Rabizadeh, Izhack Cherny, Doron Lederfein, Shany Sherman, Natalia Binkovsky, Yevgenia Rosenblat, Aida Inbal, The cell-membrane prothrombinase, fibrinogen-like protein 2, promotes angiogenesis and tumor developmentThrombosis Research, 2015, 136, 1, 118

Anna Falanga, Marina Marchetti, Laura Russo, The mechanisms of cancer-associated thrombosis, Thrombosis Research,2015, 135, S8

I. Goufman, V. N. Yakovlev, N. B. Tikhonova, R. B. Aisina, K. N. Yarygin, L. I. Mukhametova, K. B. Gershkovich, D. A. Gulin,Autoantibodies to Plasminogen and Their Role in Tumor DiseasesBulletin of Experimental Biology and Medicine, 2015, 158,4, 493

Trisha A. Rettig, Julie N. Harbin, Adelaide Harrington, Leonie Dohmen, Sherry D. Fleming, Evasion and interactions of the humoral innate immune response in pathogen invasion, autoimmune disease, and cancerClinical Immunology, 2015, 160, 2,244

Sarah K Westbury, Ernest Turro, Daniel Greene, Claire Lentaigne, Anne M Kelly, Tadbir K Bariana, Ilenia Simeoni, Xavier Pillois, Antony Attwood, Steve Austin, Sjoert BG Jansen, Tamam Bakchoul, Abi Crisp-Hihn, Wendy N Erber, Rémi Favier,Nicola Foad, Michael Gattens, Jennifer D Jolley, Ri Liesner, Stuart Meacham, Carolyn M Millar, Alan T Nurden, Kathelijne Peerlinck, David J Perry, Pawan Poudel, Sol Schulman, Harald Schulze, Jonathan C Stephens, Bruce Furie, Peter N Robinson, Chris van Geet, Augusto Rendon, Keith Gomez, Michael A Laffan, Michele P Lambert, Paquita Nurden, Willem H Ouwehand, Sylvia Richardson, Andrew D Mumford, Kathleen Freson, Human phenotype ontology annotation and cluster analysis to unravel genetic defects in 707 cases with unexplained bleeding and platelet disordersGenome Medicine, 2015, 7,1

Ades, S. Kumar, M. Alam, A. Goodwin, D. Weckstein, M. Dugan, T. Ashikaga, M. Evans, C. Verschraegen, C. E. Holmes,Tumor oncogene (KRAS) status and risk of venous thrombosis in patients with metastatic colorectal cancer,Journal of Thrombosis and Haemostasis, 2015, 13, 6

Marcel Levi, Cancer-related coagulopathiesThrombosis Research, 2014, 133, S70

Axel C. Matzdorff, David Green, Management of venous thromboembolism in cancer patientsReviews in Vascular Medicine,2014, 2, 1, 24

Claude Bachmeyer, Milène Buffo, Bérénice Soyez, No Evidence Not to Prescribe Thromboprophylaxis in Hospitalized Medical Patients with Cancer, The American Journal of Medicine, 2014, 127, 7, e33

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Taslim A. Al-Hilal, Farzana Alam, Jin Woo Park, Kwangmeyung Kim, Ick Chan Kwon, Gyu Ha Ryu, Youngro Byun, Prevention effect of orally active heparin conjugate on cancer-associated thrombosisJournal of Controlled Release, 2014, 195, 155

Samridhi Sharma, Sandipan Ray, Aliasgar Moiyadi, Epari Sridhar, Sanjeeva Srivastava, Quantitative Proteomic Analysis of Meningiomas for the Identification of Surrogate Protein Markers, Scientific Reports, 2014, 4, 7140

W. Yau, P. Liao, J. C. Fredenburgh, A. R. Stafford, A. S. Revenko, B. P. Monia, J. I. Weitz, Selective depletion of factor XI or factor XII with antisense oligonucleotides attenuates catheter thrombosis in rabbits,Blood, 2014, 123, 13, 2102

Anna Falanga, Laura Russo, Viola Milesi, The coagulopathy of cancerCurrent Opinion in Hematology, 2014, 21, 5, 423

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Personalized Medicine – The California Initiative

Curator: Demet Sag, PhD, CRA, GCP

Are we there yet?  Life is a journey so the science.

Governor Brown announced Precision Medicine initiative for California on April 14, 2015.  UC San Francisco is hosting the two-year initiative, through UC Health, which includes UC’s five medical centers, with $3 million in startup funds from the state. The public-private initiative aims to leverage these funds with contributions from other academic and industry partners.

With so many campuses spread throughout the state and so much scientific, clinical and computational expertise, the UC system has the potential to bring it all together, said Atul Butte, MD, PhD, who is leading the initiative.

At the beginning of 2015 President Obama signed this initiative and assigned people to work on this project.

Previously NCI Director Harold Varmus, MD said that “Precision medicine is really about re-engineering the diagnostic categories for cancer to be consistent with its genomic underpinnings, so we can make better choices about therapy,” and “In that sense, many of the things we’re proposing to do are already under way.”

The proposed initiative has two main components:

  • a near-term focus on cancers and
  • a longer-term aim to generate knowledge applicable to the whole range of health and disease.

Both components are now within our reach because of advances in basic research, including molecular biology, genomics, and bioinformatics. Furthermore, the initiative taps into converging trends of increased connectivity, through social media and mobile devices, and Americans’ growing desire to be active partners in medical research.

Since the human genome is sequenced it became clear that actually there are few genes than expected and shared among organisms to accomplish same or similar core biological functions.  As a result, knowledge of the biological role of such shared proteins in one organism can be transferred to another organism.

It was necessary to generate a dynamic yet controlled standardized collection of information with ever changing and accumulating data. It was called Gene Ontology Consortium. Three independent ontologies can be reached at  (http://www.geneontology.org) developed based on :

  1. biological process,
  2. molecular function and
  3. cellular component.

We need a common language for annotation for a functional conservation. Genesis of the grand biological unification made it possible to complete the genomic sequences of not only human but also the main model organisms and more:

·         the budding yeast, Saccharomyces cerevisiae, completed in 1996

·         the nematode worm Caenorhabditis elegans, completed in 1998

·         the fruitfly Drosophila melanogaster,

·         the flowering plant Arabidopsis thaliana

·         fission yeast Schizosaccharomyces pombe

·         the mouse , Mus musculus

On the other hand, as we know there are allelic variations that underlie common diseases and complete genome sequencing for many individuals with and without disease is required.  However, there are advantages and disadvantages as we can carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies but there are problems such as computing the data efficiently and sharing the information without tempering privacy. Therefore we should be mindful about few main conditions including:

  1. models of the allelic architecture of commondiseases,
  2. sample size,
  3. map density and
  4. sample-collection biases.

This will lead into the cost control and efficiency while identifying genuine disease-susceptibility loci. The genome-wide association studies (GWAS) have progressed from assaying fewer than 100,000 SNPs to more than one million, and sample sizes have increased dramatically as the search for variants that explain more of the disease/trait heritability has intensified.

In addition, we must translate this sequence information from genomics locus of the genes to function with related polymorphism of these genes so that possible patterns of the gene expression and disease traits can be matched. Then, we may develop precision technologies for:

  1. Diagnostics
  2. Targeted Drugs and Treatments
  3. Biomarkers to modulate cells for correct functions

With the knowledge of:

  1. gene expression variations
  2. insight in the genetic contribution to clinical endpoints ofcomplex disease and
  3. their biological risk factors,
  4. share etiologic pathways

therefore, requires an understanding of both:

  • the structure and
  • the biology of the genome.

These studies demonstrated hundreds of associations of common genetic variants with over 80 diseases and traits collected under a controlled online resource.  However, identifying published GWAS can be challenging as a simple PubMed search using the words “genome wide association studies”  may be easily populated with un-relevant  GWAS.

National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (http://www.genome.gov/gwastudies), an online, regularly updated database of SNP-trait associations extracted from published GWAS was developed.

Therefore, sequencing of a human genome is a quite undertake and requires tools to make it possible:

  • to explore the genetic component incomplex diseases and
  • to fully understand the genetic pathways contributing tocomplex disease

The rapid increase in the number of GWAS provides an unprecedented opportunity to examine the potential impact of common genetic variants on complex diseases by systematically cataloging and summarizing key characteristics of the observed associations and the trait/disease associated SNPs (TASs) underlying them.

With this in mind, many forms can be established:

  1. to describe the features of this resource and the methods we have used to produce it,
  2. to provide and examine key descriptive characteristics of reported TASs such as estimated risk allele frequencies and odds ratios,
  3. to examine the underlying functionality of reported risk loci by mapping them to genomic annotation sets and assessing overrepresentation via Monte Carlo simulations and
  4. to investigate the relationship between recent human evolution and human disease phenotypes.

This procedure has no clear path so there are several obstacles in the actual functional variant that is often unknown. This may be due to:

  1. trait/disease associated SNPs (TASs),
  2. a well known SNP+ strong linkage disequilibrium (LD) with the TAS,
  3. an unknown common SNP tagged by a haplotype
  4. rare single nucleotide variant tagged by a haplotype on which the TAS occurs, or
  5. Copy Number variation (CNV), a linked copy number variant.

There can be other factors such as

  • Evolution,
  • Natural Selection
  • Environment
  • Pedigree
  • Epigenetics

Even though heritage is another big factor, the concept of heritability and its definition as an estimable, dimensionless population parameter as introduced by Sewall Wright and Ronald Fisher almost a century ago.

As a result, heritability gain interest since it allows us to compare of the relative importance of genes and environment to the variation of traits within and across populations. The heritability is an ongoing mechanism and  remains as a key:

  • to selection in evolutionary biology and agriculture, and
  • to the prediction of disease risk in medicine.

Table 1.

Reported TASs associated with two or more distinct traits

Chromosomal region Rs number(s) Attributed genes Associated traits reported in catalog
1p13.2 rs2476601, rs6679677 PTPN22 Crohn’s disease, type 1 diabetes, rheumatoid arthritis
1q23.2 rs2251746, rs2494250 FCER1A Serum IgE levels, select biomarker traits (MCP1)
2p15 rs1186868, rs1427407 BCL11A Fetal hemoglobin, F-cell distribution
2p23.3 rs780094 GCKR CRP, lipids, waist circumference
6p21.33 rs3131379, rs3117582 HLA / MHC region Systemic lupus erythematosus, lung cancer, psoriasis, inflammatory bowel disease, ulcerative colitis, celiac disease, rheumatoid arthritis, juvenile idiopathic arthritis, multiple sclerosis, type 1 diabetes
6p22.3 rs6908425, rs7756992, rs7754840, rs10946398, rs6931514 CDKAL1 Crohn’s disease, type 2 diabetes
6p25.3 rs1540771, rs12203592, rs872071 IRF4 Freckles, hair color, chronic lymphocytic leukemia
6q23.3 rs5029939, rs10499194 TNFAIP3 Systemic lupus erythematosus, rheumatoid arthritis
7p15.1 rs1635852, rs864745 JAZF1 Height, type 2 diabetes*
8q24.21 rs6983267 Intergenic Prostate or colorectal cancer, breast cancer
9p21.3 rs10811661, rs1333040, rs10811661, rs10757278, rs1333049 CDKN2A, CDKN2B Type 2 diabetes, intracranial aneurysm, myocardial infarction
9q34.2 rs505922, rs507666, rs657152 ABO Protein quantitative trait loci (TNF-α), soluble ICAM-1, plasma levels of liver enzymes (alkaline phosphatase)
12q24 rs1169313, rs7310409, rs1169310, rs2650000 HNF1A Plasma levels of liver enzyme (GGT), C-reactive protein, LDL cholesterol
16q12.2 rs8050136, rs9930506, rs6499640, rs9939609, rs1121980 FTO Type 2 diabetes, body mass index or weight
17q12 rs7216389, rs2872507 ORMDL3 Asthma, Crohn’s disease
17q12 rs4430796 TCF2 Prostate cancer, type 2 diabetes
18p11.21 rs2542151 PTPN2 Type 1 diabetes, Crohn’s disease
19q13.32 rs4420638 APOE, APOC1, APOC4 Alzheimer’s disease, lipids

* The well known association of JAZF1 with prostate cancer was reported with a p value of 2 × 10−6 (18), which did not meet the threshold of 5 × 10−8 for this analysis.

PMC full text: Proc Natl Acad Sci U S A. 2009 Jun 9; 106(23): 9362–9367.

Published online 2009 May 27. doi:  10.1073/pnas.0903103106


Table 2

Allele-Frequency Data for Nine Reproducible Associations

gene diseasea SNP associated alleleb Europeand Africane δf FST reference(s)c
CTLA4 T1DM Thr17Ala Ala .38 (1,670) .209 (402) .171 .06 Osei-Hyiaman et al. 2001; Lohmueller et al. 2003
DRD3 Schizophrenia Ser9Gly Ser/Ser .67 (202) .116 (112) .554 .458 Crocq et al. 1996; Lohmueller et al.2003
AGT Hypertension Thr235Met Thr .42 (3,034) .91 (658) .49 .358 Rotimi et al. 1996; Nakajima et al.2002
PRNP CJD Met129Val Met .72 (138) .556 (72) .164 .049 Hirschhorn et al. 2002; Soldevila et al. 2003
F5 DVT Arg506Gln Gln .044 (1,236) .00 (251) .044 .03 Rees et al. 1995; Hirschhorn et al.2002
HFE HFE Cys382Tyr Tyr .038 (2,900) .00 (806) .038 .024 Feder et al. 1996; Merryweather-Clarke et al. 1997
MTHFR DVT C677T T .3 (188) .066 (468) .234 .205 Schneider et al. 1998; Ray et al.2002
PPARG T2DM Pro12Ala Pro .925 (120) 1.0 (120) .075 .067 Altshuler et al. 2000HapMap Project
KCNJ11 T2DM Asp23Lys Lys .36 (96) .09 (98) .27 .182 Florez et al. 2004

aCJD = Creutzfeldt-Jacob disease; DVT = deep venous thrombosis; HFE = hemochromatosis; T1DM = type I diabetes; T2DM = type II diabetes.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that claims this to be a reproducible association, as well as the reference from which the allele frequencies were taken. For allele frequencies obtained from a meta-analysis, only the reference claiming reproducible association is given.

dAllele frequency obtained from the literature involving a European population. Either the general population frequency or the frequency in control groups in an association study was used. To reduce bias, when a control frequency was used for Europeans, a control frequency was also used for Africans. The total number of chromosomes surveyed is given in parentheses after each frequency.

eAllele frequency obtained from the literature involving a West African population. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between Europeans and Africans.

Table 3

PMC full text:

Am J Hum Genet. 2006 Jan; 78(1): 130–136.

Published online 2005 Nov 16. doi:  10.1086/499287

Copyright/License ►Request permission to reuse

Allele-Frequency Data for 39 Reported Associations

gene disease/phenotypea SNP associated alleleb Europeand Africane δf FST referencec
ADRB1 MI Arg389Gly Arg .717 (46) .467 (30) .251 .1 Iwai et al. 2003
ALOX5AP MI, stroke rs10507391 T .682 (44) .159 (44) .523 .425 Helgadottir et al. 2004
CAT Hypertension −844 (C/T) Tg .714 (42) .659 (44) .055 0 Jiang et al. 2001
CCR2 AIDS susceptibility Ile64Val Val .87 (46) .813 (48) .057 0 Smith et al. 1997
CD36 Malaria Y to stop Stop 0 (46) .083 (48) .083 .062 Aitman et al. 2000
F13 MI Val34Leu Val .762 (42) .795 (44) .033 0 Kohler et al. 1999
FGA Pulmonary embolism Thr312Ala Ala .2 (40) .5 (42) .3 .159 Carter et al. 2000
GP1BA CAD Thr145Met Met .022 (46) .167 (48) .145 .095 Gonzalez-Conejero et al.1998
ICAM1 MS Lys469Glu Lys .643 (42) .875 (48) .232 .12 Nejentsev et al. 2003
ICAM1 Malaria Lys29Met Met 0 (46) .354 (48) .354 .335 Fernandez-Reyes et al.1997
IFNGR1 Hp infection −56 (C/T) T .455 (44) .604 (48) .15 .023 Thye et al. 2003
IL13 Asthma −1055 (C/T) T .196 (46) .25 (44) .054 0 van der Pouw Kraan et al. 1999
IL13 Bronchial asthma Arg110Gln Gln .273 (44) .119 (42) .154 .05 Heinzmann et al. 2003
IL1A AD −889 (C/T) T .295 (44) .391 (46) .096 0 Nicoll et al. 2000
IL1B Gastric cancer −31 (C/T) T .826 (46) .375 (48) .451 .335 El-Omar et al. 2000
IL3 RA −16 (C/T) C .739 (46) .875 (48) .136 .037 Yamada et al. 2001
IL4 Asthma −590 (T/C) T .174 (46) .708 (48) .534 .436 Noguchi et al. 1998
IL4R Asthma Gln576Arg Arg .295 (44) .565 (46) .27 .118 Hershey et al. 1997
IL6 Juvenile arthritis −174 (C/G) G .5 (44) 1 (46) .5 .494 Fishman et al. 1998
IL8 RSV bronchiolitis −251 (T/A) Th .659 (44) .229 (48) .43 .301 Hull et al. 2000
ITGA2 MI 807 (C/T) T .316 (38) .25 (48) .066 0 Moshfegh et al. 1999
LTA MI Thr26Asn Asn .357 (42) .5 (44) .143 .018 Ozaki et al. 2002
MC1R Fair skin Val92Met Met .068 (44) 0 (44) .068 .047 Valverde et al. 1995
NOS3 MI Glu298Asp Asp .5 (44) .136 (44) .364 .247 Shimasaki et al. 1998
PLAU AD Pro141Leu Pro .659 (44) .979 (48) .32 .287 Finckh et al. 2003
PON1 CAD Arg192Gln Arg .174 (46) .727 (44) .553 .461 Serrato and Marian 1995
PON2 CAD Cys311Ser Ser .826 (46) .762 (42) .064 0 Sanghera et al. 1998
PTGS2 Colon cancer −765 (G/C) C .238 (42) .292 (48) .054 0 Koh et al. 2004
PTPN22i RA Arg620Trp Trp .084 (1,120) .024 (818) .059 .03 Begovich et al. 2004
SELE CAD Ser128Arg Arg .091 (44) .021 (48) .07 .025 Wenzel et al. 1994
SELL IgA nephropathy Pro238Ser Ser .065 (46) .333 (48) .268 .183 Takei et al. 2002
SELP MI Thr715Pro Thr .864 (44) .977 (44) .114 .063 Herrmann et al. 1998
SFTPB ARDS Ile131Thr Thr .5 (44) .348 (46) .152 .025 Lin et al. 2000
SPD RSV infection Met11Thr Met .568 (44) .478 (46) .09 0 Lahti et al. 2002
TF AD Pro570Ser Pro .957 (46) .935 (46) .022 0 Zhang et al. 2003
THBD MI Ala455Val Ala .87 (46) .848 (46) .022 0 Norlund et al. 1997
THBS4 MI Ala387Pro Pro .341 (44) .083 (48) .258 .166 Topol et al. 2001
TNFA Infectious disease −308 (A/G) A .182 (44) .205 (44) .023 0 Bayley et al. 2004
VCAM1 Stroke in SCD Gly413Ala Gly 1 (46) .938 (48) .063 .041 Taylor et al. 2002

aAD = Alzheimer disease; AIDS = acquired immunodeficiency syndrome; ARDS = acute respiratory distress syndrome; CAD = coronary artery disease; Hp = Helicobacter pylori; MI = myocardial infarction; MS = multiple sclerosis; RA = rheumatoid arthritis; RSV = respiratory syncytial virus; SCD = sickle cell disease.

bThe associated allele is the SNP associated with disease, regardless of whether it is the derived or the ancestral allele. The frequencies for this allele are given.

cThe reference that reported association with the listed disease/phenotype.

dFrequency obtained from the Seattle SNPs database for the European sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

eFrequency obtained from the Seattle SNPs database for the African American sample. The total number of chromosomes surveyed is given in parentheses after each frequency.

fδ = The difference in the allele frequency between African Americans and Europeans.

gAssociated allele in database is A.

hAssociated allele in reference is A.

iThis SNP was not from the Seattle SNPs database; instead, allele frequencies from Begovich et al. (2004) were used.

They reported that “The SNPs associated with common disease that we investigated do not show much higher levels of differentiation than those of random SNPs. Thus, in these cases, ethnicity is a poor predictor of an individual’s genotype, which is also the pattern for random variants in the genome. This lends support to the hypothesis that many population differences in disease risk are environmental, rather than genetic, in origin. However, some exceptional SNPs associated with common disease are highly differentiated in frequency across populations, because of either a history of random drift or natural selection. The exceptional SNPs  are located in AGT, DRD3, ALOX5AP, ICAM1, IL1B, IL4, IL6, IL8, and PON1. Of note, evidence of selection has been observed for AGT (Nakajima et al. 2004), IL4(Rockman et al. 2003), IL8 (Hull et al. 2001), and PON1 (Allebrandt et al. 2002). Yet, for the vast majority of the common-disease–associated polymorphisms we examined, ethnicity is likely to be a poor predictor of an individual’s genotype.”

In 2002The International HapMap Project was launched:

  • to provide a public resource
  • to accelerate medical genetic research.

Two Hapmap projects were completed. In phase I the objective was to genotype at least one common SNP every 5 kilobases (kb) across the euchromatic portion of the genome in 270 individuals from four geographically diverse population. In Phase II of the HapMap Project, a further 2.1 million SNPs were successfully genotyped on the same individuals.

The re-mapping of SNPs from Phase I of the project identified 21,177 SNPs that had an ambiguous position or some other feature indicative of low reliability; these are not included in the filtered Phase II data release. All genotype data are available from the HapMap Data Coordination Center (http://www.hapmap.org) and dbSNP (http://www.ncbi.nlm.nih.gov/SNP).

In the Phase II HapMap we identified 32,996 recombination hotspots3,6,36 (an increase of over 50% from Phase I) of which 68% localized to a region of≤5 kb. The median map distance induced by a hotspot is 0.043 cM (or one crossover per 2,300 meioses) and the hottest identified, on chromosome 20, is 1.2 cM (one crossover per 80 meioses). Hotspots account for approximately 60% of recombination in the human genome and about 6% of sequence (Supplementary Fig. 6).

In addition to many previously identified regions in HapMap Phase I including LARGESYT1 andSULT1C2 (previously called SULT1C1), about  200 regions identified from the Phase II HapMap that include many established cases of selection, such as the genes HBB andLCT, the HLA region, and an inversion on chromosome 17. Finally, in the future, whole-genome sequencing will provide a natural convergence of technologies to type both SNP and structural variation. Nevertheless, until that point, and even after, the HapMap Project data will provide an invaluable resource for understanding the structure of human genetic variation and its link to phenotype.



HMM libraries, such as PANTHER, Pfam, and SMART, are used primarily to recognize and annotate conserved motifs in protein sequences.

In the genomic era, one of the fundamental goals is to characterize the function of proteins on a large scale.

PANTHER, for relating protein sequence relationships to function relationships in a robust and accurate way under two main parts:

  • the PANTHER library (PANTHER/LIB)- collection of “books,” each representing a protein family as a multiple sequence alignment, a Hidden Markov Model (HMM), and a family tree.
  • the PANTHER index (PANTHER/X)- ontology for summarizing and navigating molecular functions and biological processes associated with the families and subfamilies.

PANTHER can be applied on three areas of active research:

  • to report the size and sequence diversity of the families and subfamilies, characterizing the relationship between sequence divergence and functional divergence across a wide range of protein families.
  • use the PANTHER/X ontology to give a high-level representation of gene function across the human and mouse genomes.
  • to rank missense single nucleotide polymorphisms (SNPs), on a database-wide scale, according to their likelihood of affecting protein function.

PRINTS is a compendium of protein motif ‘fingerprints’. A fingerprint is defined as a group of motifs excised from conserved regions of a sequence alignment, whose diagnostic power or potency is refined by iterative databasescanning (in this case the OWL composite sequence database).

The information contained within PRINTS is distinct from, but complementary to the consensus expressions stored in the widely-used PROSITE dictionary of patterns.

However, the position-specific amino acid probabilities in an HMM can also be used to annotate individual positions in a protein as being conserved (or conserving a property such as hydrophobicity) and therefore likely to be required for molecular function. For example, a mutation (or variant) at a conserved position is more likely to impact the function of that protein.

In addition, HMMs from different subfamilies of the same family can be compared with each other, to provide hypotheses about which residues may mediate the differences in function or specificity between the subfamilies.

Several computational algorithms and databases for comparing protein sequences developed and matured:

  1. particularly Hidden Markov Models (HMM;Krogh et al. 1994Eddy 1996) and
  2. PSI-BLAST (Altschul et al. 1997),

The profile has a different amino acid substitution vector at each position in the profile, based on the pattern of amino acids observed in a multiple alignment of related sequences.

Profile methods combine algorithms with databases: A group of related sequences is used to build a statistical representation of corresponding positions in the related proteins. The power of these methods therefore increases as new sequences are added to the database of known proteins.

Multiple sequence alignments (Dayhoff et al. 1974) and profiles have allowed a systematic study of related sequences. One of the key observations is that some positions are “conserved,” that is, the amino acid is invariant or restricted to a particular property (such as hydrophobicity), across an entire group of related sequences.

The dependence of profile and pattern-matching approaches (Jongeneel et al. 1989) on sequence databases led to the development of databases of profiles

  1. BLOCKS,Henikoff and Henikoff 1991;
  2. PRINTS,Attwood et al. 1994) and
  3. patterns (Prosite,Bairoch 1991) that could be searched in much the same way as sequence databases.

Among the most widely used protein family databases are

  1. Pfam (Sonnhammer et al. 1997;Bateman et al. 2002) and
  2. SMART (Schultz et al. 1998;Letunic et al. 2002), which combine expert analysis with the well-developed HMM formalism for statistical modeling of protein families (mostly families of related protein domains).

Either knowing its family membership to predict its function, or subfamily within that family is enough (Hannenhalli and Russell 2000).

  • Phylogenetic trees (representing the evolutionary relationships between sequences) and
  • dendrograms (tree structures representing the similarity between sequences) (e.g.,Chiu et al. 1985Rollins et al. 1991).

The PANTHER/LIB HMMs can be viewed as a statistical method for scoring the “functional likelihood” of different amino acid substitutions on a wide variety of proteins. Because it uses evolutionarily related sequences to estimate the probability of a given amino acid at a particular position in a protein, the method can be referred to as generating position-specific evolutionary conservation” (PSEC) scores.

Schematic illustration of the process for building PANTHER families.

  1. Family clustering.
  2. Multiple sequence alignment (MSA), family HMM, and family tree building.
  3. Family/subfamily definition and naming.
  4. Subfamily HMM building.
  5. Molecular function and biological process association.

Of these, steps 1, 2, and 4 are computational, and steps 3 and 5 are human-curated (with the extensive aid of software tools).



Further Reading

Human Phenome Project: Freimer N., Sabatti C. The human phenome project. Nat. Genet. 2003;34:15–21.

Jones R., Pembrey M., Golding J., Herrick D. The search for genenotype/phenotype associations and the phenome scan. Paediatr. Perinat. Epidemiol. 2005;19:264–275.

Stearns F.W. One hundred years of pleiotropy: A retrospective. Genetics.2010;186:767–773.

Welch J.J., Waxman D. Modularity and the cost of complexity. Evolution.2003;57:1723–1734.

Albert A.Y., Sawaya S., Vines T.H., Knecht A.K., Miller C.T., Summers B.R., Balabhadra S., Kingsley D.M., Schluter D. The genetics of adaptive shape shift in stickleback: Pleiotropy and effect size. Evolution. 2008;62:76–85.

Brem R.B., Yvert G., Clinton R., Kruglyak L. Genetic dissection of transcriptional regulation in budding yeast. Science. 2002;296:752–755.

Morley M., Molony C.M., Weber T.M., Devlin J.L., Ewens K.G., Spielman R.S., Cheung V.G. Genetic analysis of genome-wide variation in human gene expression. Nature. 2004;430:743–747. [PMC free article] [PubMed]

Wagner G.P., Zhang J. The pleiotropic structure of the genotype-phenotype map: The evolvability of complex organisms. Nat. Rev. Genet. 2011;12:204–213.

Cooper Z.N., Nelson R.M., Ross L.F. Informed consent for genetic research involving pleiotropic genes: An empirical study of ApoE research. IRB. 2006;28:1–11.


Model Organisms:

Worm Sequencing Consortium. The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science.1998;282:2012–2018.

Adams MD, et al. The genome sequence of Drosophila melanogasterScience.2000;287:2185–2195.

Meinke DW, et al. Arabidopsis thaliana: a model plant for genome analysis. Science. 1998;282:662–682. [PubMed]

Chervitz SA, et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 1999;27:74–78.

The FlyBase Consortium The FlyBase database of the Drosophila Genome Projects and community literature. Nucleic Acids Res. 1999;27:85–88.

Blake JA, et al. The Mouse Genome Database (MGD): expanding genetic and genomic resources for the laboratory mouse. Nucleic Acids Res. 2000;28:108–111.

Ball CA, et al. Integrating functional genomic information into the Saccharomyces Genome Database. Nucleic Acids Res. 2000;28:77–80.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G., Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., et al. 2001. The sequence of the human genome. Science 291: 1304–1351.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K., Dewar, K., Doyle, M., FitzHugh, W., et al. 2001. Initial sequencing and analysis of the human genome. Nature 409: 860–921.

Mi, H., Vandergriff, J., Campbell, M., Narechania, A., Lewis, S., Thomas, P.D., and Ashburner, M. 2003. Assessment of genome-wide protein function classification for Drosophila melanogaster. Genome Res.

Ashburner, M., Ball, C.A., Blake, J.A., Botstein, D., Butler, H., Cherry, J.M., Davis, A.P., Dolinski, K., Dwight, S.S., Eppig, J.T., et al. The Gene Ontology Consortium. 2000. Gene ontology: Tool for the unification of biology. Nat. Genet. 25: 25–29.


Computational Biology

Attwood TK, Beck ME, Bleasby AJ, Parry-Smith DJ. PRINTS–a database of protein motif fingerprints. Nucleic Acids Res. 1994 Sep;22(17):3590-6.

Obenauer JC, Yaffe MB. Computational prediction of protein-protein interactions.

Methods Mol Biol. 2004;261:445-68. Review.

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Hodgman TC. The elucidation of protein function by sequence motif analysis.  Comput Appl Biosci. 1989 Feb;5(1):1-13. Review.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., and Lipman, D.J. 1997. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res. 25: 3389–3402.

Spencer CC, et al. The influence of recombination on human genetic diversity.PLoS Genet. 2006;2:e148.

Petes TD. Meiotic recombination hot spots and cold spots. Nature Rev. Genet.2001;2:360–369.

Griffiths RC, Tavaré S. The age of a mutation in a general coalescent tree. Stoch Models. 1998;14:273–295. doi: 10.1080/15326349808807471.

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Attwood, T.K., Beck, M.E., Bleasby, A.J., and Parry-Smith, D.J. 1994. PRINTS—A database of protein motif fingerprints. Nucleic Acids Res. 22: 3590–3596.

Bairoch, A. 1991. PROSITE: A dictionary of sites and patterns in proteins. Nucleic Acids Res. 19 Suppl: 2241–2245.

Bairoch, A. and Apweiler, R. 2000. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 28: 45–48.

Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., Griffiths-Jones, S., Howe, K.L., Marshall, M., and Sonnhammer, E.L. 2002. The Pfam protein families database. Nucleic Acids Res. 30: 276–280.

Sonnhammer, E.L., Eddy, S.R., and Durbin, R. 1997. Pfam: A comprehensive database of protein domain families based on seed alignments. Proteins 28:405–420.

Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285–1293. [PubMed]

Thomas, P.D., Kejariwal, A., Campbell, M.J., Mi, H., Diemer, K., Guo, N., Ladunga, I., Ulitsky-Lazareva, B., Muruganujan, A., Rabkin, S., et al. 2003. PANTHER: A browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucleic Acids Res. 31: 334–341.

HUGO Gene Nomenclature Committee (2011). HGNC Database.http://www.genenames.org/.


Population Genomics, GWAS, Inheritance, Heritability, Migration, Selection  an Evolution:

Dayhoff, M.O., Barker, W.C., and McLaughlin, P.J. 1974. Inferences from protein and nucleic acid sequences: Early molecular evolution, divergence of kingdoms and rates of change. Orig. Life 5: 311–330.

Joseph Lachance Disease-associated alleles in genome-wide association studies are enriched for derived low frequency alleles relative to HapMap and neutral expectations BMC Med Genomics. 2010; 3: 57.

Joseph Lachance, Sarah A. Tishkoff  Biased Gene Conversion Skews Allele Frequencies in Human Populations, Increasing the Disease Burden of Recessive Alleles  Am J Hum Genet. 2014 October 2; 95(4): 408-420.

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Joseph Lachance, Sarah A. Tishkoff  Population Genomics of Human Adaptation

Annu Rev Ecol Evol Syst. Author manuscript; available in PMC 2014 November 5.

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Joseph Lachance, Sarah A. Tishkoff SNP ascertainment bias in population genetic analyses: Why it is important, and how to correct it  Bioessays.

Erik Corona, Rong Chen, Martin Sikora, Alexander A. Morgan, Chirag J. Patel, Aditya Ramesh, Carlos D. Bustamante, Atul J. Butte Analysis of the Genetic Basis of Disease in the Context of Worldwide Human Relationships and Migration PLoS Genet. 2013 May; 9(5): e1003447.

Olga Y. Gorlova, Jun Ying, Christopher I. Amos, Margaret R. Spitz, Bo Peng, Ivan P. Gorlov J Derived SNP Alleles Are Used More Frequently Than Ancestral Alleles As Risk-Associated Variants In Common Human Diseases Bioinform Comput Biol.

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1:15PM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com

1:15 p.m. – Keynote Speaker – International Genetics Health and Disease

International Genetics Health and Disease

The principles of personalized medicine and how they affect the lives of people acknowledge no national boundaries. Although there are some differences among the diverse populations around the world in terms of their genetic variation, the general principles of personalized medicine apply uniformly across many populations. Dr. Periz will discuss how personalized medicine is viewed across the many European countries with particular emphasis on how Spain is implementing it into its medical care.

Keynote Speaker

Antonio L. Andreu Periz, M.D.
Director, Instituto de Salud Carlos III, Madrid

@insalud_es  @CIBER-BBN

Governmental & Public Health National Organization like a combination of CDC and “Hybrid NIH in the US”

Personalized Medicine (PM) in Europe

Europe and Spain — PM is changing Medical Practice, regulations standard of care.


In Europe 28 National systems in Spain alone 17 systems

Implementation of PM in Europe: Hospitals, Regulation,

  • develop proof of concept
  • identify mechanisms
  • bring basic research to clinical
  • incorporation into a Portfolio of policies on PM

Horizon 2020 in EU – 2016 launch action on PM in various countries in EU

  • Translational level for all EC members
  • Coalition of 28 Research Centers in Europe to promote PM
  • Sharing Databases, Data on HC, infrastructure for Translational research
  • Biomarkers
  • clinical trials

CSA – Coordination Support Action

  • PerMed 500,000 Euro for 5 years, 9 operating partners, representatives of Ministry of Health, Israel and Canada Ministry of Health are included
  • Research Agenda for PM in Europe – SWOT Analysis
  • Recommendations for UC to start PM in 2016
  • – basic research
  • – translation
  • – ICTs
  • – Regulatory

SPAIN – Initiatives on PM: Aggregation of Knowledge

  • One single organization collaborates with 22 Institutions on Biomedical research – Concentration in Barcelona and in Madrid
  • Projects of Excellence: PhD level Projects – Clinical Practice: Imaging, Endocrinology, genomics, cardiology
  • 2014 — 35 Applicants – not all are on Cancer 25% are in Cancer 75% are in other clinical Fields
  • 12Million Euros will fund 1/4 of the applicants
  • PhD Thesis on PM – common project 2 yr governmental institute and 2 years in biotech industry

EAPM – Europe Alliance for PM

  • raise awareness on HOW PM CAN SHAPE Healthcare in Europe: Diagnosis, Treatment,
  • Specialized Treatment for Europe’s Patient (STEPs) – Five Steps

Global alliances to shape Medical Practice based on PM – Collaboration Industry and Academia

  • PMC in the US (Personalized Medical Coalition)
  • PerMEd in Europe (coalition  in Europe  supporting innovation in personalized medicine)
  • EAPM (European Alliance for Personalized Medicine)



– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf







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8:20AM 11/12/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

REAL TIME Coverage of the Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com


8:20 a.m. Special Guest Keynote Speaker – The Future of Personalized Medicine

The Future of Personalized Medicine

Special Guest Speaker

Margaret Hamburg, M.D.
Commissioner of Food and Drugs Administration

[Her Father was President of IOM said at the introduction to the Keynote]

How to ask the right question is what HMS taught me best 

Increasing the knowledge of Biology, response to disease, preventive strategies.

2004 — Monumental year — One year after completion of sequencing the Genome

2008/9 – Breast Cancer – pharmacotherapy approved, a protein involved in triggering the disease.Target therapy – risk of disease identified

WHAT FDA is doing on Genetics Information as PARTNERS in Medicine

25% of drugs approved are Targeted therapies

LABELING drugs on genetic information

diagnostics test — identify good respondents

Companion Diagnostics – should be used in Targeted therapies. IGF1, HER2 expression and amplification

PM more important in ONCOLOGY , HepB, Cystic Fibrosis, differential response, CVD – expansion, more to be done

In 2002 — a Program to discuss Genetic information VSDS – New Genomics Program, National Center for Toxicology Research a participants

Translational Scientist are added.

Completion Genome sequencing — push to PM 2011 – Genomics evaluation Team for Safety.

Challenge – Drug, Biologics – interaction need coordination by Agency to discuss challenges and collaboration with out side Group.

Developers of Targeted therapies: Orphan Drugs, Biomarkers – expedited review to promote innovations, fast track breakthrough therapies. Opportunities of Scientist to engage discussion with FDA

 – ALL hands on Deck Approach at FDA – making products available, i.e. SCLC (small cell lung cancer)

Since 2005 – 25 Guidance Reports, i.e., Orphan Drugs and on Companion Diagnostics to be developed in tandem with drug development.

Companion Diagnostics – 3 month review, enforcement and direction – in the framework

FDA — needs to keep up with development in the Diagnostics and in the disease ares.

Illumina – Assays using SNIPS – FDA assesses a shared curated DB on mutation, reduce the review time significantly

FDA – NGS – reference libraries, Genomics Reference and Storage of genomics data

Tools and Capabilities  – support regulatory and science, statistical methods of analysis — implemented for Breast Cancer — signaled the way of new Partnerships and New Clinical Trials formats and methods in its development.

New diagnostics – AMP Program Alzheimer’s Disease, rheumatoid arthritis (RA), inflammatory bowel syndrome (IBS)

What Science is needed for the Regulators to effectively HELP spar innovation.

Pharmacogenomics, Pharmacogenetics — MAPPING the Human Genome and all other areas of “OMICS” – moving from Lab to bedside — requires expertise in Disease prevention, Difference in patients life, Standard medical practice

  • Biology and Pathways
  • Biomarkers
  • New diagnostics
  • Increased communication Universities, new paradigms models and continual effort of SHARING and coordination of shared resources


– See more at: http://personalizedmedicine.partners.org/Education/Personalized-Medicine-Conference/Program.aspx#sthash.qGbGZXXf.dpuf





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Pharmacogenomics needs new materials that are inert against the host and specifically  active to modulate molecular metabolism towards wanted homeostasis of the physiological system.  These can come from natural resources or men-made.  That is why we must know the origin  to  improve.     Recently, Synthetic Biology, even though it is a developing upcoming field, it is generating mile stones for applications in the clinic, the biotechnology industry and in basic molecular research. As  a result, it created a multidisciplinary expertise from scientists to engineers.  Among other things extending the search to first life on Earth is one of the many alternatives.  Here I like to present how synthetic biology can be initiated onto Translational Medicine from adiscovery of molecules from the sea.

Microorganisms played a role in evolution to start a life.  99 % of our genome is related to microbial organisms. initially there was a classical  Microbiology, then evolved to Industrial Microbiology and Biotechnology then Microbial Genomics and now Microbiome and Health became the focus.  Finally,  the circle is getting tide into how microbiome involved with healthy and disease state of human? How they can be used that is what it really means to include microorganisms into human health for diagnostics and targeted therapies?

Or should we start from  scarcity?

Microbiology is my first formal education and  building block.  Simple but help to understand system biology and  the mechanism of life in a nut shell.   The closest field is embryonic stem cell biology for building “synthesizing” a whole new organism.  Then  system biology and developmental biology also gain interest.

The real  remember the month of October in 2001 when DOE reported that they sequenced 23 organisms in Walnut Creek.  Having seen presentation to identify microorganisms through complex crystal structure assays through chemical pathway  at the Microbial Genomics Meeting organized by ASM in Monterey, CA in 2001.

Discovery of microorganisms in marine life like in Mediterranean Sea, containing 38% salt,is very similar with finding circulating disease making cells.   Yet, they are similar since both search for a specific needle in the pile.  Furthermore, the unique behavior of enzymes from microbial organisms such as Taq polymerase or restriction enzymes made it possible for us to develop new technologies for copying and propagating significant sequences.  When these early molecular biology methods are combined with the power of genomics and knowledge of unique structures in molecular physiology, it is possible to design better and sensitive sensors or build an organism to rptect or fix the need of the body.  neither sensors nor synthesized organism model are complete since one is missing the basic element of life “transformation of information” the other is missing the integrity that once nature provided in a single simple cell.

Having sensory smart chip/band/nanomolecule to redesign the cells may also possible if only we know the combination.  Thus, we have options to deliver if we know what to be carried.

An external file that holds a picture, illustration, etc.<br /><br /><br /><br /><br />
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(Figure: The combined strategy of gene-based screening and bioactivity-based screening for marine microbial natural products (MMNPs) discovery, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3705366/figure/marinedrugs-11-00700-f002/)

As we come across, novel pathways or primary pathways of physiology gain significant interest to determine marine microbial compound for therapeutics since they are further away from the evolution three that gives an advantage for biomedical/translational scientist to avoid most part of th eimmune responses such as inflammation, toxicity. Yes, indeed these are not scientific tails but true since currently, 16 of 20 marine antitumor compounds under clinical trial are derived from microbial sources because marine microorganisms are a major source for MMNP discovery.  However, isolation of these organisms.  For example, pretreatment methods, enrichment, physical, and chemical techniques (e.g., dry heat, exposure to 1%–1.5% phenol, sucrose-gradient centrifugation, and filtration through cellulose membrane filters) can be applied to increase especially the less abundant specific groups of marine microorganisms, . A variety of pretreatment methods including recovery of these microorganisms.  This reminds me ecosystem of the soil, since in soil the trouble is identifying the specific culture among millions of others.

Regardless of the case,  nutrients are the key for selecting and isolating any organisms but specifically, as a result any marine microbes have specific nutrient requirements for growth (e.g., sponge extract ) or chemical (e.g., siderophores, signal molecules, non-traditional electron donors, and electron acceptors.  This also should remind us subject of Biology 101 Essential Vitamins and Minerals.  What we eat who we are.

For example, Bruns et al. employed technique where they employed different carbon substrates (agarose, starch, laminarin, xylan, chitin, and glucose) at low concentrations (200 μM each) so that they can  improve the cultivation efficiency of bacteria from the Gotland Deep in the central Baltic Sea. As a result of this growth medium they were able to elevate yield, which is created higher cultivation efficiencies (up to 11% in fluid media) compared to other studies.

Yet, another component must be addressed that is culture medium such as ionic strength for a microbila growth. For example, Tsueng et al. study on marine actinomycete genus Salinispora that can produce bioactive secondary metabolites such as desferrioxamine, saliniketals, arenamides, arenimycin and salinosporamide.  However, they observed that  three species of SalinisporaS. arenicolaS. tropica, and S. pacifica require a high ionic strength but  S. arenicolahas a lower growth requirement for ionic strength than S. tropica and S. pacificaUsing after assaying them against sodium chloride-based and lithium chloride-based media. As  aresult, there is a specificity for growth. 

In addition, energy must be supported imagine that in marine organisms the metabolism is very unique, may be slow and possibly.  However, the main criteria is  most of them grow under low oxygen conditions like tumors.  Warburg effect posed a  problem for human but helped microorganisms to survive and evolve.  One’s weakness the other’s strength make a great teamwork for solving diseases of human kind es especially for cancer. 

This reminds us to utilize minerals, electrons specifically after all the simplest form of carbon metabolism based on biochemical pathways like Crebs cycle, one carbon metabolism and amino acid metabolism etc. Even though 90% of human body made up off microbial origin there are microorganisms that are not cultured yet.

The irony is less than 1% of microorganisms can be cultured.  Furthermore, they are not included for representing the total phylogenetic diversity. Therefore, majority of work concentrated on finding and cultivating the uncultured majority of the microbial world for MMNPs’.  For example,  an uncultivated bacterial symbiont of the marine sponge Theonella swinhoei  producing many antitumor compounds such as pederin, mycalamide A, and onnamide A.

In any conditions if any living needs to be recognized and remembered, their place would be either on top or the bottom of the stack. Microbiome searches for specificity among tone of other organisms to recognize the disease, changes in cell differentiation and pathways or marine microbiologist search for uncommon scarce organisms. Yet, both of them are beneficial with their unique way.

Then what is the catch or fuss?  The catch is screening to identify what makes this organism unique that can be use for human health. Translational medicine may start from the beginning of life from microorganisms created.  This can be called with its newly coined named”synthetic biology” but if we go further than the conventional screening methods which include bioactivity-guided screening and gene-guided screening  and increase the power with genomics we may call it “synthetic genomics”.

As  a result these signature sequences establishes the “unique” biomarkers  or therpaeutics to be used for drug discovery, making vaccines, and remodulating the targeted cells. How?

These microorganisms secrete these metabolites or proteins to their growth medium just like a soluble protein, if you will like a inflammation factor or any other secreted protein of our human body cells. Collecting substrate or extract the pellet could be the choice.   in a nut shell this require at least three steps: First, finding the bioactivity, apply bioactivity-guided screening for direct detection of  the activity such as antimicrobial, antitumor, antiviral, and antiparasitic activities.  Second, a bioinformatic assessment of the secondary metabolite biosynthetic potential in the absence of fully assembled pathways or genome sequences. Third, application on cell lines and possible onto model organisms can improve the process of MMNP discovery so that allow us to prioritize strains for fermentation studies and chemical analysis. 

In summary, establish the culture growth, analyze bioactivity and discover the new gene product to be used.  Here is an example, first they  isolated Marinispora sp from the saline culture.  Next step,  identify new sources of bioactive secondary metabolites, gene-guided screening has been deployed to search target genes associated with NPs biosynthetic pathways, e.g., the fragments between ketosynthase and methylmalonyl-CoA transferase of polyketides (PKS) type I, enediyne PKS ketosynthase gene, O-methyltransferase gene, P450 monooxygenase gene, polyether epoxidase gene, 3-hydroxyl-3-methylglutaryl coenzyme A reductase gene, dTDP-glucose-4,6-dehydratase (dTGD) gene, and halogenase gene. The, apply bioinformatics that includes synthesizing the knowledge with  homology-based searches and phylogenetic analyses, gene-based screening  to predict new secondary metabolites discovered by isolates or environments.  Finally, identify the sequnce for PCR and use against a cell line or model organisms. In this case,  CNQ-140 based on significant antibacterial activities  against drug-resistant pathogens (e.g., MRSA) and impressive and selective cancer cell cytotoxicities (0.2–2.7 μM of MIC50 values) against six melanoma cell lines provided significant outcome. They are recognized as antitumor antibiotics with a new structural class, marinomycins A–D

This is a great method but there are two botle necks: 1. 99% of microbial organisms are not cultured in the labs. 2. Finding the optimum microbial growth and screening takes time. Thus, assesments can me done through metagenomics.  However, metagenomics has its shortcomings since on face of living unless applications applied in vivo in vitro results may not be valid.  The disadvantage of  metagenomics can be listed as:  1. inability of efficient acquisition of intact gene fragment,  2. incompatibility of expression elements such as promoter in a heterologous host.  On the pther hand, there can be possible resolution to avoid these factors  so metagenomics-based MMNP discovery can be plausable such as development  in  synthetic biology by large DNA fragment assembly techniques for artificial genome synthesis and synthetic microbial chassis suitable for different classes of MMNP biosynthesis.

However, many gene clusters have been identified by combined power of genomics and biioinformatics for MNP discovery.  This is  mainly necessary since  secondary metabolites usually biosynthesized by large multifunctional synthases that acts in a sequential assembly lines like adding carboxylic acid and amino acid building blocks into their products.  


Simmons TL, Coates RC, Clark BR, Engene N, Gonzalez D, Esquenazi E, Dorrestein PC, Gerwick W

Proc Natl Acad Sci U S A. 2008 Mar 25; 105(12):4587-94.

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