Home Genetic Testing

The Brazen New World of Home Genetic Testing

If you’re curious about whether your genes put you at elevated risk for cancer, hypertension, Alzheimer’s, heart disease, diabetes, or a wide range of rarer “inheritable diseases,” you may be tempted by ads for the many at-home (also called direct-to-consumer) genetic tests. Costing several hundred to a thousand dollars or so, they’re now sold on the Internet, and there are plans to sell them in drugstores. Some tests claim to provide personal nutrition advice based on your genetic profile. Usually all you have to do is rub a swab inside your cheek and mail it to the company, which will scan your DNA, looking for mutations and variations that suggest increased risk, and then send you the results.

Sounds clear-cut, but genetic testing—especially when done at home—raises many practical and ethical questions and problems.

Too much information? Or too little?

Home genetic tests fall into a regulatory gray area. They have not been reviewed by the FDA or any other agency, unlike most physician-ordered genetic tests or other kinds of at-home medical tests. There’s little or no evidence that the great majority of the tests are accurate, reliable, or “clinically meaningful” (in other words, useful or practical). Last summer the FDA told some companies that the tests are medical devices—that is, they are intended to diagnose, prevent, or treat diseases—and thus the companies should submit data and get FDA clearance in order to continue marketing them. The companies, meanwhile, say that they’re just supplying information rather than diagnosing or treating diseases, and that people have a right to know what’s going on in their DNA. So far, the FDA has not barred the sale of the tests.

One problem is the overblown claims made in much of the marketing material. In most cases, there’s no research showing that the results of the tests can help people prevent disease or lead to better treatment or longer lives. Let’s say a healthy young man takes a genetic test at home and finds out he’s at high risk for eventually developing prostate cancer. What should he do? Get frequent PSA tests, a biopsy, ultrasound? It’s not certain that any step would add even a day to his life.

And that’s assuming the test results are accurate to begin with—which is questionable. Last year the Government Accountability Office (GAO) published results from its investigation of home genetic tests, showing that companies produced inconsistent or even contradictory results from the same DNA sample. The GAO also cited many examples of deceptive marketing, bogus claims, and erroneous advice, including companies using the results to sell “customized” supplements that supposedly help cure disease or repair DNA damage.

Lots of unknowns

Before doing any genetic testing, discuss the risks and benefits thoroughly with your doctor or a genetic counselor. You may well decide not to be tested. Who will know the results of the test? What will your options be? Will your health insurance be affected? How accurate is the test?

Moreover, the results of genetic testing are often ambiguous. Relatively few diseases are controlled by a single abnormal dominant gene (such as Huntington’s disease) or a pair of abnormal recessive genes (such as Tay-Sachs disease or sickle cell anemia). Most are multifactorial—influenced by several genes, the passage of time, and the social and physical environment. That is, a gene or genes that may put you at risk for, say, type 2 diabetes might not come into play if you maintain a healthy weight, eat sensibly, and exercise as you age.

With those more complex diseases, testing may saddle you with unanswerable questions. If an abnormal gene or mutation turns up, you may feel doomed and see yourself as a sick person—even though it doesn’t guarantee you’ll get the disease, or tell you when you’ll get it (at 50? or 90?). On the other hand, a good test result is no guarantee you won’t get the disease, especially since most tests look only at a small portion of the more than 20,000 genes in the body.

Bottom line: The science of genetic risk prediction holds great promise, but is still in its infancy. For now, be wary about genetic testing, especially do-it-at-home tests—unless you have a special family history, have consulted with a qualified professional, and have thought long and hard about the pros and cons.


When testing may be warranted

In some circumstances, it’s reasonable to consider genetic testing, when it’s done in conjunction with a health-care professional knowledgeable in genetic medicine and by a specialized lab. For instance:

• Breast cancer: About 2% of women have a family history strong enough to warrant testing, according to the U.S. Preventive Services Task Force. If the results are positive, you should have frequent mammograms and other testing (such as MRI), and may even decide to have prophylactic surgery.

• Colon cancer: If you have a strong family history of this cancer, you need frequent colonoscopies to find and remove polyps. Genetic testing can be helpful in determining if you need even more frequent colonoscopies.

• Dementia: Three rare gene mutations are known to almost always produce early onset of Alzheimer’s disease, but account for less than 1% of all cases. Another gene is linked to increased risk for the more common late-onset Alzheimer’s, though most people who have this gene do not develop the disease. Testing remains controversial, since if you have one of the genes, what can you do? So far there is no way to prevent Alzheimer’s.

• Pregnancy: If you are planning a pregnancy and certain disorders run in your family and/or you belong to an ethnic or racial group that tends to have certain disorders (such as Ashkenazi Jews, who are at risk for Tay-Sachs disease, and African Americans, at risk for sickle cell anemia), you should discuss genetic testing with your doctor.

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Behind The Headlines: Cannabis use 'genetically linked' to schizophrenia

What is Behind the Headlines?
We give you the facts without the fiction. Professor Sir Muir Gray, founder of Behind the Headlines, explains more... 


Cannabis use 'genetically linked' to schizophrenia

Study finds people predisposed to [schizophrenia] and drug users share common genes,” the Mail Online reports. A new study suggests that ‘schizophrenia’ genes are associated with cannabis use.

It has long been known that there is an association between cannabis use and schizophrenia – but the “direction of travel” has been hotly debated.

Does cannabis use trigger the onset of schizophrenia in vulnerable individuals? Or are people with a genetic predisposition to develop schizophrenia more likely to use cannabis than the population at large (possibly as a coping mechanism)?

This latest study suggests that the latter may be the case; at least in some people. The study involved 2,082 healthy adults whose genetic make-up was examined for risk factors for schizophrenia.

People with more genetic risk factors (carrying more of the DNA variants that have been associated with schizophrenia) were more likely to have reported ever using cannabis.

However, it is important to note that none of the people in the study actually had a diagnosis of schizophrenia. In addition, as this is a cross-sectional study (see below), it cannot definitively answer the question of cause and effect.

A person’s risk for schizophrenia, or for cannabis use, are likely to be influenced by a complex mixture of genetic factors (including those not identified or examined here), lifestyle and environmental factors.

Where did the story come from?

The study was carried out by researchers from the Institute of Psychiatry, King’s College London; Queensland Brain Institute and QIMR Berghofer Medical Research Institute, Australia; the Department of Developmental Psychology and EMGO Institute for Health and Care Research, Amsterdam; the Washington University School of Medicine.

It was funded by the UK Medical Research Council and National Institute for Health Research; the Australian National Health, Medical Research Council and Australian Research Council; the Centre for Research Excellence on Suicide Prevention (CRESP – Australia); and the Netherlands Organization for Health Research and Development.

The study was published in the peer-reviewed medical journal Molecular Psychiatry.

The Mail Online reported the story accurately and informatively.

What kind of research was this?

This was a cross-sectional study using data collected in a larger cohort study. It aimed to assess the association between cannabis use and the level of genetic predisposition for schizophrenia.

As it is a cross-sectional study it is only able to describe this association and cannot prove cause and effect. That is whether the genetic predisposition caused them to use cannabis or that conversely, cannabis would cause them to develop schizophrenia.

What did the research involve?

A group of 2,082 unrelated healthy adults were recruited from the large Australian Twin Registry studies.

The participants were asked questions over the telephone on their cannabis (marijuana) use, including:
Did you ever use marijuana?
How old were you the very first time you tried marijuana (not counting the times you took it as prescribed)?
How many times in your life have you used marijuana (do not count times when you used a drug prescribed for you and took the prescribed dose)?

The genotype (each person’s genetic make-up) was obtained. These were compared with samples from a large Swedish study which has identified a number of single nucleotide polymorphisms (SNPs), DNA sequence variations, that are believed to increase the risk of developing schizophrenia.

The presence of more than one of these SNPs gives a “polygenic” (multiple gene variants) risk factor, and some SNPs are associated with a particularly higher risk (having the most significant associations with schizophrenia).

These risk scores were analysed in comparison with the answers to the cannabis questions to look for any associations.

In the second part of the study, the researchers looked at the polygenic risk scores of 990 twins (just over a third were identical twins).

They took the mean polygenic risk score from each pair of twins and used this to predict whether neither, one or both twins used cannabis.

What were the basic results?

Out of the 2,082 adults included in the study, 1,011 (48.6%) had ever used cannabis. The mean age of starting cannabis was 20.1 (95% Confidence Interval [CI] 19.7 to 20.5) and the mean number of times they’d used cannabis over their lifetime was 62.7 (95% CI 19.7 to 20.5).

The researchers found a significant association between a person’s extent of genetic predisposition for schizophrenia and their reported use of cannabis. People who had used cannabis had higher genetic risk scores for schizophrenia than those who had never used cannabis. The strongest associations were found between the higher risk SNPs and ever use of cannabis.

However, the results showed that the genetic risk factors they assessed only predicted a small amount of a person's risk of using cannabis. This meant that other factors have more of an influence on whether a person uses cannabis.

In the secondary analysis, twin pairs where both reported using cannabis had the greatest polygenic risk factors for schizophrenia.

Pairs where only one of them used cannabis had an intermediate level of risk factors, and the lowest burden was in those where neither used cannabis.

How did the researchers interpret the results?

The researchers say this study shows “that to some extent the association between cannabis and schizophrenia is due to a shared genetic aetiology [cause] across common variants. They suggest that individuals with an increased genetic predisposition to schizophrenia are both more likely to use cannabis and to use it in greater quantities.”

Conclusion

This study shows an association between genetic risk factors for schizophrenia and cannabis use. However, as it is a cross-sectional study, it cannot answer the often debated cause and effect question of whether cannabis use increases risk of schizophrenia, or whether there is a common genetic predisposition to both.

The study cannot prove that cannabis use is a risk factor for developing schizophrenia.

It also cannot prove that the genetic risk factors (SNPs – variations in the DNA sequence that have been associated with schizophrenia) also directly increase the risk of using cannabis. As the researchers’ results suggested, the genetic risk factors they assessed only predicted a small amount of a person's risk of using cannabis. There may be many other factors involved. A complex mixture of genetics (including DNA variations not examined here), lifestyle and environmental factors is likely to contribute to a person’s risk of developing schizophrenia, and to their risk of using cannabis.

It should also be noted that none of the participants in the study actually had a diagnosis of schizophrenia. Though the SNPs thought to increase the genetic risk of developing schizophrenia were identified in a large Swedish cohort study, the authors do point out that they may not be accurate.

They say that in this Swedish sample from which these SNPs were identified, use of cannabis may have been more common among the people who had schizophrenia than in the controls without schizophrenia.

They say this could mean that the SNPs actually increase risk of cannabis use rather than risk of schizophrenia.

A further limitation of the study is that cannabis use was self-reported which may give rise to inaccuracies in the estimated level of use. Also people may not have been willing to disclose any use of an illegal substance during a telephone interview.

Cannabis may not be as dangerous as other drugs (including legal drugs such as tobacco and alcohol) but it is certainly not safe. There are many negative effects of cannabis, including a risk of developing dependency, its tendency to reduce motivation and concentration, and the likelihood that it reduces male fertility.

Furthermore, the risks of the tobacco and nicotine which are usually consumed at the same time need to be taken seriously. Read more information about the health risks associated with cannabis.

Analysis by Bazian. Edited by NHS Choices. Follow Behind the Headlines on Twitter. Join the Healthy Evidence forum.

Links to the headlines
Are cannabis smokers genetically more likely to develop schizophrenia? Study finds people predisposed to the condition and drug users share common genes. Mail Online, June 24 2014

Links to the science
Power RA, Verweij KJH, Zuhair M, et al. Genetic predisposition to schizophrenia associated with increased use of cannabis. Molecule Psychiatry. Published online June 24 2014

http://www.nhs.uk/news/2014/06June/Pages/Cannabis-use-genetically-linked-to-schizophrenia.aspx

Discovery Genomics program at the Duke Clinical Research Institute

Duke’s Discovery Genomics center rides pharmacogenomics wave
2010-12-14 08:00

By Jessica Wapner

Last year, a research project led by David Goldstein of Duke University found that variations in the IL28B gene had profound effects on how people with hepatitis C respond to treatment. More specifically, those with certain mutations were twice as likely to respond to prolonged drug therapy (Nature 461, 399–401, 2009). The link's impact on patient care and drug development served as a wake-up call for investigators to ramp up efforts to identify genetic variants associated with disease occurrence and treatment response.

Spurred by this homegrown insight, investigators at the Duke University School of Medicine in Durham, North Carolina decided to form Discovery Genomics. The new center—a collaboration between the Duke Clinical Research Institute (DCRI) and the university's Center for Human Genome Variation (CHGV)—launched in October. Although projects within the university are its main focus, Discovery Genomics also operates as a fee-for-service business for pharmaceutical companies interested in finding genetic variations associated with drug efficacy and toxicity
Source


DURHAM – Duke University is looking to capitalize on its extensive research into genomics by offering to provide genetic analysis in clinical trials.

The Discovery Genomics program at the Duke Clinical Research Institute, Duke’s clinical research organization, will offer pharmaceutical companies the opportunity to study the genetic composition of clinical trial participants. That information could tell companies whether a drug will work better in some people and not others or whether certain individuals are more prone to incurring side effects.

DCRI is among the first, if not the first, to develop a program for analyzing the genetic sequences of clinical trial patients.

“It is now possible to screen a genome for variations that have strong influences on therapeutic responses and susceptibility to adverse events,” says David Goldstein, director of Discovery Genomics. “An adverse event that severe can make it difficult to win approval. We can use genetics to see if a pattern can be found.”

The decision by DCRI to offer the new service comes after work done by Goldstein and his colleagues at the Duke Center for Human Genome Variation identified how genetic variations affected the treatment of hepatitis C patients.

The course of treatment for the viral infection typically consists of 48 weeks worth of drugs interferon and antiviral ribavirin. The treatment only works in about half of patients and can cause severe complications, including anemia. Many patients drop out before completing the treatment regimen.

Goldstein and his team, working with Schering-Plough, which helped fund the study, analyzed the blood samples from clinical trial participants taking the drug and found genetic variations within patients who benefited from the treatment and within patients who suffered from side effects.

Using that information, LabCorp developed a test for the genetic variation to help hepatitis C patients determine whether they are likely to benefit from the drug or are likely to incur side effects, helping them decide whether they want to undergo the treatment.

Dr. Bob Harrington, director of DCRI, says such revelations are particularly exciting for physicians, who are now better able to steer patients to the best treatment options.

Dr. Kevin Schulman, associate director of DCRI, says this is the foundation needed for personalized medicine to come to fruition.

Personalized medicine is a concept in which a patient’s genetic information is used to identify the best courses of treatment, as well as to identify diseases the patient is more predisposed toward and to work toward preventing those diseases.

Geoff Banks, president and founder of ClinPharm Consulting, says there has been a lot of talk about personalized medicine, but the idea has not been readily implemented due to cost and complication. “I think it’s good that Duke is doing this,” he says. “I think there is a need in the industry to start moving in this direction and to have these services available.”

The genetic analysis work is benefiting from the quickly dropping cost for gene sequencing and analysis.

A genomewide association study to determine which patients are more likely to benefit from a treatment would take about two to three months to complete and cost about $500,000, while a study to determine which patients are more likely to suffer side effects takes about three to five months to complete and costs between $125,000 and $750,000.

Such studies could, however, end up reducing the market for particular drugs by identifying the subset that most benefits.

But, says Tom Kaminski, in business development at DCRI, such studies could salvage a drug that has failed a critical trial by identifying appropriate patients or by helping pharmaceutical companies find other uses for their drugs. DCRI staff currently are looking back into some clinical trials for failed drugs to see if trial participant genetics played a role and if there are any drugs that can be salvaged.

So far, the U.S. Food and Drug Administration has not weighed in on genetic analysis of clinical trial participants, though Goldstein says that based on conversations he has had with the regulatory body, he believes the agency could look to use it to assure patient safety.

Read more: Duke expands genetics research Triangle Business Journal

Genes and Hepatitis C; Susceptibility, Fibrosis Progression and Response to Treatment

From Liver International

Genes and Hepatitis C

Susceptibility, Fibrosis Progression and Response to Treatment

Manuel Romero-Gomez; Mohamed Eslam; Agustín Ruiz; Marta Maraver

Authors and Disclosures

Posted: 04/28/2011; Liver International. 2011;31(4):443-460. © 2011 Blackwell Publishing

Abstract

Hepatitis C virus contact and infection show three different phenotypes: spontaneous viral clearance (SVC), chronic hepatitis C (CHC) and sustained virological response (SVR) following antiviral treatment. Many factors, including genetics, influence the evolution of these three phenotypes. We performed a literature search (PubMed) up to 31 January 2010 without language restriction to identify relevant studies on genes and hepatitis C. Additional studies were sought by reviewing the reference lists of the identified articles. Meta-analysis (using Meta-disk 1.4) was conducted to evaluate the association of single nucleotide polymorphism (SNP) in the IL28B region and SVR. The candidate gene approach showed strong relationships between human leucocyte antigen class II (DQB1*0301 and DRB1*1101) and SVC. A cirrhosis risk score involving 7 SNPs has been validated recently. The set of odds ratios of studies demonstrated an association between SNP (rs12987960/rs8099917) in the IL28B and SVR in CHC treated with peginterferon plus ribavirin (OR: 4.6; 95% CI: 2.9–7.3). The overall distribution of protective allele correlated with ethnic differences in SVR (Asians, Europeans, Hispanic and Afro-Americans) together with SVC, but not with fibrosis stage or viral load. These polymorphisms did not influence SVR in very-easy-to-treat patients such as genotype 2/3, rapid virological responders or patients with acute hepatitis C. While the genetic fingerprint for fibrosis progression remains elusive, IL28b polymorphism predicts SVC and SVR. However, nearly half of patients achieving SVR did not show favourable genotype. Further genetic signals are warranted to complete the puzzle of factors influencing hepatitis C.

Introduction
Over the past 15 years, several studies have addressed the role of genetic factors in spontaneous clearance, fibrosis progression and response to combined antiviral therapy. Documented evidence indicates that interindividual genome variability contributes considerably to the observed differences in natural resistance or susceptibility to specific micro-organism, to the observed phenotype once infection is established, or to the therapeutic response when the infectious disease is pharmacologically treated. Recently, genome-wide association studies (GWAS) including a large number of single nucleotide polymorphisms (SNP) confirmed the influence of some polymorphisms in the interleukin 28B (IL28B) region on the possibility of achieving viral clearance, either spontaneously or after pegylated interferon alpha+ribavirin (Peg-IFN/RBV) treatment. This genetic marker is a stronger predictor of sustained response than other well-documented factors such as viral genotype, viral load or fibrosis. All these data supported the emerging interest of hepatologists in the influence of genetic factors in hepatitis C virus (HCV). In this review, we analyse the impact of genes on susceptibility, fibrosis progression and sustained virological response (SVR).

Methods
Relevant studies were identified by searching Medline (PubMed) up to 31 January 2010. We searched the literature without language restriction using a combination of the following terms: ('Hepatitis C'[Mesh] OR 'Hepacivirus'[Mesh] OR 'Hepatitis C, Chronic'[Mesh]) AND ('Databases, Genetic'[Mesh] OR 'Genetic Testing [Mesh] OR 'Genetic Association Studies'[Mesh] OR 'Genetic Loci'[Mesh]). To guarantee no loss of specific articles relating genetic factors and susceptibility to HCV infection, or hepatic fibrosis progression, or SVR following IFN treatment, a search combining the terms 'Hepatitis C'[Mesh]' AND 'Genetic Predisposition to Disease'[Mesh] OR 'Liver Cirrhosis'[Mesh] OR 'sustained virological response' was also performed. Additional studies were sought by reviewing the reference lists of the identified articles. Two co-authors separately examined the titles and abstracts. All original human studies concerning the topics of this review were selected for further full-article analysis, provided phenotypes and genotypes were correctly defined. Because the field of hepatitis C genetics remains a topic on which few studies have used prospective designs or had included large sample sizes, it was our intent to include as much information as possible despite some of these data requiring confirmation in better-designed studies. We conducted a meta-analysis that included all published studies reporting odds ratio (OR) or risk ratio that had been calculated by comparing the prevalence of genotype CC (rs12968760) in patients with SVR vs non-SVR. Data were combined using fixed effect (Mantel–Haenszel) as well as random effect (DerSimonian and Laird) models. Random effects were selected when heterogeneity was present.
The phenotypes analysed in the current review were: (a) spontaneous viral clearance (SVC), defined as patients showing positive anti-HCV by EIA 3.0 and repeatedly negative HCVRNA by polymerase chain reaction (PCR); (b) histological fibrosis assessed by Metavir, Scheuer or Ishack index. Fibrosis progression rate was calculated in some studies when the date of infection was known. An index was derived as the fibrosis stage divided by the period of time between infection and liver biopsy. Patients could be classified in cross-sectional analysis as advanced vs non-advanced fibrosis or according fibrosis progression rate as 'faster' or 'slower' fibrosis; (c) SVR was defined as negative HCVRNA at 6 months from the end of therapy.

Genetic Factors and Susceptibility to Hepatitis C

Following acute HCV infection, spontaneous resolution of HCV infection has been reported as being between 10 and 80% of those infected; approximately a quarter of patients with acute HCV spontaneously clear the virus.^[1] Race and gender are the main factors involved in spontaneous clearance, together with clinical presentation (clearance is more often seen after icteric hepatitis), absence of human immunodeficiency virus (HIV) co-infection, rapid decline of HCV RNA and the strength and pattern of hepatitis C-specific CD4 cell responses.^[2] Host factors have been involved in infectious diseases under the paradigm of gene–environmental interaction. To control infectious agents, multicellular organisms have developed an extremely sophisticated defense network, i.e. the immune system, which serves to repel viral and bacterial infections and to avoid pervasive distribution of microorganisms within tissues that may compromise overall hemostasis and survival. Genome-wide RNA interference experiments have revealed a panoply of host genes involved in HCV propagation.^[3] Host genetic factors contribute towards explaining differences in the natural history of several infectious diseases such as leprosy,^[4,5] AIDS,^[6,7] dengue,^[8] malaria^[9] or chronic hepatitis B.^[10] Further, there are specific genomic variants related to the immune system, such as those observed within MAL gene, that can confer on the carriers of these variants a degree of protection against a variety of microorganisms.^[11] HCV infection is not an exception, and the discovery of host factor contributing to HCV pathogenesis will help us to understand host–virus relationships, and to improve our management of the HCV pandemic.

An Update on the Search for Hepatitis C Virus-related Genetic Host Factors

The candidate gene approach has been the most widely used in identifying genetic factors involved in HCV susceptibility. This implies that almost all studies performed to-date are hypothesis driven and that the selections of candidate genes have been based on the knowledge accumulated on HCV lifecycle, or genes involved in the functioning of the immune system. An analysis of the selected candidate genes reveals that investigators have scanned the human leucocyte antigen (HLA) region in detail, as well as cytokines related to natural and acquired immunity such as IFN, tumour growth factor (TGFβ1) and IL pathways. In contrast, other genes related to viral lifecycle have received less attention (see details later).

Candidate Genes from the Human Leucocyte Antigen Region

The HLA region, located at chromosome 6p21, comprises several of genes involved in antigen recognition and host immunity. Alric et al.^[12] originally proposed the contribution of HLA region to HCV risk. Antigen class II DQB1*0301 and DRB1*1101 alleles have been associated with spontaneous clearance of HCV in several independent studies with individuals of different ethnic backgrounds, and supported by meta-analyses.^[13] Indeed, high-resolution genotyping of major histocompatibility (MHC) antigens class I and II of a large multiracial cohort of women from the USA infected by HCV demonstrated that several class I and class II alleles are associated with HCV viraemia.^[14] Although the association between HLA region and SVC seems incontrovertible, the specific variants involved in this genetic association, the exact proteins related to SVC and/or the molecular mechanisms of such variants remain largely unclear. Further, the strong (and long range) linkage disequilibrium (LD) within the HLA region and the co-existence of multiple optimal candidate genes within this DNA segment complicate the positional cloning of true functional variants underlying the observed genetic associations with SVC. For example, it was proposed that one of the contributing genes for this association could be tumour necrosis factor (TNF). TNF locus is located at 6p21.33 within the HLA region between lymphotoxin alpha and beta (LTA and LTB) loci. Overall, TNF is located closer to HLA-G and HLA-B regions than to the HLA-DQ locus and it was originally proposed to play a critical role in immune response to HCV infection.^[15] However, a meta-analysis of 12 independent studies comprising 1395 cases and 1288 controls for the −308A/G marker revealed a slight, and statistically non-significant, effect of this marker for the risk of HCV infection (OR=1.18; P=0.096).^[16] However, because of this weak effect, combined with the observed trend towards association, it cannot be ruled out that TNF variants might contribute to HLA region-SVC phenotype association by acting in concert (additively or epistatically) with other polymorphisms within the HLA region.^[17] Indeed, MHC classes I and II loci are >1.2 megabases away, and the genetic signals observed are probably tracking different functional variants. Individual differences in spontaneous resolution of HCV infection could be explained by the existence of epistasis between HLA region and other unlinked loci. It was hypothesized that the existence of epistasis between MHC class I and killer cell immunoglobulin-like receptor (KIR) genes might modulate the spontaneous clearance of HCV. Notably, homozygous carriers of genotypic combinations of KIR (2DL3/2DL3) and its natural ligand, HLA-C (C1/C1 allotype), are protected against HCV infection (OR for spontaneous resolution=1.71, CI: 1.2–2.42; P=0.003).^[18] Further, this original study also suggested that a therapeutic reduction of inhibitory signals to natural killer might help the host in the spontaneous clearance of HCV. However, studies to replicate the effect of this genotype pair have produced contradictory results, i.e. positive association in some series,^[19,20] and negative in others.^[21] Analyses using larger series and exhaustive meta-analyses are necessary for a definitive view of these interesting findings.

Other Candidate Genes Analysed

Early-on, the existence of genes outside of the HLA region had been hypothesized in relation to SVC.^[22] Several investigators selected markers within/near IL genes such IL1, IL6, IL4, IL10, IL12B, IL18, IL19/IL20, IL22 or IL10RA and their natural receptors, as candidate genes in exploring HCV infection risk.^[23–31] Overall, these studies lack the consistence observed for the HLA region; the findings have not been systematically confirmed^{[22,24,26,32,33]} and remain controversial. Most of these studies need to be considered preliminary and require corroboration in larger series. IL10 is located at 1q32 chromosomal region within a cluster of paralog genes comprising IL10, IL19, IL20 and IL24 cytokines. The protein encoded by this gene is a cytokine produced primarily by monocytes, and to a lesser extent, by lymphocytes. This cytokine has pleiotropic effects in immunoregulation and inflammation. It down-regulates the expression of T helper 1 (Th1) cytokines, MHC class II antigens and co-stimulatory molecules on macrophages. It also enhances B cell survival, proliferation and antibody production. High IL10 levels have been related to disease progression^[34] and IL10 polymorphisms have been associated with SVC in at least four independent studies.^{[23,25,28,29]}

Some genetic signals observed in loci such as TGFβ1,^[35] CTLA4,^[36] interferon regulatory factor-1 (IRF-1),^[37,38] C–C motif chemokine receptor gene cluster,^[39,40] LTA (a gene of the HLA region),^[41] STAT1 or interferon alpha2 (INFA2)^[42] and interferon gamma^[43] require further validation. Lastly, genetic elements not directly related to the immune system, most of them HCV-interacting proteins, are involved in different stages of HCV lifecycle. The role of claudin 1 coreceptor, involved in late-stage HCV binding to the cell,^[44] or polymorphisms in the low-density lipoprotein receptor (LDL-R) gene involved in viral endocytosis^[45,46] together with apolipoproteins (apoE and apoB) are used by HCV as blood vehicles that facilitate interaction with the LDL receptor during HCV endocytosis.^[47,48] ApoE can also be recruited by HCV machinery during late stages of lifecycle for viral assembly.^[49] APOE¢4 alleles might protect against severe liver disease^[50] but APOE¢3 allele has been associated with HCV infection persistence.^[51] In addition, variability in the promoter region of the APOB gene might also modify HCV susceptibility.^[49] Hence, the apolipoprotein variability hypothesis for HCV infection seems attractive, and further research is warranted. Factors influencing the interaction between host and the virus, such as the paraoxonase-1 (PON1)-192 polymorphism^[52] could contribute, together with other polymorphisms, to the variations in the host response to HCV infection.

Genome-wide Association Studies in Susceptibility Analysis

In addition to information obtained using hypothesis-driven studies (the candidate gene approach), we now have information from early GWAS analyses conducted to identify the mechanisms underlying spontaneous HCV clearance. Overall, these hypothesis-free investigations using massive, parallel, genotypic research technologies (DNA arrays) combined with the emerging independent studies that reproduce these findings have provided incontrovertible evidence that genomic variation near the IL28B locus is directly related to SVC. IL28B encodes a cytokine distantly related to type I IFNs and the IL10 family. Together with IL28A and IL29, there are three closely related cytokine genes that form a cytokine gene cluster on a chromosomal region mapped to 19q13. Expression of the cytokines encoded by the three genes can be induced by viral infection. All three cytokines have been shown to interact with a heterodimeric class II cytokine receptor that consists of interleukin 10-receptor beta and interleukin 28-receptor alpha. Both receptors are now strong candidates for further genetic analysis. In the initial study, Ge et al.^[53] found a higher prevalence of the CC genotype in healthy individuals compared with those with chronic hepatitis C (CHC) (73 vs 63%; P<2.5 × 10⁻⁶). However, these differences have not been confirmed in subsequent studies. Thomas et al.^[54] evaluated a cohort of 388 patients who spontaneously resolved the infection and compared them with 620 patients with CHC. Genotype CC from the rs12979860 in the IL28B gene was found in 58.5% (227/388) of patients with spontaneous clearance and in 32.6% (202/620) of patients with CHC (P=3 × 10⁻¹³). These results have been confirmed in two further studies. Montes-Cano et al.^[55] confirmed that the prevalence of genotype CC in patients with SVC was two-fold that of chronic carriers. Rauch and colleagues conducted a GWAS and identified a genetic signal in chromosome 19 that was strongly related to spontaneous clearance, irrespective of HIV co-infection or route of infection. Moreover, gender remained an independent variable associated with spontaneous clearance (OR: 1.7; 13–2.4).^[56] Indeed, the distribution of IL28B rs12979860 genotype CC was similar in males and females (72.5 vs72.4%; P=NS) with spontaneous clearance. Lastly, spontaneous clearance rate was lower in males and in Afro-Americans (AAs). In AA, the prevalence of the polymorphism was higher in patients with SVC [genotype CC: 33% (32/97) vs. 13.5% (26/193) than in patients with CHC; P=1 × 10⁻⁴].

In summary, as hypothesized previously, genetic factors play a key role in clearing the virus post-infection. HLA class II and some genetic signals located in the 19q13 region, including IL28B, KIR2DL3, TGFβ1, LDLR and APOE, are the strongest predictors of spontaneous clearance (Table 1) Table 1b, 1c.

Genetic Factors Related to Fibrosis Progression

Fibrosis progression is the main prognosis determinant of liver disease outcome in CHC.^[57] The natural history of the infection clearly shows a subgroup of patients chronically infected with HCV (a range from 5 to 20% in several cohort studies^[58]) who gradually could progress to cirrhosis and to end-stage liver disease. Patients may be classified according to Metavir score from F0 to F4. In genetic studies, the fibrosis phenotype could be analysed in two different ways: (a) patients who reached advanced fibrosis (F3–F4 vs F0–F2); (b) fibrosis progression rate, i.e. dividing fibrosis detected in liver biopsy (0–4) by duration of infection (in years). Variables associated with fibrosis progression include: (a) age at infection; (b) alcohol intake (>50 g/day); (c) male gender; (d) hepatitis B co-infection; (e) immunodeficiency because of HIV or the use of immunosuppressant drugs such of those used after liver transplantation; (f) excess weight; (g) liver steatosis; (h) presence of metabolic syndrome and/or type II diabetes; (i) iron overload.^[59] Host genetic factors might have a relevant influence on the natural history of CHC. Many studies have implicated several SNPs (single nucleotide variations at specific positions of the genome detected in more than 1% of population) in order to analyze their possible impact on fibrosis progression, and the risk of hepatocellular carcinoma.^[60]

Candidate Genes Implicated in the Immune and Inflammatory Response: Human Leucocyte Antigen and Interleukins

Human leucocyte antigen class II haplotypes, mainly of DRB1, were associated with persistently normal alanine transaminase (ALT) and mild fibrosis^[61,62] in a cohort of 83 patients with normal ALT levels over a 6-month period and 233 patients with elevated ALT. HLADRB1*11 was overrepresented in those with normal ALT levels (43 vs 24%, OR: 2.36) and mild fibrosis.^[63] Moreover, DRB1*11 allele was associated with a lower progression rate (1.58 vs 2.14) and a lower probability of developing cirrhosis.^[64] However, other studies did not find this association between HLA class and fibrosis progression.^[65,66] Association studies of fibrosis progression with polymorphisms in chemokine receptor 5 (CCR-5), monocyte chemotactic protein 2 (MCP-2) and monocyte chemotactic protein-1 (MCP-1) are conflicting in their findings. Hellier et al.^[67] included 337 patients in their study and found that the Δ–32 deletion was associated with more advanced fibrosis (OR=1.97; P=0.015), but with reduced portal inflammation. These results have not been confirmed in other studies but, instead, have been contradicted.^[68–70] Despite an initial association observed between MCP-2 and MCP-1^[71] with fibrosis, subsequent studies have not confirmed this association.^[72,73] Kato and colleagues^[74] studied nine SNPs of the interferon regulatory factor-7 (IRF-7) gene (four of these SNPs in the promoter region) in 406 patients (178 with cirrhosis). Two non-synonymous SNPs at positions 1047 and 2157 (A-to-G in both cases) resulting in amino acid changes (Lys/Glu and Gln/Arg respectively) were reported. The polymorphisms 1047AG and 2157AG were in complete LD, and they were more frequently seen in cirrhotic patients (5.6%) than in non-cirrhotic individuals (1.7%) (OR: 3.27; P=0.03). However, there was no association between SNPs in the promoter region and the presence of cirrhosis. In multivariate analysis, 1047AG and 2157AG were independently associated with cirrhosis (AA vs AG-adjusted OR: 2.5; 95% CI: 1.2–5.6; P=0.02). IRF-7 has been found to affect immune responses mainly by regulating the transcription of IFN-stimulated genes.^[75] Additional possible mechanisms for the effect of IRF-7 gene polymorphisms on the progression of liver fibrosis include the IRF-7-induced activation of IFN-β and regulated on activation normal T cell expressed and secreted (RANTES). RANTES serves as a key ligand for CCR5 and plays a significant role in attracting T cells to the portal area of the liver infected with HCV. The activation of RANTES has been suggested to be involved in the progression of CHC to advanced forms of liver disease.^[76,77] IL10 is known to influence the Th1/Th2 cytokine profile, affecting both the innate and adaptive immune responses to infection. HCV has been shown to induce the activation of IL10 secretion, and increased IL10 production has been observed to correlate with persistent HCV infection, higher inflammation grade and an increased risk of liver cancer. Two receptor chains, IL10RA and IL10RB, are known to mediate the functions of IL10. Although SNP in the minor allele in G330R IL10R1^[78] and the homozygosity for two IL10 haplotypes:^[79,80] −819 (Cto-A); and −1082 (AA genotype, ATA/ATA and ACC/ACC) have been reported to be associated with faster fibrosis rates, contradictory findings have been observed in different studies evaluating the significance of this last-mentioned polymorphism. Hennig et al.^[30] examined 631 HCV patients and found a variation in IL10RA, IL10RA-rs9610 (3'-UTR), which appeared to be correlated with reduced inflammation, individuals carrying an A allele being less likely to present with severe inflammation (OR=0.29; 95% CI: 0.10–0.83; P<0.05). IL18, also called IFN-c–inducing factor, is an obligatory cytokine for IFN-c production, and plays a key role in the induction of Th1 responses and viral clearance as well as in the development of liver fibrosis. Single-nucleotide promoter polymorphisms influence the transcription of IL18 mRNA. Pro-IL18 is a biologically inactive precursor that is produced by monocytes, macrophages and immature dendritic cells during an acute immune response. It is activated intracellularly by caspase-1 to induce IFN-c, iNOS (inducible nitric oxide synthetase), TNF-α secretion, as well as induction of other inflammatory cytokines such as IL6, IL8, IL2, MCP-1, MIP-a and MIP-b. Increased IL18 production is neutralized by IL18BP in CHC infection, and this neutralization is crucial for the regulation of inflammation and fibrosis development. Manohar et al.^[81] have investigated the association between the −607 polymorphism and severity of HCV infection. They evaluated 204 patients with CHC and 350 matched healthy controls. The −607 A/A allele was more common in patients with mild disease than in patients with severe diseases (38.6% vs 21%, OR=0.424; P=0.05). IL18 promoter −607 A/A allele is a potential protective marker in patients with CHC. These results have been confirmed by another study.^[82] IL12 is a cytokine that induces production of the IFNγ. Suneetha et al.^[83] showed that homozygosity for the minor allele of this SNP, 1188C/C, was more common in patients who had mild fibrosis compared with those with severe fibrosis (23.7 vs 6.25%; P=0.004). Several studies have shown that TNF may play a role in the pathogenesis of CHC by influencing fibrosis progression rate. Dai et al.^[84] investigated the biallelic polymorphism G vs A in the promoter region at positions −308 (TNF308.2) and −238 (TNF238.2). In 250 biopsy-proven CHC individuals, the TNF 308.2 allele copy numbers were significantly associated with more severe fibrosis stage (F3–F4; P=0.006) and higher mean fibrosis score (P=0.007). Logistic regression analysis showed that a higher fibrosis score was independently related to the TNF308.2 allele (OR: 1.38). However, conflicting results have been reported on this association.^[85,86] ApoE binds to some cellular receptors including proteoglycans, heparan sulphate and LDL-R. The HCV competes with the apoE in the process of entry into the cell via the LDL-R. Specifically, apoE4 is the protein subtype with more affinity for these receptors while the apoE-2 has the lowest affinity. Patients carrying ¢4 allele seem to be protected against cirrhosis (4.3 vs 19.1% in ¢2/¢3 allele carriers). In a cohort of 111 patients, the ¢4 allele was associated with a lower risk of cirrhosis when evaluated in the multivariate analysis that took into account confounding factors such as gender, alcohol intake, time of evolution of the infection and the age at biopsy^[87] However, APOB SNPs did not influence liver fibrosis rate.^[47] SNPs at positions 1874 (T-to-A) of the IFN-γ gene has also been described as accelerating the rate of fibrosis progression in HCV patients.^[88] The enzyme 2'-5'-oligoadenylate synthetase 1 (OAS-1), an important component of the innate immune system, has an antiviral function.^[89] Li et al.^[90] reported the association between six SNPs of OAS-1 and fibrosis in 409 patients with HCV. Patients with rs3741981 genotypes A/A, A/G and G/G of an SNP of OAS-1 at the exon 3 were at a gradient increased risk of fibrosis progression and suffering from cirrhosis (P=0.001). Multivariate logistic regression analysis indicated that genotype G/G was an independent factor associated with cirrhosis (OR: 3.11; P=0.013). Mannan-binding lectin (MBL), encoded by the MBL2 gene, can have an important role as an opsonin and complement-activating molecule with an important function in the initiation, regulation and amplification of immune response. A small number of studies have examined the relationship between MBL polymorphisms and HCV infection. These studies differ with respect to the cohorts used, categorization of subjects and the MBL gene mutations investigated. Brown et al.^[91] demonstrated an association of higher MBL/MASP-1 complex activity (related to polymorphisms in the promoter and structural regions of MBL gene) with severity of fibrosis in HCV. In 102 Euro-Brazilian patients,^[92] MBL2 polymorphism was associated with moderate and severe fibrosis in CHC, after controlling for gender and age. Six common SNPs, three in the promoter (H/L, X/Y and P/Q) and three in exon 1 (A, the wild-type, and B, C or D also known as O) were evaluated using real-time polymerase chain reaction (RT-PCR) with fluorescent hybridization probes. The frequency of the YA/YO genotype was significantly higher in H patients vs the controls (P=0.022). Genotypes associated with low levels of MBL (XA/XA, XA/YO and YO/YO) were decreased significantly in the patients with severe fibrosis (stage F4) compared with patients with moderate fibrosis (stage F2) (P=0.04) and to the control group (P=0.011). Genes involved in the transduction of myxovirus (MxA) and protein kinase R (PKR) together with pro-inflammatory cytokines involved in IFN resistance such as IL10, TNF, IL6, GH and IL1 have been selected for candidate gene analyses. Yee and colleagues investigated the association between myxovirus resistance-1 (Mx1), PKR, liver fibrosis in 374 treatment-naive patients with genotype-1 chronic HCV infection (194 Caucasian Americans CAs and 180 Aas), using a genetic haplotype approach. He reported independent association between Mx1-CAGT and PKR-TGATT and less severe hepatic fibrosis even after control for the confounding factors such as race and gender. However, the associations were not statistically significant in a second, independent validation cohort.^[93]

Candidate Genes Implicated in Iron Metabolism as Fibrogenic Factor

Iron overload seems to induce a deleterious effect on fibrosis progression rate. However, the relationship between the presence of HFE gene mutations and disease progression remains controversial. Some authors have reported a strong association between mutations and fibrosis progression^[94] while others have not observed these relationships.^[95–97] Thorburn et al.^[98] in a large cohort did not detect association, whereas Erhardt et al.^[99] did, and highlighted that their systematic study demonstrated that the association between HFE gene mutations and progression was independent of other confounding variables. Tung et al.^[100] studied 316 CHC patients and highlighted that heterozygous mutations in both exon 2 and exon 4 were associated with increased histological damage, and a faster fibrosis progression rate. No associations between histological progression of liver disease and TfR1 SNPs^[101–103] have been reported. Further, Nramp1 protein, now termed solute carrier family 11 member 1 (SLC11A1) protein is located in the late endosomal compartment of resting macrophages, and is recruited to the phagosome by phagocytosis and seems to be an intracellular transporter of iron, while playing an important role in immune response against intracellular microorganisms such as HCV. Several mutations resulting in polymorphic mRNA expression have been identified in the SLC11A1 gene (2q35). Four alleles have been found in different populations with the absence of allele 3 exerting a protective effect on progression to cirrhosis in patients with HCV. In a Spanish cohort that included 242 patients with biopsy-proven CHC and 194 healthy control subjects, allele 3 carriers showed faster fibrosis progression rate of 0.16 ± 0.20 vs 0.09 ± 0.08 units of fibrosis/year. Hence, the 2/2 genotype of the promoter region of the SLC11A1 gene was found to be associated with mild portal inflammation and a lack of advanced fibrosis (OR: 8.85; P=0.002). Indeed, the interaction between allele 3 of SLC11A1 and the -238 A/G mutations in the promoter region of TNF gene appears to promote accelerated progression of fibrosis (OR: 2.53; P=0.039).^[104]

Candidate Genes Implicated in Hepatic Stellate Cell Activation

TGFβ1 plays a significant role in hepatic stellate cell activation, and increased levels of this chemokine have been related to a faster rate of fibrosis progression.^[105] Several polymorphisms have been described: Arg/Pro mutation at codon 25 (G/C carriers) induces a rapid progression to cirrhosis with a progression rate of 0.23 vs 0.008 of the Arg/Arg group (non-mutated TGFβ1). These results proceeded from studies with small series of subjects but have been confirmed in further cohorts.^[106–109] Thus, SNPs from these 2 genes (TGFβ1 and AT) are confirmed as being implicated in fibrosis progression. Several factors such as hyperhomocysteinaemia and C protein deficiency as well as increased factor VIII:C have been associated with fibrosis progression.^[110] Factor V Leiden mutation A560G has been associated with an increased risk of cirrhosis and faster fibrosis progression (0.37 vs 0.18 units of fibrosis/year).^[111,112] Inhibition of thrombin receptor protease-activated receptor 1 polymorphism, 1426 (C/T instead of T/T);^[113] or myeloperoxidase gene −463A have been shown to be associated with greater fibrosis.^[114,115] With the haplotype approach, Mx1-CAGT and PKR-TGATT haplotypes of antiviral genes were independently associated with mild liver fibrosis, following adjustment for potential confounders (for Mx1-CAGT haplotype: OR: 0.33; P=0.0027; for PKR-TGATT haplotype: OR: 0.56; P=0.0405). These findings were validated using an independent cohort in which a protective trend for the PKR-TGATT and Mx1-CAGT haplotypes was confirmed, albeit the association with the latter haplotype was not statistically significant.

Genome-wide Scan and Fibrosis Progression

A genome-wide scan including 24 823 candidate SNPs from 12 248 covering genes in 433 biopsy-proven CHC individuals identified 100 SNPs associated with an increased risk of advanced fibrosis.^[116] In 483 patients from the validation cohort, only two out of 100 were found to be associated with advanced fibrosis. A missense SNP in the DEAD (Asp–Glu–Ala–Asp) box polypeptide 5 gene causing an amino acid replacement at position 480 (S480A) in exon 13 was associated with an increased risk of advanced fibrosis (OR: 1.8 and 2.3 in the 2 cohorts), while a missense SNP in the carnitine palmitoyltransferase 1A gene causing amino acid change at position 275 (A275T) in exon 8 was associated with a decreased risk for advanced fibrosis (OR: 0.3 and 0.6 in the 2 cohorts). A cirrhosis risk score (CRS) based on genetic markers identified from two Caucasian cohorts was derived from 361 SNPs showing associations with fibrosis. Seven SNPs (one SNP in the antizyme-inhibitor-1 gene, one SNP in the Toll-like receptor-4 gene and 5 SNPs in five other genes of unclear function) showed the highest predictability for cirrhosis.^[117] CRS offered a better prediction of cirrhosis compared with clinical factors (age, gender and alcohol abuse). Two CRS cut-off values were eventually suggested to identify the majority of patients at low risk (<50) and those at high risk (>70) of developing cirrhosis. Genetic CRS has been recently confirmed by Li et al.^[118] in 420 Caucasian individuals and by Marcolongo et al.^[119] in 271 patients with mild fibrosis followed-up over 60 months without therapy. The best prediction accuracy of CRS was in males with no fibrosis at baseline.

Genetic Variations and Sustained Response to Peginterferon+ribavirin Treatment

Sustained virological response rate variability following a course of Peg-IFN/RBV treatment is extremely high. Genotype, viral load, fibrosis and metabolic disturbances including obesity, insulin resistance and steatosis were the factors most influential in SVR.^[120] Several SNPs from candidate genes have been associated with achieving SVR in patients receiving Peg-IFN/RBV treatment. Regulatory genes of IFN antiviral activity, immune-response genes and genes implicated in obesity and in insulin resistance have been analysed. The mechanism of action of IFN has been characterized, and the key points identified are: (a) interaction with IFN alpha receptor; (b) Janus-kinase and tyrosin-kinase activity; (c) STATs phosphorylation; (d) synthesis of antiviral proteins such as 2'–5' OAS, MxA protein induced by IFN. Intracellular MxA, which works like GTPase to achieve its antiviral effect, seems to be the most specific marker of antiviral activity of IFN. In patients receiving induction doses of IFN, MxA levels increased in parallel with antiviral activity.^[121] SNP −88T in the MxA gene was found to be associated with lower MxA protein activity. In patients with a low viral load, SVR was statistically significantly higher (62%) in −88T patients than in patients bearing the −88A allele (32%).^[122] The 2'–5' OAS enzyme plays a major role in the clearance of the virus. However, some studies that had included analysis of the GG genotype (in the 3'UTR region) do not predict SVR.^[89] Lastly, a tandem repeat of three nucleotides in the PKR gene classified as 'large' when containing >9 repeats has been found to be associated with SVR. Large/large polymorphism was more often seen in patients achieving SVR than in non-responders (89.4 vs 71.8%; P=0.017).^[13] ApoE has been implicated in the mechanism of entry of the HCV into the cell via the LDL-R. In a cohort of 506 patients treated with Peg-IFN/RBV, the ¢4 allele was found to be associated with poorer response in patients with genotype 1 (30 vs 42%; P<0.05).^[123] Several polymorphisms from proinflammatory cytokines have been included as candidate genes in the prediction of SVR. The biallelic polymorphism in TNF (−238 and −308) seems not to be associated with SVR.^[124] TGFβ1 and interleucin-10 polymorphisms have been strongly related to achieving SVR. Genotype −29 C/C in TGFβ1 promotes resistance to Peg-IFN/RBV treatment.^[125] Further, genotypes −592 A/A and −819 T/T in the IL10 gene have been found to be linked to higher SVR.^[126] Lastly, in a multivariate analysis of 105 patients treated with IFN/RBV, HLA class I B44 was seen to be independently associated with improved SVR to combined IFN/RBV, together with viral non-1 genotype. However, no association between this allele and SVR was detected in patients receiving IFN alone.^[127] In spite of these well-selected candidate genes, and some associations being confirmed in multivariate analysis, the majority of them showed minor impact on clinical practice, and they have not been included in the daily management of patients with CHC.

Several pharmacogenetic studies using GWAS for HCV treatment response assessment have demonstrated relationships between several polymorphisms in the 19q13 region and SVR (Table 2). Ge and colleagues conducted a GWAS analysis in 1137 patients of a cohort from the IDEAL study; a trial comparing Peg-IFN α-2b in two different doses (1.0 μg/kg/week vs. 1.5 μg/kg/week) versus standard doses of Peg-IFN α-2a. Using the Illumina Human 610® quad bead chip, the authors demonstrated that the probability of achieving SVR in patients bearing CC in the position rs12979860 in 19q13 region was double that of those with CT/TT (OR: 2; 95% CI: 1.8–2.3; P=1.37 × 10⁻²⁸). Moreover, the distributions of this CC genotype in several World populations were strongly related to SVR whether in Asians, Europeans, Hispanic or AAs. Tanaka et al.^[128] conducted a GWAS analysis in 154 Japanese patients; 82 non-responders and 72 with SVR using Affimetrix SNP 6.0® genome wide SNP typing array testing 621 220 SNPs. Several genetic signals in the 19q13 region were observed to be strongly related to SVR (rs12980275; P=1.93 × 10⁻¹³ and rs8099917; P=3.11 × 10⁻¹⁵). Sequencing a 40 kb region in the 19q13 region indicated that 7 SNPs were strongly related to each other, suggesting that the association with SVR was primarily driven by one or other of these SNPs. Further, using quantitative RT-PCR, IL28B mRNA was found to be higher in patients who were homozygous carriers of the major allele. Lastly, multivariate analysis indicated that rs8099917 (G allele) and female gender were independently associated with SVR while fibrosis, markers of liver dysfunction (such as platelets) or viral load were excluded from the final multivariate model. Suppiah et al.^[129] conducted a GWAS of SVR to Peg-IFN/RBV in 293 Australian patients with genotype 1, and a validation cohort of 555 individuals. An association between rs8099917 in the IL28B gene and SVR was observed (OR: 1.98, 95% CI: 1.57–2.52; P<0.05) confirming the previous data from Ge and colleagues. In a recent GWAS analysis, all these markers in the 19q13 region were found to be associated with SVR but, after multivariate analysis, rs12979860 was found to be independently associated with the chance of achieving a cure, and as such, supporting a major role for this genetic signal.^[130] These results have since been confirmed in different series such as those of McCarthy et al.,^[131] Del Campo et al.,^[132] Montes-Cano et al.^[55] and Rauch et al..^[56] Further, this polymorphism has also been strongly associated with the possibility of achieving SVR in patients infected by genotype 1, without rapid virological response (RVR) (clearance of the virus after 4 weeks of treatment).^[133] Conversely, in patients with CHC non-1 genotype, IL28B polymorphism rs8099917 G was not associated with higher SVR. In 230 patients with genotype 2 or 3 receiving Peg-IFN/RBV, SVR was 79.5% in patients bearing the G allele vs 86.4% in patients with the T allele (OR: 1.62; 95% CI: 0.8–3.3; P=NS).^[58] Recently, Mangia et al.^[134] confirmed the usefulness of genotype CC in predicting SVR in patients with genotype 2/3 without RVR, but not in the overall cohort (Table 3).

Further, favourable polymorphism rs12979860 was more often seen in genotype 2, 3 than genotype 1 (Fig. 1) suggesting that IL28B polymorphism not only strongly influences SVR but also appears to explain much of the difference in response observed between population groups representing different viral genotypes and host ethnicity.^[57] A meta-analysis including all these studies was conducted (Fig. 2) and all studies confirmed the association between genotype CC rs12987960 and SVR (OR: 4.5; 95% CI: 2.8–7.3). In a multivariate analysis of HCV-4 patients, baseline viral load, fibrosis and the IL28 T allele (OR: 0.124, 95% CI: 0.030–0.505) were significantly associated with SVR.^[106] The strongest predictor for the final outcome was RVR (OR: 26.00; 95% CI: 7.148–94.545, P<0.0001). If RVR was included in the multivariate model, only the RVR and the fibrosis score remained significant. Thus, determination of IL28 polymorphism may not be useful to select patients with HCV-4 for abbreviated treatment schedules.^[135] IFN-λ is transduced from IL28B gene (19q13). It binds to the heterodimer receptor complex composed of 2 subunits IFN-λ-R1 and IL10-R2. After receptor binding, IFN-λ has been observed to promote the JAK-STAT antiviral pathway, inducing phosphorylation of Janus-kinase1 and tyrosine-kinase2, promoting signal transducer and activating transcription (STAT) phosphorylation. After coupling with IFN, regulatory factor 9 translocates to the nucleus and binds the IFN-stimulated response element in DNA to initiate gene t

ranscription, mainly IFN-stimulated genes including 5'2'OAS, protein-tyrosine-kinase, IL8 and IFN-regulated factor-7. Thus, IFN-l, seems to be able to: (a) inhibit HCV replication; (b) down-regulate HLA-I presentation; (c) inhibit the entry of HCV particles into the ribosome; (d) have better haematological tolerance than IFN alpha, mainly because of reduced expression of IFN-λ-R1 receptor in blood cells. Nevertheless, the specific genetic variant involved remains to be determined, together with the amino acid change that could promote the protein. Indeed, antiviral activity of IFN-λ3 varies depending on the final amino acid change: Val97 to Ala increases 68-fold the antiviral activity, while Arg51 to Ala does not modify it.^[136]

Figure 1.

Genotype CC (rs12979860) distribution in spontaneous viral clearance (n=69), healthy individuals (n=1169) and chronic hepatitis C (n=524) in Spain.

Figure 2.

Estimated impact of rs12979860 genotype on the possibility of achieving sustained virological response in patients infected by genotype 1. Note: Half of the responders are non-CC genotype.
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Overall, these hypothesis-free investigations using massive, and parallel, genotypic research technologies such as DNA arrays, together with independently reproduced studies, have provided incontrovertible evidence that genomic variation in or around the IL28B locus is directly related to SVR induced by Peg-IFN/RBV therapy. The genetic variant (or variants) needs to be described, as are the functional analyses to demonstrate the impact of these changes on the host's ability to clear viral infection. Further, the role of the genetic alteration in patients infected by non-1 genotypes should be demonstrated; IL28 polymorphism may not be useful in selecting patients with HCV-4 for abbreviated treatment schedules. However, these data need further confirmation before final conclusions can be formulated. Lastly, from a clinical point of view, approximately a third of patients bearing the CC genotype (rs12979860) did not achieve SVR, while on the other hand, nearly a half of CT heterozygous and a third of TT homozygous individuals could achieve SVR when treated with Peg-IFN/RBV. Thus, genetic factors that could modulate (positively or negatively) the effect of this polymorphism on SVR warrant further exploration. Perhaps other genetic markers could explain this gap between IL28B genotype and SVR^[137] (Fig. 3).

Click Figure To Enlarge

Figure 3.

Meta-analysis showing a strong association between sustained virological response and genotype CC (rs 12987960).
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In conclusion, the lessons from the review of these studies are: (a) there are innate host characteristics that model HCV lifecycle and infection resolution; (b) the investigation of SVR and SVC are fully complementary. This means that host genetic factors might behave similarly by simultaneously affecting both phenotypes; (c) together with HLA region studies and meta-analyses related to them, GWAS have provided overwhelming evidence indicating that the host immunological hypothesis for virus clearance is plausible; (d) the isolation of this unanticipated factor (IL28B) will provide new research opportunities and will have a considerable clinical impact on HCV diagnosis, prognosis and therapy in these patients; (e) based on genetic studies, spontaneous clearance or fibrosis progression can be considered as complex phenotypes. This assumption implies that many other genetic and non-genetic factors need to be identified, using candidate gene or hypothesis-free approaches. To achieve these goals, genetic markers need to be studied in large cohorts of patients, keeping in mind their interaction with environmental and viral factors, which could affect the natural history of CHC. We feel that genetic studies are opening up a new era in HCV investigation. However, despite recent successes, there is still a considerable gap between genetic discoveries in the laboratory and application of the findings to innovative clinical practice; fortunately, it is a gap that is continuing to close.

• References
• http://www.medscape.com/viewarticle/740571_2

Nanozyme shuts down production of hepatitis C virus in the body

UF researchers develop “nanorobot” that can be programmed to target different diseases

Monday, July 16, 2012.

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GAINESVILLE, Fla. — University of Florida researchers have moved a step closer to treating diseases on a cellular level by creating a tiny particle that can be programmed to shut down the genetic production line that cranks out disease-related proteins.

In laboratory tests, these newly created “nanorobots” all but eradicated hepatitis C virus infection. The programmable nature of the particle makes it potentially useful against diseases such as cancer and other viral infections.

The research effort, led by Y. Charles Cao, a UF associate professor of chemistry, and Dr. Chen Liu, a professor of pathology and endowed chair in gastrointestinal and liver research in the UF College of Medicine, is described online this week in the Proceedings of the National Academy of Sciences.

“This is a novel technology that may have broad application because it can target essentially any gene we want,” Liu said. “This opens the door to new fields so we can test many other things. We’re excited about it.”

During the past five decades, nanoparticles — particles so small that tens of thousands of them can fit on the head of a pin — have emerged as a viable foundation for new ways to diagnose, monitor and treat disease. Nanoparticle-based technologies are already in use in medical settings, such as in genetic testing and for pinpointing genetic markers of disease. And several related therapies are at varying stages of clinical trial.

The Holy Grail of nanotherapy is an agent so exquisitely selective that it enters only diseased cells, targets only the specified disease process within those cells and leaves healthy cells unharmed.

To demonstrate how this can work, Cao and colleagues, with funding from the National Institutes of Health, the Office of Naval Research and the UF Research Opportunity Seed Fund, created and tested a particle that targets hepatitis C virus in the liver and prevents the virus from making copies of itself.
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Hepatitis C infection causes liver inflammation, which can eventually lead to scarring and cirrhosis.

The disease is transmitted via contact with infected blood, most commonly through injection drug use, needlestick injuries in medical settings, and birth to an infected mother. More than 3 million people in the United States are infected and about 17,000 new cases are diagnosed each year, according to the Centers for Disease Control and Prevention. Patients can go many years without symptoms, which can include nausea, fatigue and abdominal discomfort.

Current hepatitis C treatments involve the use of drugs that attack the replication machinery of the virus. But the therapies are only partially effective, on average helping less than 50 percent of patients, according to studies published in The New England Journal of Medicine and other journals. Side effects vary widely from one medication to another, and can include flu-like symptoms, anemia and anxiety.

Cao and colleagues, including graduate student Soon Hye Yang and postdoctoral associates Zhongliang Wang, Hongyan Liu and Tie Wang, wanted to improve on the concept of interfering with the viral genetic material in a way that boosted therapy effectiveness and reduced side effects.

The particle they created can be tailored to match the genetic material of the desired target of attack, and to sneak into cells unnoticed by the body’s innate defense mechanisms.

Recognition of genetic material from potentially harmful sources is the basis of important treatments for a number of diseases, including cancer, that are linked to the production of detrimental proteins. It also has potential for use in detecting and destroying viruses used as bioweapons.

The new virus-destroyer, called a nanozyme, has a backbone of tiny gold particles and a surface with two main biological components. The first biological portion is a type of protein called an enzyme that can destroy the genetic recipe-carrier, called mRNA, for making the disease-related protein in question. The other component is a large molecule called a DNA oligonucleotide that recognizes the genetic material of the target to be destroyed and instructs its neighbor, the enzyme, to carry out the deed. By itself, the enzyme does not selectively attack hepatitis C, but the combo does the trick.
“They completely change their properties,” Cao said.

In laboratory tests, the treatment led to almost a 100 percent decrease in hepatitis C virus levels. In addition, it did not trigger the body’s defense mechanism, and that reduced the chance of side effects. Still, additional testing is needed to determine the safety of the approach.

Future therapies could potentially be in pill form.

“We can effectively stop hepatitis C infection if this technology can be further developed for clinical use,” said Liu, who is a member of The UF Shands Cancer Center.

The UF nanoparticle design takes inspiration from the Nobel prize-winning discovery of a process in the body in which one part of a two-component complex destroys the genetic instructions for manufacturing protein, and the other part serves to hold off the body’s immune system attacks. This complex controls many naturally occurring processes in the body, so drugs that imitate it have the potential to hijack the production of proteins needed for normal function. The UF-developed therapy tricks the body into accepting it as part of the normal processes, but does not interfere with those processes.

“They’ve developed a nanoparticle that mimics a complex biological machine — that’s quite a powerful thing,” said nanoparticle expert Dr. C. Shad Thaxton, an assistant professor of urology at the Feinberg School of Medicine at Northwestern University and co-founder of the biotechnology company AuraSense LLC, who was not involved in the UF study. “The promise of nanotechnology is extraordinary. It will have a real and significant impact on how we practice medicine.”

http://news.ufl.edu/2012/07/16/nanobot/

Personalized medicine : Genetic makeup to predict the risk of disease or response to a drug

Description
Personalized medicine centers on being able to predict the risk of disease or response to a drug based on a person’s genetic makeup. But a study by scientists at Washington University School of Medicine in St. Louis suggests that, for most common diseases, genes alone only tell part of the story. Their research shows the environment interacts with DNA in ways that are difficult to predict, even in simple organisms like single-celled yeast.

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“Measuring the environment becomes crucial when we try to understand how it interacts with genetics,” Cohen says. “Having a particular genetic variant may not have much of an effect but combined with a person’s environment, it may have a huge effect.”

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The Gene-Environment Enigma
Released: 12/3/2010 9:00 AM EST Source: Washington University in St. Louis
Newswise — Personalized medicine centers on being able to predict the risk of disease or response to a drug based on a person’s genetic makeup. But a study by scientists at Washington University School of Medicine in St. Louis suggests that, for most common diseases, genes alone only tell part of the story.

That’s because the environment interacts with DNA in ways that are difficult to predict, even in simple organisms like single-celled yeast, their research shows.
“The effects of a person’s genes – and, therefore, their risk of disease – are greatly influenced by their environment,” says senior author Barak Cohen, PhD, a geneticist at Washington University School of Medicine. “So, if personalized medicine is going to work, we need to find a way to measure a human’s environment.”

The research is available online in PLoS Genetics.
To understand gene-environment interactions at the most basic level – at the individual DNA letters that make up the genetic code – the researchers turned to a model organism, the yeast Saccharomyces cerevisiae, culled from North American oak trees and vineyards, where it grows naturally. They asked whether growing the yeast in different environments would influence the rate at which the yeast produce spores, a form of sexual reproduction.
This complex trait is heavily influenced by genetics, Cohen’s earlier research has shown. In a study published in 2009 in Science, he determined that just four DNA variants, called single nucleotide polymorphisms (SNPs), account for 90 percent of the efficiency with which yeast produce spores.

In that study, the researchers noted that yeast from oak trees produced spores with 99 percent efficiency; the vineyard strains were far less efficient, at 7 percent. Then, they put each combination of the four SNPs in both the oak and vineyard strains, to determine how the genetic variants interacted with one another.
The researchers showed that the four variants “interacted like crazy such that the combined effects of any four variants were always larger than the sum of their individual effects,” Cohen says.

By developing a statistical model to account for the genetic interactions, they could genotype any combination of the four SNPs in either strain of yeast and predict with a high level of confidence their effect on sporulation.

But in that study, the yeast were grown in the same environment – glucose.
In the current study, the scientists grew the two yeast strains with all 16 combinations of four SNPs in different simple sugars: glucose, fructose, sucrose, maltose, raffinose, grape juice, galactose and a combination of sucrose, glucose and fructose.
“These were all mono- or di-saccharides, so the environments are not radically different from one another,” Cohen explains. “It’s not like we heated up the yeast or froze them, added acids or put them in a centrifuge. We simply changed the carbon source and measured the effects of those four SNPs in the different environments.”
Surprisingly, the researchers found that the effects of the four SNPs on spore production were dramatically different in the different environments. The effects of different combinations of SNPs in one environment were not an accurate predictor of the effects of those same SNPs in other environments.

For example, one combination of the four SNPs increased sporulation efficiency by 40 percent in glucose, but that same SNP combination increased efficiency by 80 percent when the yeast were grown in raffinose.

Indeed, the relative importance of particular SNPs and their interactions were not constant but varied with the genetic background of the yeast strain and the environment.
“Having a particular combination of SNPs was never a great predictor,” Cohen says. “If we didn’t know the environment in which the yeast were grown, we could not accurately predict the effect of the SNPs on producing spores. And if we can’t make accurate predictions about the way environment influences complex traits in yeast, then it will be exceedingly difficult to do so in people.”

The new research raises many questions: what is a human’s environment and how can it be measured? Is the environment a person lived in during childhood important or the environment he lives in now?

Cohen suspects that any environment that matters is likely to leave a measurable molecular signature. For example, eating a lot of fatty foods raises triglycerides; smoking raises nicotine levels; and eating high-fat, high-sugar foods raises blood sugar levels, which increases the risk of diabetes. The key, he says, is to figure out what are good metabolic readouts of the environment and factor those into statistical models that assess genetic susceptibility to disease or response to medication.

“Measuring the environment becomes crucial when we try to understand how it interacts with genetics,” Cohen says. “Having a particular genetic variant may not have much of an effect but combined with a person’s environment, it may have a huge effect.”
Cohen says he’s not hopeless when it comes to personalized medicine. As scientists conduct ever-larger studies to identify rare and common variants underlying diseases such as cancer, diabetes and schizophrenia, they will be more likely to uncover variants that have larger effects on disease. Even then, however, a person’s environment will be important, he adds.
The research was funded by the National Science Foundation.
Gerke J, Lorenz K, Ramnarine S, Cohen B. Gene-environment interactions at nucleotide resolution. Sept. 2010. PLoS Genetics.

Washington University School of Medicine's 2,100 employed and volunteer faculty physicians also are the medical staff of Barnes-Jewish and St. Louis Children's hospitals. The School of Medicine is one of the leading medical research, teaching and patient care institutions in the nation, currently ranked fourth in the nation by U.S. News & World Report. Through its affiliations with Barnes-Jewish and St. Louis Children's hospitals, the School of Medicine is linked to BJC HealthCare.