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How to Write a Research Paper

Writing a research paper is a bit more difficult that a standard high school essay. You need to site sources, use academic data and show scientific examples. Before beginning, you’ll need guidelines for how to write a research paper.

Start the Research Process

Before you begin writing the research paper, you must do your research. It is important that you understand the subject matter, formulate the ideas of your paper, create your thesis statement and learn how to speak about your given topic in an authoritative manner. You’ll be looking through online databases, encyclopedias, almanacs, periodicals, books, newspapers, government publications, reports, guides and scholarly resources. Take notes as you discover new information about your given topic. Also keep track of the references you use so you can build your bibliography later and cite your resources.

Develop Your Thesis Statement

When organizing your research paper, the thesis statement is where you explain to your readers what they can expect, present your claims, answer any questions that you were asked or explain your interpretation of the subject matter you’re researching. Therefore, the thesis statement must be strong and easy to understand. Your thesis statement must also be precise. It should answer the question you were assigned, and there should be an opportunity for your position to be opposed or disputed. The body of your manuscript should support your thesis, and it should be more than a generic fact.

Create an Outline

Many professors require outlines during the research paper writing process. You’ll find that they want outlines set up with a title page, abstract, introduction, research paper body and reference section. The title page is typically made up of the student’s name, the name of the college, the name of the class and the date of the paper. The abstract is a summary of the paper. An introduction typically consists of one or two pages and comments on the subject matter of the research paper. In the body of the research paper, you’ll be breaking it down into materials and methods, results and discussions. Your references are in your bibliography. Use a research paper example to help you with your outline if necessary.

Organize Your Notes

When writing your first draft, you’re going to have to work on organizing your notes first. During this process, you’ll be deciding which references you’ll be putting in your bibliography and which will work best as in-text citations. You’ll be working on this more as you develop your working drafts and look at more white paper examples to help guide you through the process.

Write Your Final Draft

After you’ve written a first and second draft and received corrections from your professor, it’s time to write your final copy. By now, you should have seen an example of a research paper layout and know how to put your paper together. You’ll have your title page, abstract, introduction, thesis statement, in-text citations, footnotes and bibliography complete. Be sure to check with your professor to ensure if you’re writing in APA style, or if you’re using another style guide.


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research paper on genetics

research paper on genetics

Frontiers in Genetics

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Enrico  Domenici

enrico domenici

Department of Cellular, Computational and Integrated Biology, University of Trento

Povo , Italy

Field Chief Editor

José A G Agúndez

josé a g agúndez

Institute of Molecular Pathology Biomarkers, University of Extremadura

Cáceres , Spain

Specialty Chief Editor

Pharmacogenetics and Pharmacogenomics

Atsushi  Asakura

atsushi asakura

University of Minnesota Twin Cities

St. Paul , United States

Stem Cell Research

John R Battista

john r battista

Louisiana State University

Baton Rouge , United States

Evolutionary and Genomic Microbiology

Original Research

Accepted on 03 Mar 2023

Using the Multi-Tissue Transcriptome-Wide Association Study Identifies Candidate Susceptibility Genes for Respiratory Infectious Diseases

in Genetics of Common and Rare Diseases

Editorial: Contribution of Omics to our Understanding of Human-Bacterial Interplay in Health and Disease

in Evolutionary and Genomic Microbiology

Data Report

The tissue-specific chromatin accessibility landscape of Papaver somniferum

in Plant Genomics

High GTSE1 expression promotes cell proliferation, metastasis and cisplatin resistance in ccRCC and is associated with immune infiltrates and poor prognosis

in Cancer Genetics and Oncogenomics

Research Topics

Submission open

Prognosis and Diagnosis of Hepatocellular Carcinoma

research paper on genetics

Neuroepigenomics Pathogenesis in Major Depressive Disorder

research paper on genetics

The Links Between Microbe, Metabolism, Microenvironment, and Cancer

A Year in Review: Discussions in Human and Medical Genomics

Genetics Research

Genetics Research

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Journal profile

Genetics Research is a fully open access journal providing a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations, developmental, evolutionary, and population genetics as well as ethical, legal and social aspects.

Editor spotlight

Genetics Research maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.  

Special Issues

Latest articles, mir-19-3p targets pten to regulate cervical cancer cell proliferation, invasion, and autophagy.

Background . Cervical cancer is the second most common cancer among women worldwide. Extensive studies have shown that microRNAs (miRNA/miR) can regulate the formation, progression, and metastasis of cancer. The purpose of this study was to investigate the effect of miR-19-3p on the proliferation, invasion, and autophagy of cervical cancer cells and to explore the underlying mechanism. Methods . SiHa and HeLa cells were transfected with miR-19-3p mimic and inhibitor. miR-19-3p and PTEN expression were detected using real-time quantitative PCR and western blot, respectively. The binding between miR-19-3p and PTEN was predicted using Targetscan7.2 and verified by a dual-luciferase reporter gene assay. The effects of miR-19-3p on cell invasion and proliferation were evaluated by Transwell assays and MTT, respectively. The effect of miR-19-3p on autophagy was observed using fluorescence microscopy. Results . The expression of miR-19-3p in cervical cancer tissues and SiHa and HeLa cells was significantly upregulated, whereas the expression of PTEN was significantly downregulated. PTEN was one of the direct targets of miR-19-3p. The miR-19-3p mimic significantly reduced the apoptosis rate and autophagy and promoted cell proliferation and invasion of the SiHa and HeLa cells. Conclusion . In summary, miR-19b-3p can target PTEN to regulate the proliferation, invasion, and autophagy of cervical cancer cells. Our findings indicate the potential of miR-19-3p as a target for cervical cancer treatment in the future.

LncRNA MBNL1-AS1 Suppresses Cell Proliferation and Metastasis of Pancreatic Adenocarcinoma through Targeting Carcinogenic miR-301b-3p

Pancreatic adenocarcinoma (PAAD) has been a huge challenge to public health due to its increasing incidence, frequent early metastasis, and poor outcome. The molecular basis of tumorigenesis and metastasis in PAAD is largely unclear. Here, we identified a novel tumor-suppressor long noncoding RNA (lncRNA) MBNL1-AS1, in PAAD and revealed its downstream mechanism. Quantitative real-time PCR (qRT-PCR) data showed that MBNL1-AS1 expression was significantly downregulated in PAAD tissues and cells, which was closely associated with metastasis and poor prognosis. Cell counting kit-8 (CCK-8) assay, transwell assay, and western blot verified that overexpression of MBNL1-AS1 suppressed cell proliferation, migration, and epithelial mesenchymal transformation (EMT) behavior in PAAD cells. By using a dual luciferase reporter gene system, we confirmed that miR-301b-3p was a direct target of MBNL1-AS1. Further mechanismic study revealed that upregulation of miR-301b-3p abolished the inhibitory effect of MBNL1-AS1 overexpression on cell proliferation, tumorigenesis, migration and EMT. Our results demonstrate that MBNL1-AS1 plays a tumor-suppressive role in PAAD mainly by downregulating miR-301b-3p, providing a novel therapeutic target for PAAD.

A Survival Model Based on the ASB Genes and Used to Predict the Prognosis of Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is increasing in incidence worldwide, with poor and unpredictable patient prognosis limited by diagnostic and therapeutic approaches. New genes are urgently needed to improve this situation. The ankyrin repeat and suppressor of the cytokine signaling (SOCS) box (ASB) family are a promising class of tumorigenesis-related genes. We examined the expression and mutation of 18 ASB genes in various tumors for this study. The findings revealed that ASB genes exhibit significant copy number variation (CNV) and single nucleotide variation (SNV). There were substantial variations in ASB gene expression in different tumor tissues, and different levels of methylation of ASB genes affected the gene expression and tumor progression. By applying LASSO regression analysis, we established a KIRC survival model based on five ASB genes (ASB6, ASB7, ASB8, ASB13, and ASB17). Additionally, ROC curve analysis was used to assess the survival model’s accuracy. Through univariate and multivariate COX regression analysis, we demonstrated that the model’s risk score might be an independent risk factor for individuals with KIRC. In summary, our KIRC survival model could accurately predict patients’ future survival. Further, we also quantified the survival model through a nomogram. This series of findings confirmed that ASB genes are potential predictive markers and targeted therapies for KIRC. Our KIRC survival model based on five ASB genes can help more clinical practitioners make accurate judgments about the prognosis of KIRC patients.

Association of Androgen-Receptor Gene Mutations with the Copy Number of Androgen-Receptor Silk Protein A Complex and Glutathione-S-Transferases T1 and M1 in Prostate Cancer Patients

Objective . The purpose of our work was to explore the association of mutations in the androgen receptor gene and copy numbers of the androgen-receptor silk protein A complex with glutathione-S-transferases T1 and M1 in prostate cancer patients. Materials and Methods . Eighty-five patients with PC and 85 healthy controls were included in the study. Fasting peripheral venous blood was collected, whole blood genomic DNA was extracted, and AR gene-receptor genotype was detected by a high-resolution melting curve analysis detection technology. Expression levels of androgen receptor (AR) and filamin protein A (FlnA) were detected by Western blotting. RT-PCR was used to detect the copy number of T1 and M1 glutathione-S-transferases. Results . The wild-type androgen receptor gene rs5918762 is of TT type. The frequencies of CC and TC genes in the prostate cancer group were significantly higher than those in the normal control group . Compared with TT-type PC patients, PC patients with TC-type and CC-type had higher expression levels of sex hormone receptor silk protein A complex and higher copy numbers of GSTT1 and GSTM1 . Androgen-receptor gene mutation (T ⟶ C) was significantly positively correlated with the expression level of androgen-receptor silk protein A complex and the copy number of GSTT1 and GSTM1. Conclusion . Androgen-receptor gene polymorphisms were significantly associated with expression levels of androgen receptor complex A and silk proteins, and copy numbers of T1 and M1 glutathione-S-transferases. A combination of four factors can be used to identify prostate cancer susceptibility and disease progression.

Allelic and Genotype Frequencies of CYP2B6 ∗ 2 (64C > T) and CYP2B6 ∗ 3 (777C > A) in Three Dominant Ethnicities of the Iranian Population

Background . Cytochrome P450 complex plays a key role in drug metabolism. CYP2B6 has an essential part in Cytochrome P450 complex metabolism. This study aims to determine the allelic distribution of CYP2B6 ∗ 2 and CYP2B6 ∗ 3 in three main Iranian ethnicities: Fars, Turk, and Kurd. Methods . The study was conducted on 174 unrelated healthy volunteers from three main Iranian ethnicities. After DNA extraction from peripheral blood samples, genotyping of CYP2B6 ∗ 2 and ∗ 3 was performed using tetra ARMS and ARMS PCR, respectively. Results . The average age of 174 cases was 40.69 11.87 (mean SD) and 39.06 11.63 (mean SD) for males and females. In the CYP2B6 ∗ 2 variant, the genotyping frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 8.7%, 86%, and 5.2%, respectively. The CYP2B6 ∗ 2 (c.64C > T) allele frequency was 48.2% (95% CI: (37.8–58.6)). In the CYP2B6 ∗ 3 variant, the frequency of wild type (C/C), heterozygous (C/T), and homozygous mutant (T/T) was 75.3%, 11%, and 13.6%, respectively. The CYP2B6 ∗ 3 (c.777C > A) allelic frequency was 19.1% (95% CI: (17.5–20.7)). Conclusion . Allelic distribution in three main Iranian ethnicities, i.e., Turk, Kurd, and Fars, is remarkably higher than that in other populations, even that in Southern Iran. High frequencies of CYP2B6 ∗ 2 and ∗ 3 in the Iranian population highly affect drug responsiveness. Understanding such variability could help to increase drug efficacy and reduce its toxicity.

Establishment of a Lymph Node Metastasis-Associated Prognostic Signature for Lung Adenocarcinoma

Background . Lung adenocarcinoma (LUAD) is the most common histological subtype of non-small cell lung cancer (NSCLC) with a low 5-year survival rate, which may be associated with the presence of metastatic tumors at the time of diagnosis, especially lymph node metastasis (LNM). This study aimed to construct a LNM-related gene signature for predicting the prognosis of patients with LUAD. Methods . RNA sequencing data and clinical information of LUAD patients were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Samples were divided into metastasis (M) and nonmetastasis (NM) groups based on LNM status. Differentially expressed genes (DEGs) between M and NM groups were screened, and then WGCNA was applied to identify key genes. Furthermore, univariate Cox and LASSO regression analyses were conducted to construct a risk score model, and the predictive performance of model was validated by GSE68465, GSE42127, and GSE50081. The protein and mRNA expression level of LNM-associated genes were detected by human protein atlas (HPA) and GSE68465. Results . A prognostic model based on eight LNM-related genes (ANGPTL4, BARX2, GPR98, KRT6A, PTPRH, RGS20, TCN1, and TNS4) was developed. Patients in the high-risk group had poorer overall survival than those in the low-risk group, and validation analysis showed that this model had potential predictive value for patients with LUAD. HPA analysis supported the upregulation of ANGPTL4, KRT6A, BARX2, RGS20 and the downregulation of GPR98 in LUAD compared with normal tissues. Conclusion . Our results indicated that the eight LNM-related genes signature had potential value in the prognosis of patients with LUAD, which may have important practical implications.

Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors.  Read the winning articles .

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Genetic Research

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) was founded 40 years ago to help elucidate the biological underpinnings of alcohol dependence, including the potential contribution of genetic factors. Twin, adoption, and family studies conclusively demonstrated that genetic factors account for 50 to 60 percent of the variance in risk for developing alcoholism. Case–control studies and linkage analyses have helped identify DNA variants that contribute to increased risk, and the NIAAA-sponsored Collaborative Studies on Genetics of Alcoholism (COGA) has the expressed goal of identifying contributing genes using state-of-the-art genetic technologies. These efforts have ascertained several genes that may contribute to an increased risk of alcoholism, including certain variants encoding alcohol-metabolizing enzymes and neurotransmitter receptors. Genome-wide association studies allowing the analysis of millions of genetic markers located throughout the genome will enable discovery of further candidate genes. In addition to these human studies, genetic animal models of alcohol’s effects and alcohol use have greatly advanced our understanding of the genetic basis of alcoholism, resulting in the identification of quantitative trait loci and allowing for targeted manipulation of candidate genes. Novel research approaches—for example, into epigenetic mechanisms of gene regulation—also are under way and undoubtedly will further clarify the genetic basis of alcoholism.

Evidence from archeological artifacts indicates that fermented beverages existed as early as 10,000 B.C. The excessive consumption of alcohol, however, results in dangers to the health and well being of the drinker and those around him or her. Today, the World Health Organization estimates that alcohol causes 1.8 million deaths (3.2 percent of all deaths) worldwide and 58.3 million (4 percent of total) disability-adjusted life-years (DALYs) 1 lost to disease ( ). In the United States, alcohol dependence (i.e., alcoholism) is a major health problem, affecting 4 to 5 percent of the population at any given time, with a lifetime prevalence of 12.5 percent ( Hasin et al. 2007 ).

The National Institute on Alcohol Abuse and Alcoholism (NIAAA) was founded 40 years ago to further understanding of the biological underpinnings of alcohol dependence. Early genetic studies were focused on delineating whether environmental factors, genetic factors, or both contributed to the risk for alcohol dependence. Once it was apparent that genetics did indeed play a role in alcohol dependence, NIAAA began to fund studies seeking to identify relevant genes. Since then, studies in humans and animals have used complementary approaches to understand the genetics of alcohol use and dependence. This overview summarizes the evidence supporting a role for genetic factors in alcoholism and describes how new genetic findings could affect our understanding of the causes and factors contributing to this debilitating disease and could potentially guide the development of improved treatments.

Evidence of a Genetic Contribution to Alcohol Dependence

Several study designs, including twin, family, and adoption studies, are used to determine whether relatively common diseases, such as alcohol dependence, are caused at least in part by genetic factors and to estimate the magnitude of the overall genetic contribution. Twin studies compare the similarity in disease status (i.e., concordance 2 ) between identical (i.e., monozygotic) and fraternal (i.e., dizygotic) twins. If risk for a disease (e.g., alcohol dependence) is determined at least in part by genetic factors, monozygotic twins, who have identical genetic material (i.e., genomes), would be expected to have a higher concordance rate for alcohol dependence than dizygotic twins, who on average share only half their genome. Studies by several groups have indeed shown higher concordance rates for alcohol dependence among monozygotic than among dizygotic twins ( Agrawal and Lynskey 2008 ). Family studies, which evaluate the members of a family (both alcoholic and nonalcoholic members) for the presence of the disease, also have provided convincing evidence that the risk for alcohol dependence is determined partly by genetic influences ( Gelernter and Kranzler 2009 ). Overall, family, adoption, 3 and twin studies provide convergent evidence that hereditary factors play a role in alcohol dependence, with variations in genes estimated to account for 50 to 60 percent of the total variance in risk. These estimates suggest that although genetic factors are important, nongenetic factors also contribute significantly to the risk for alcohol dependence.

Strategies for Identifying Genes Contributing to Alcohol Dependence

Researchers have developed several strategies to identify genes that contribute to differences in the risk for alcohol dependence, including case–control studies and linkage analyses. These strategies depend on the premise that for a particular position in the DNA of these genes, more than one possible form exists. Each of these forms is termed an allele. The study methods used to identify genes that affect the risk for alcohol dependence assume that the presence of certain alleles increases the risk of alcoholism. These variants that affect risk can be located either directly within a gene or near a gene.

Case–control studies compare allele frequencies in a sample of alcoholic and control subjects. Because DNA is inherited from both parents, every person carries two copies of the DNA at a given position in the genome—one allele that was inherited from the father and one allele that was inherited from the mother. The genotype describes the variation at a particular position within the genome and is defined by the allele inherited from the father and the allele inherited from the mother. If a given allele contributed to the risk for alcohol dependence, one would expect the allele and/or genotype frequencies to differ between the case and the control subjects (see figure 1A ).

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Approaches to identifying genes contributing to the risk of alcoholism. A) Case–control association study design. Each circle represents a person who is either an alcoholic (case subject) or not an alcoholic (control subject). The study assesses the role of a single-nucleotide polymorphism (SNP)* that exists in two different variants (i.e., alleles)—allele 1 and allele 2. Because each person inherits two copies of the SNP from their parents, the numbers in the circles represent the three possible genotypes (11, 12, and 22). Many more case than control subjects carry at least one copy of allele 1 (i.e., have the 11 and 12 genotypes), suggesting that people with allele 1 may be more likely to develop alcoholism. B) Linkage study design. A three-generation family tree (pedigree) is shown. Squares represent male subjects and circles represent female subjects. Shaded symbols represent alcoholic individuals and unshaded symbols represent nonalcoholic individuals. In this pedigree, there are alcoholic individuals in each generation, and both men and women are affected.

NOTE: *An SNP is a DNA sequence variation occurring when a single nucleotide in a DNA marker (or other genetic sequence) differs between members of a species or between the chromosome pairs in an individual.

Initially, case–control studies often were performed using small numbers of alcoholic and control subjects and examined the role of a single gene, frequently testing only for a single variation. This approach has limited power, and many results could not be replicated. The most robust result from these early studies was the demonstration that the genes encoding two alcohol-metabolizing enzymes—alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH)—played an important role in determining alcoholism risk (this will be discussed in more detail in the next section).

With the advances of molecular genetics technologies, it then became possible to scan the genome using a type of genetic variation called microsatellites. In this approach, called linkage analysis, the pattern of transmission of a disease (e.g., alcoholism) in families with multiple affected members is compared with the pattern of transmission of certain microsatellites (see figure 1B ). The underlying hypothesis is that alcoholics within a family share many risk alleles; therefore, genes containing alleles that increase the risk for alcoholism reside within chromosomal regions that are inherited by most or all alcoholic family members. Unfortunately, however, the chromosomal regions that were identified using this approach often contained hundreds or even thousands of genes, making it very challenging to determine which specific gene(s) contribute to the risk for alcoholism.

The Collaborative Studies on Genetics of Alcoholism Study

Another major advancement in the search for genes contributing to the risk for alcoholism was the initiation in 1989 of the NIAAA-funded Collaborative Studies on Genetics of Alcoholism (COGA), a family study with the expressed goal of identifying contributing genes using newly available genetic technologies ( Begleiter et al. 1995 ; Bierut et al. 2002 ; Edenberg 2002 ). The study was groundbreaking in several ways, including its size, emphasis on families, and extensive characterization of subjects. In the process, COGA researchers developed a novel assessment instrument, the Semi-Structured Assessment of the Genetics of Alcoholism (SSAGA), which since has been translated into nine languages and is used by over 237 investigators worldwide in studies of alcohol use and dependence.

Families were obtained by recruiting alcohol-dependent probands (i.e., index cases) who were in treatment and who gave permission to contact their family members. This approach generated a dataset of 1,857 families consisting of 16,062 individuals as of March 2010. Moreover, the researchers identified a genetically informative subset comprising 262 families with at least three first-degree relatives who met lifetime criteria for both Diagnostic and Statistical Manual of Mental Disorders, Third Edition, Revised (DSM–III–R) ( American Psychiatric Association 1987 ) alcohol dependence and Feighner definite alcoholism; 4 this subset became the focus of genetic analyses. The extensive characterization of subjects also allowed analysis of the role of hereditary characteristics (i.e., endophenotypes) that often are associated with alcoholism but are not direct symptoms of alcoholism, such as certain electrophysiological traits, drug dependence, other related psychiatric conditions, and personality measures ( Edenberg 2002 ).

Genetic analyses in this subsample of the COGA dataset have implicated several different chromosomal regions as possibly containing one or more genes contributing to alcohol dependence; to related clinical characteristics (i.e., phenotypes) such as smoking, depression, suicidal behavior, conduct disorder, and the largest number of drinks within a 24-hour period; and to neurobiological endophenotypes such as event-related potentials and brain oscillations in electrophysiological activity ( Edenberg 2002 ; Edenberg and Foroud 2006 ). Despite much progress, however, identification of the specific genes contributing to these phenotypes remains a challenging task because they lie within broad linkage regions that often encompassed 10 to 30 million base pairs.

In addition to COGA, NIAAA has supported several other large family studies designed to identify genes contributing to the risk for alcohol dependence. These include a large study in Ireland that is recruiting siblings ( Kendler et al. 1996 ; Prescott et al. 2005 ), a family study of both alcohol dependence and alcohol-related endophenotypes (including electro-physiological measures, similar to COGA) ( Hill 1998 ), and a study of Mission Indian families ( Ehlers et al. 2004 ). Twin studies also have remained a focus of several NIAAA-funded research projects ( Jacob et al. 2001 ; Madden et al. 2000 ). Moreover, a study of offspring of alcoholic fathers has expanded into a longitudinal, multigenerational genetic study that is focused on better understanding the factors contributing to the initiation of alcohol use as well as the long-term risk for alcohol dependence ( Schuckit 1991 ). Finally, studies also have examined African-American alcohol-dependent families ascertained on the basis of cocaine or opioid dependence ( Gelernter and Kranzler 2009 ). Together, these approaches, although by no means completed, already have resulted in the identification of some genes that impact the risk for alcohol dependence. Some of these genes and the proteins they encode are discussed in the next section.

Genes Contributing to Alcohol Dependence

Genes encoding alcohol-metabolizing enzymes.

Classic studies, which have been replicated in many populations, have demonstrated that certain coding variations in two genes affecting alcohol metabolism have a strong protective effect—that is, they both substantially lower the risk for alcoholism. These variants affect a gene called ADH1B , which encodes a variant of ADH, and a gene called ALDH2 , which encodes a variant of ALDH ( Edenberg 2000 , 2007 ; Hurley et al. 2002 ) ( figure 2 ). The protective variant in the ALDH2 gene, known as ALDH2*2 , involves a point mutation that results in the exchange of the amino acid glutamate at position 487 of the ALDH protein for the amino acid lysine. This mutation acts in a nearly dominant manner to render the enzyme almost inactive: even people who inherit only one copy of ALDH2*2 and one “normal” copy of the gene (i.e., people who are heterozygous for this mutation) produce an ALDH enzyme with extremely low enzyme activity ( Crabb et al. 1989 ). As a result, these individuals exhibit highly elevated levels of acetaldehyde, which produces aversive reactions, including flushing, elevated heart rate (i.e., tachycardia), and nausea after consuming even a small amount of alcohol ( Eng et al. 2007 ). Similarly, coding variations in the ADH1B gene (called ADH1B*2 and ADH1B*3 ) that encode highly active enzymes which increase the rate at which acetaldehyde is produced also are strongly protective and reduce the risk for alcohol dependence ( Edenberg 2007 ; Thomasson et al. 1991 ).

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The main steps of alcohol metabolism. Alcohol first is metabolized to acetaldehyde by the enzyme alcohol dehydrogenase (ADH), which is encoded by several genes, each of which may exist in several variants (i.e., alleles). Certain alleles encode ADH molecules that result in the metabolism of alcohol (denoted by the red arrow above ADH). As a result, buildup of acetaldehyde occurs (denoted by the upward-pointing arrow), leading to such aversive effects as nausea, flushing, and accelerated heart beat (i.e., tachycardia). The acetaldehyde then is metabolized to acetate by the enzyme aldehyde dehydrogenase (ALDH), which also is encoded by several genes existing in different alleles. Certain alleles in the ALDH2 gene, which encodes a key ALDH enzyme, can result in very low activity of the enzyme (denoted by the black arrow with a red line through it), again causing acetaldehyde accumulation and the resulting aversive effects.

These gene variations have been selected for in different populations. For example, the ALDH2*2 variant is common only among people from east Asia, the ADH1B*2 variant is common among people from east Asia and the Middle East, and the ADH1B*3 variant is common in people from Africa ( Edenberg 2007 ; Eng et al. 2007 ; Li et al. 2007 , 2009 ). All of these variations have strikingly strong effects on risk; thus, in Asian populations, ALDH2*2 and ADH1B*2 each can lower risk by two- to sevenfold. No other known gene variations have such a strong effect on risk for alcoholism.

The influence of ADH variations on risk was further investigated through linkage studies performed in non-Asian families. These analyses detected linkage of alcoholism to a broad region on chromosome 4q that included the ADH gene cluster ( Long et al. 1998 ; Prescott et al. 2006 ; Reich 1996 ; Reich et al. 1998 ; Williams et al. 1999 ). Given the strong prior evidence for the role of the ADH genes in alcoholism susceptibility, the COGA investigators initially focused on the 262 families from the study with a very strong history of alcoholism. In these families, they determined the genotype for 110 DNA markers known as single-nucleotide polymorphisms (SNPs), which were distributed throughout the ADH gene cluster. These analyses detected significant evidence of association of alcoholism with 12 SNPs located in and around the ADH4 gene ( Edenberg et al. 2006 ) and modest evidence of association with noncoding SNPs 5 in ADH1A and ADH1B . Moreover, the analyses provided evidence that the ADH1B*3 allele was protective among African-American families ( Edenberg et al. 2006 ). The association of several noncoding ADH polymorphisms with alcohol dependence has been replicated in other studies ( Edenberg 2007 ; Macgregor et al. 2009 ).

Genes Encoding γ-Aminobutyric Acid Receptors

The brain-signaling molecule (i.e., neurotransmitter) γ-aminobutyric acid (GABA), by interacting with a molecule called the GABA-A receptor, mediates several effects of alcohol, including alcohol’s sedative and anxiety-reducing (i.e., anxiolytic) effects, motor incoordination, tolerance, and dependence ( Kumar et al. 2009 ). Several genes that encode subunits of the GABA-A receptor are associated with an increased risk for alcoholism. For example, significant evidence suggests that a gene called GABRA2 , which with other GABA-A receptor genes is located in a cluster on chromosome 4, is associated with alcoholism ( Edenberg et al. 2004 ). This finding has been replicated in many (but not all) case–control studies in Europeans, Australians, and Plains Indians ( Edenberg and Foroud 2006 ; Gelernter and Kranzler 2009 ). In several samples, the association with GABRA2 was greatest among those alcohol-dependent people who also were dependent on nicotine ( Philibert et al. 2009 ) or illicit drugs ( Agrawal et al. 2006 ; Philibert et al. 2009 ); the latter subgroup is characterized by greater severity of alcohol problems in general ( Dick et al. 2007 ). In addition, another gene within the chromosome 4 GABA-A cluster, GABRG1 , also may influence the risk for alcoholism ( Covault et al. 2008 ; Enoch et al. 2009 ).

Finally, GABA-A genes on other chromosomes, including GABRG3 on chromosome 15 ( Dick et al. 2004 ) and GABRA1 on chromosome 5 ( Dick et al. 2006 ), also have been associated with alcoholism. However, these associations have not yet been replicated in other samples and therefore must be considered tentative.

Genes Encoding Acetylcholine Receptors

Another neurotransmitter system involved in the actions of alcohol is acetylcholine, which can interact with different types of receptors, including muscarinic and nicotinic receptors. As with the GABA-A receptor, the subunits for each of these receptors are encoded by different genes that have several different alleles (i.e. code for different forms of the receptor subunit), and certain alleles have been associated with an increased risk for alcoholism. For example, the gene that encodes the muscarinic acetylcholine receptor subtype 2, called CHRM2 , appears to be an important risk factor for alcohol dependence. The receptor encoded by this gene is a G-protein–coupled receptor 6 involved in many functions. In the COGA study, SNPs in CHRM2 were associated with alcohol dependence, a finding that was replicated in an independent study ( Edenberg and Foroud 2006 ).

Extensive research also has examined the neuronal nicotinic acetylcholine receptors (nAChRs), which are affected by both nicotine and alcohol. DNA variation in the genes that encode the subunits of these receptors may play a role in the susceptibility to alcohol dependence and nicotine addiction. Similar to the GABA-A receptors, the genes encoding these receptors are found in clusters on several chromosomes. Studies have reported an association of SNPs in CHRNA5–CHRNA3 ( Wang et al. 2009 ) and CHRNA6–CHRNB3 ( Hoft et al. 2009 ) gene clusters with alcohol dependence or alcohol consumption.

Genome-wide Association Studies

In the past few years, it has become possible to genotype up to a million SNPs throughout the genome in a single experiment—an approach called genome-wide association studies (GWASs). This technique, which is based on the assumption that common genetic variation contributes to disease risk, allows a comprehensive test of association across the genome, rather than testing only one gene at a time. It has been used for many different diseases, with varying success. In particular, the relatively low statistical power of GWASs is a significant hurdle. Thus, the analyses require very large samples because most variations only have small effects; moreover, the multiple testing involved in a GWAS reduces the statistical power to detect associations.

Several studies recently have reported GWAS results from case–control studies comparing alcohol-dependent case subjects to nondependent control subjects. The first published study, conducted in Germany, compared 487 men in inpatient treatment for alcohol dependence to 1,358 control subjects ( Treutlein et al. 2009 ). The study identified several SNPs in a region on chromosome 2 that previously had been linked to alcohol dependence, as well as SNPs in a gene called CDH13 that is located on chromosome 16 and the ADH gene ADH1C on chromosome 4.

Recently, COGA reported results of a GWAS that included 847 alcohol-dependent case and 552 control subjects ( Edenberg et al. 2010 ). The combined evidence from this case–control study, a follow-up in families, and gene expression data provided strongest support for the association with alcohol dependence of a cluster of genes on chromosome 11. 7 However, the associations detected in the COGA GWAS did not reach the threshold for statistical significance for this type of analysis, and therefore additional studies must be conducted to further define the associated genes. Several SNPs nominated as candidates in the earlier German GWAS also were replicated in the COGA sample, including SNPs in or near the genes CPE , DNASE2B , SLC10A2 , ARL6IP5 , ID4 , GATA4 , SYNE1 , and ADCY3 .

Another recent report ( Bierut et al. 2010 ) described a GWAS using an overlapping set of COGA subjects as well as additional subjects recruited as part of other addiction research projects. This sample included both African-American and European-American subjects, and the primary analysis sought to identify association with alcohol dependence using a case–control design. Although none of the detected associations met genome-wide criteria for statistical significance, there was some evidence to support the previously reported association in GABRA2 as well as in a gene called ERAP1 , which encodes the enzyme endoplasmic reticulum aminopeptidase 1 ( Bierut et al. 2010 ). Finally, a GWAS in a sample of twins and their families recruited in Australia is currently being analyzed.

Genetic Animal Models of Alcohol’s Effects and Alcohol Use

Since the earliest days of alcohol research, the use of animal models has featured strongly in attempts to understand genetic contributions to the mechanisms through which alcohol exerts its biological effects and to individual differences in risk for alcohol dependence. The main advantage of animal models for these genetic analyses is that they allow researchers to more tightly control environmental influences, thereby making it easier to identify genetic risk factors.

In 1959, inbred mouse strains first were shown to differ in their tendency to drink alcohol (McClearn and Rodgers 1959), and studies with inbred strains continue to this day. Each inbred strain possesses a random collection of genes (i.e., genotype), but all the animals within a strain are genetically identical. This reduction in the genetic variation among the animals studied could increase the power to identify genes contributing to alcohol-related traits.

Another commonly used type of animal model involves selectively bred lines. Starting in the late 1940s, researchers in Chile bred rats that preferred to drink alcohol-containing solutions as well as rats that avoided alcohol ( Mardones and Segovia-Riquelme 1983 ). Such selective breeding has been repeated numerous times with rats and mice, resulting in pairs of animal lines that differ with respect to a particular alcohol-related trait. A list of currently available rodent selected lines is shown in table 1 . Studies with the high- and low-drinking selected lines in particular have been a major focus of NIAAA-sponsored research efforts (for a review, see Crabbe et al. 2010 ; other reviews were published in a special issue of Addiction Biology , Vol. 11[3–4], 2006). Animals have been selected for many alcohol-related traits, including preference for alcohol, tolerance or sensitivity to alcohol’s effects, and withdrawal severity. New selection projects also are emerging; for example, researchers are breeding mice that exhibit binge-like drinking ( Crabbe et al. 2009 ).

Selectively Bred Rat and Mouse Lines With Differential Responses to Alcohol *

Studies with these selected lines have contributed a great deal to understanding the neurobiological bases for alcohol’s myriad effects. For example, researchers consistently have observed low levels of the neurotransmitter serotonin in certain brain areas (i.e., the limbic system) and other indications of dysregulation of the serotonin system in animal lines bred for high alcohol drinking (Crabbe 2008). Other studies with selected lines have shown dysregulation of the GABA and glutamate systems in animals bred to exhibit severe withdrawal. ( Finn et al. 2004 ).

Contributions of Genetic Animal Model Research

Enhanced understanding of alcohol’s pharmacology and neurobiology.

Animal research has been invaluable for discovering how alcohol exerts its biological effects. For example, numerous studies have shown an important role for GABA neurotransmission in mediating alcohol’s acute and chronic effects ( Finn et al. 2004 ; Lobo and Harris 2008 ; Kumar et al. 2009 ). Additional animal studies have demonstrated that alcohol’s pharmacology involves nearly all major neurotransmitter targets, including the glutamate/NMDA, 8 serotonin, dopamine, norepinephrine, and cannabinoid receptor systems ( Kelai et al. 2006 ; Smith et al. 2008 ; Vengeliene et al. 2005 ). By acting on all these signaling systems, alcohol ultimately exerts its effects through modulation of intracellular signaling cascades ( Newton and Messing 2006 ). Without animal models, researchers could not have gained an understanding of these highly complex mechanisms underlying alcohol’s diverse effects, and genetic animal models in particular have aided in understanding individual differences in sensitivity to these effects.

Gene Identification and Quantitative Trait Locus Mapping

Animal models also have been exploited for many years in attempts to identify specific gene variations associated with increased sensitivity to alcohol’s effects. These gene-mapping studies, which commenced in the early 1990s, have used methods similar to those described above for human studies (e.g., linkage analyses). They primarily have sought to identify quantitative trait loci (QTLs)—DNA regions that are associated with characteristics (i.e., quantitative traits) which vary in the degree to which they are present (e.g., sensitivity to alcohol or height). Such traits typically are determined by multiple genes and each QTL may contain one or more of these genes. Compared with humans, studies with rats and mice have the distinct advantage that researchers can use individuals with defined genotypes and control patterns of mating, making it much easier to localize the chromosome region of interest (i.e., the “locus” of the QTL). The most recent systematic review ( Crabbe et al. 1999 ) of the alcohol-related QTL data for the various alcohol-related traits being mapped, which now is out of date, listed the likely locations of several genes affecting alcohol withdrawal severity, preference for drinking, and sensitivity to alcohol’s effects. Researchers at the Oregon Health & Science University now maintain a much more recent update of mouse alcohol QTL locations for these and other alcohol-related traits, which can be accessed via the Portland Alcohol Research Center Web site ( ).

The greatest success story for alcohol-related QTL mapping in rodents has been the discovery of a quantitative trait gene (QTG) 9 that affects acute withdrawal severity from both alcohol and pentobarbital in mice. Originally, investigators mapped several QTLs contributing to this trait to locations on various mouse chromosomes ( Buck et al. 1997 ). Subsequent studies with a variety of specifically created genetic animal models gradually narrowed down the size of the DNA region (i.e., reduced the confidence interval) around one of these QTLs until only a few genes remained within the confidence interval. Functional studies then demonstrated that the most likely gene contributing to the trait was Mpdz , which encodes a protein containing multiple structural components known as PDZ-domains ( Shirley et al. 2004 ). Studies of this gene’s pattern of expression in the brain and of the functions of the MPDZ protein continue, as do studies to identify the receptor molecules with which MPDZ interacts (e.g., the serotonin 2C receptor) ( Chen et al. 2008 a ; Reilly et al. 2008 ).

Additional mapping studies aim to narrow other QTLs for alcohol responses, both in animals ( Bennett et al. 2007 , 2008 ; Hitzemann et al. 2009 ) and in humans. A recent comparison of data from mouse and human QTL mapping identified a promising region of human chromosome 1 that was linked to alcohol dependence and which overlapped with an area of mouse chromosome 1 that has been linked to an alcohol withdrawal QTL ( Ehlers et al. 2010 ). 10 However, as described by Ehlers and colleagues (2010) a detailed comparison of rodent and human maps to see whether the QTLs from rodent studies identify the same chromosomal regions as the linkage studies in humans is very difficult. Nevertheless, some promising results of cross-species consistency exist, which likely will increase in number as the details of both rodent and human genetic maps improve.

Classical QTL analysis has associated individual differences in gene sequence (or in other genetic markers, such as microsatellites) with differences in the phenotype being mapped. A recent development in rodent QTL mapping has been development of expression QTL (eQTL) mapping. eQTLs are DNA regions that differ not in their gene sequence, but in the level to which the gene becomes active (i.e., is expressed) in individuals differing with respect to certain alcohol-related traits. This information can be gathered from microarray experiments that measure the levels of individual mRNAs. These additional eQTLs greatly expand the pool of potentially informative genes.

The eQTL approach has been used to compare gene expression in brain tissue from several rodent lines and strains genetically predisposed to drink alcohol with control tissue from low-drinking animals. The chromosomal location of differentially expressed genes then was compared with QTL data based on genetic sequence variations (i.e., polymorphisms). This combination of information suggested several candidate genes that may influence alcohol drinking ( Mulligan et al. 2006 ; Weng et al. 2009 ).

An additional refinement to the gene-finding efforts has been the study of networks of proteins or the genes that encode them. The reasoning is that even if different studies (or studies in different species) do not identify the same specific gene as being involved in a trait, they might identify a network of genes that underlies the genetic “signal” across studies and datasets and which encodes proteins that have similar functions or are involved in similar pathways (e.g., McBride et al. 2009 ).

Candidate Gene Studies and Gene Targeting

Another important development enhancing the possibilities of genetic animal models of alcoholism was the development of transgenic animals in the late 1980s. These are animals that have been genetically modified so that the expression of a single candidate gene has been selectively inactivated or augmented compared with the parent strain. This approach allows researchers to study the influence of individual genes on risk for alcoholism (or many other diseases or behaviors). By now, more than 100 candidate genes have been studied for their contribution to alcohol’s effects, usually by comparing mice in which a single gene has been inactivated (i.e., knockout mice) with control mice in which the gene still is functional. As reviewed by Crabbe and colleagues (2006) , most of the genes thus studied were found to influence some aspect of alcohol sensitivity. For example, of 84 different transgenic animals tested for effects on alcohol self-administration, one-quarter exhibited increased drinking, one-third exhibited decreased drinking, and 40 percent did not differ from control animals ( Crabbe et al. 2006 ). This finding clearly demonstrates the multiplicity of genetic influences on alcohol responses. As gene-targeting technologies allow more specific experimental regulation of genes than simple deactivation or over-expression, these approaches will continue to provide important data. For example, researchers now can manipulate genes so that they are expressed only in certain cell types or during particular developmental periods.

Candidate gene studies also have been valuable in looking for consistency across species in the impact of certain genes or gene variants. Invertebrate models (e.g., the fruit fly Drosophila or the worm Caenorhabditis elegans ) offer much more powerful tools to manipulate the genome than do rodents ( Lovinger and Crabbe 2005 ). However, to be able to use such models, researchers first need to demonstrate that corresponding genes exist in these organisms and that they actually have similar functions. One example of such convergence of evidence is the finding that a small signaling molecule called neuropeptide Y (NPY) and its receptors play a role in alcohol intoxication in mice, rats, and Drosophila ( Chen et al. 2008 b ; Gilpin et al. 2004 ; Thiele et al. 2002 ). A meta-analysis of human association data, in contrast, found no clear evidence that polymorphisms in the gene encoding a precursor of NPY are associated with alcohol dependence ( Zhu et al. 2003 ). However, some genes encoding NPY receptors may play a role in alcohol dependence and withdrawal ( Wetherill et al. 2008 ). Finally, certain signaling proteins (e.g., epidermal growth factor receptor [EGFR], protein kinase C, protein kinase A, and cyclic AMP [cAMP]) have been implicated in alcohol’s effects across multiple species, including humans, rats, mice, Drosophila , and zebrafish ( Corl et al. 2009 ; Newton and Messing 2006 , 2007 ; Peng et al. 2009 ).

Studies of Gene–Environment Interaction

Studies clearly have shown that both genetic and environmental factors contribute to the risk for alcohol dependence, and it is likely that the interplay between these factors is critical for determining the risk for alcohol abuse and dependence. Advances in genetic technologies already have allowed researchers to explore the genome in ever greater detail, and with the advent of whole-genome sequencing, complete delineation of genetic variation soon will be available. In contrast, our understanding of the critical environmental factors influencing alcohol use disorders remains inadequate and is an area of active research. One of the challenges is how to define the environment, which may include family, peer, and societal influences; other exposures; personality or psychiatric factors (which also have genetic components); and many more, most of which change over time. Furthermore, the influence of these factors on the risk of alcohol use disorders varies within the lifespan ( Sher et al. 2010 ; van der Zwaluw and Engels 2009 ).

Animal models offer significant advantages for studies attempting to tease apart genetic and environmental influences on an individual’s risk for alcoholism. Given their methodological power, it is surprising how little research into this area has been done using genetic animal models. One trait that has been found to be genetically determined is alcohol preference of inbred mouse strains. Thus, specific mouse strains have displayed their tendencies to drink more or less alcohol by choice repeatedly across 50 years of studies. In fact, alcohol preference in these animals is even more replicable across studies (and therefore, across environments) than brain weight ( Wahlsten et al. 2006 ), suggesting that it is strongly influenced by genetic effects. Not all alcohol traits are so stable, however, and the combined effects of genetic and environmental manipulations could be exploited more fully using genetic animal models.

A recent review has discussed several important features of gene–environment interaction research ( Sher et al. 2010 ). For example, the social environment plays such a crucial role in shaping drinking behaviors in humans, but it is difficult to identify corresponding rat and mouse behaviors and environmental factors. One example of a study analyzing gene– environment interactions in animals ( Hansson et al. 2006 ) compares the influence of environmental stress in a rat line selectively bred for high alcohol preference (i.e., the Marchigian-Sardinian preferring rats) with their nonselected control group. The investigators found that the genetically “enriched” rats were more sensitive than the control animals to the effects of environmental stress on reinstatement of previously extinguished alcohol drinking (i.e., the alcohol-preferring rats resumed alcohol drinking more easily after being exposed to a stressor). Moreover, the differences resulted, at least in part, from variations between high-drinking and low-drinking animals in a gene encoding a receptor for corticotropin-releasing hormone (CRH) (which is involved in the body’s stress response) and in the expression of that gene ( Hansson et al. 2006 ). Thus, this study demonstrated an interaction between a specific genotype and an environmental factor (i.e., stress).

Analysis of human gene–environment interactions also are complicated by the fact that these interactions are important from adolescent exposure to alcohol and then throughout life. Accordingly, from a developmental perspective, the critical environmental influences are likely to change over time (e.g., the relative influence of family versus peer factors). Studies that follow genetically specified animals prospectively while extracting biological information at different times along the way are a promising area for future research that has not been sufficiently exploited thus far.

Future Directions

Research into the genetics of alcoholism, both in humans and in animal models, has made great strides over the past four decades, and even more approaches are beginning to be evaluated. For example, there is growing interest in studying epigenetic factors—that is, factors which alter certain phenotypes by modifying regulation of gene expression, without, however, changing the gene’s DNA sequence. One such factor that can impact gene expression is methylation of the DNA. Other epigenetic changes alter the packaging of DNA into chromatin. For example, two enzyme families called histone acetyltransferases and deacetylases can be used to alter chromatin structure experimentally, and studies found that when such changes accompany chronic drug administration, they can modify cocaine-related behaviors in rats ( Renthal and Nestler 2009 ). Although similar research on alcohol-related traits still is in its infancy, some studies have found that alcoholic patients exhibited greater levels of DNA methylation of two different genes than nonalcoholics and, consequently, greater reduction in the expression of those genes ( Bleich et al. 2006 ; Bonsch et al. 2005 ).

MicroRNAs—short RNA molecules naturally encoded by the genome that can bind to certain mRNA molecules, thereby repressing the further processing of these mRNAs—also might be involved in regulating alcohol’s effects (Miranda et al. 2010). These microRNAs also offer a new experimental method for silencing the expression of specifically targeted genes. The expression of microRNAs is sensitive to epigenetic modulation, and turning microRNAs on or off has become feasible in rodent models. Modification of microRNAs may offer a new pathway for identifying critical genes that can then serve as target for new therapeutic drugs for alcoholism treatment.

In summary, the genetics field has undergone a technological revolution, particularly in the past decade, allowing researchers to process large numbers of samples for their genetic studies and to efficiently interrogate the entire genome. Using these strategies, researchers have been able to identify a number of genes in which variations appear to contribute to the susceptibility to alcohol dependence. It is important to note, however, that the individual role of each of these genes, and the SNPs within them, is quite modest. This means that a given allele or SNP that has been found to be associated with alcohol dependence may increase the risk of alcoholism only incrementally. As a result, it would be a gross overinterpretation of the results obtained in human association studies to date to suggest that we currently have a means to identify people at greatest risk for alcohol dependence. With the exception of the strong protective effects of certain ADH and ALDH variants, each gene variant identified to date has a much smaller individual effect on alcoholism risk than, for example, a family history of alcoholism.

Another challenge is to relate the complex human behavioral phenotypes to specific variations in the sequence and expression of specific genes and, perhaps more importantly, to the function of the proteins encoded by these genes. The answers may come from networks of genes that encode proteins of similar function, rather than from specific genes individually. Examining such networks represents another level of complexity that poses a huge quantitative challenge, computationally and statistically. However, researchers also are making substantial progress on this bioinformatics front, and the continuing development of greatly enhanced bioinformatics capacity is increasing the power of studies in both rodent models and humans.

The identification of any genes that appear important in alcoholism susceptibility provides an opportunity to better understand the biological pathways involved in alcohol’s actions. It also may yield important insights that will allow the development of better pharmacological treatments to help those who wish to reduce their alcohol consumption. All such potential new therapies will of course be tested first in animal models ( Egli 2005 ), and the coordination of animal model and human research therefore will continue to be an important theme for alcohol research for many years to come.


Preparation of this manuscript was supported by National Institutes of Health grants U10AA008401, P60AA007611, P60AA10760, U01AA13519, R37AA06460, U01AA016660, and R21AA017941 and the Department of Veterans Affairs.

1 DALYs are a measure of burden of disease. One DALY is equal to 1 healthy year of life lost.

2 For a definition of this and other technical terms, see the glossary, pp. 161–164.

3 Adoption studies compare the disease status of adoptees with that of their birth parents (with each of whom they share on average half their genome) and of their adoptive parents (with whom they typically have no genetic relationship and do not share their genome).

4 These criteria, which were the accepted diagnostic criteria at the time of COGA’s initiation, were based on the definitions established in the DSM–III–R ( American Psychiatric Association 1987 ) and by Feighner and colleagues (1972) .

5 Noncoding SNPs are DNA sequence variations that are located in regions of the ADH gene that do not encode the actual ADH protein.

6 G-protein–coupled receptors interact with a signaling molecule (e.g., acetylcholine) outside the cell, resulting in the activation of signaling pathways within the cell and thereby inducing a cellular response. Specifically, binding of the receptor to the signaling molecule alters the structure of the receptor so that it can activate an associated G-protein, which in turn can act on other proteins in the cell.

7 The genes located in this cluster are SLC22A18, PHLDA2, NAP1L4, snora54, CARS, and OSBPL5 .

8 The N -methy- d -aspartate receptor is one of the receptor types for the neurotransmitter glutamate.

9 In contrast to a QTL, which only identifies a DNA region that is likely to contain a gene contributing to a quantitative trait (but also may contain other, unrelated DNA sequences), a QTG represents the actual gene.

10 Although humans and mice have different numbers of chromosomes and substantial variation in their genome, there are some parallels between the two genomes. Thus, about 80 percent of genes that are located closely together on a human chromosome also tend to be located in a cluster on a mouse chromosome.

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ISSN: 2378-3648

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NLM title abbreviation: J Genet Genome Res

ISO abbreviation: J Genet Genome Res

Other titles: JGGR

Category: Genetics/Genome/Gene

DOI: 10.23937/2378-3648

Peer review: Double blind

Review speed: 3 weeks

Fast-track review: 10 days

Publication format (s): Electronic and print

Publication policy: Open Access; COPE guide

Publication type(s): Periodicals

Publisher: ClinMed International Library

Country of publication: USA

Language: English

Contact email: [email protected]

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ClinMed Journals Index Copernicus Values

Clinical Medical Image Library: 93.51

International Journal of Critical Care and Emergency Medicine: 92.83

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Journal of Infectious Diseases and Epidemiology: 91.55

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International Archives of Nursing and Health Care: 90.87

International Journal of Ophthalmology and Clinical Research: 90.80

International Archives of Urology and Complications: 90.73

Journal of Clinical Nephrology and Renal Care: 90.33

Journal of Family Medicine and Disease Prevention: 89.99

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Journal of Dermatology Research and Therapy: 89.34

International Journal of Clinical Cardiology: 89.24

International Journal of Radiology and Imaging Technology: 88.88

Obstetrics and Gynaecology Cases - Reviews: 88.42

International Journal of Blood Research and Disorders: 88.22

International Journal of Diabetes and Clinical Research: 87.97

Copy number footprints of platinum-based anticancer therapies

February 13, 2023

Copy number footprints of platinum-based anticancer therapies

Image credit: NCI

Topic Pages

Read the full collection of topic pages published in plos genetics..

Research Article

Bayesian inference of admixture graphs on Native American and Arctic populations

One way of summarizing historical relationships between genetic samples is by constructing an admixture graph. Nielsen & Vaughn et al develop and apply a novel MCMC sampling method, AdmixtureBayes, to a set of Native American and Arctic genomes to reconstruct the demographic history of these populations and report posterior probabilities of specific admixture events.

Image credit: pgen.1010410

Bayesian inference of admixture graphs on Native American and Arctic populations

Recently Published Articles

Current Issue

Current Issue January 2023

Stepwise recombination suppression around the mating-type locus in an ascomycete fungus with self-fertile spores

Vittorelli & de la Vega et al find a striking convergence in lifecycle, sexual biology and stepwise recombination suppression around the mating-type locus in phylogenetically distant fungi of the order Sordariales.

Image credit: pgen.1010347

Stepwise recombination suppression around the mating-type locus in an ascomycete fungus with self-fertile spores

Biosynthetic constraints on amino acid synthesis at the base of the food chain may determine their use in higher-order consumer genomes

The amount and type of food that organisms consume shapes their fitness. Gómez Ortega and colleagues attempt to improve dietary amino acid proportions (protein quality) for male and female reproduction in the fruitfly.

Image credit: Katja Schulz, Flickr

Biosynthetic constraints on amino acid synthesis at the base of the food chain may determine their use in higher-order consumer genomes

Opinion Piece (invitation only)

Increased calorie gain from lactose digestion could contribute to selection for lactase persistence

Lactase persistence is the retained ability of adults to express the gene for lactase, allowing adults to consume milk. In this Opinion, David Curtis highlights a factor that is overlooked in existing theories regarding the nature of the factors driving selection for LP as a trait.

Increased calorie gain from lactose digestion could contribute to selection for lactase persistence

Image credit: Kim Gorga, Unsplash

Timing of integration into the chromosome is critical for the fitness of an integrative and conjugative element and its bacterial host

Integrative and conjugative elements (ICEs) are major contributors to genome plasticity in bacteria. McKeithen-Mead & Grossman demonstrate that the timing of integration of an ICE is linked to cessation of autonomous replication of the ICE, and that perturbing this linkage leads to a decrease in ICE and host fitness.

Timing of integration into the chromosome is critical for the fitness of an integrative and conjugative element and its bacterial host

Image credit: pgen.1010524

Spinal cord extracts of amyotrophic lateral sclerosis spread TDP-43 pathology in cerebral organoids

Amyotrophic lateral sclerosis (ALS) is a disease characterized by the degeneration of selected groups of neuronal cells. Tamaki et al use a self-organizing three-dimensional cell culture model to test if TDP-43 could contribute the progression of the disease that occurs across the central nervous system of patients.

Spinal cord extracts of amyotrophic lateral sclerosis spread TDP-43 pathology in cerebral organoids

Image credit: pgen.1010606

Germline protein, Cup, non-cell autonomously limits migratory cell fate in Drosophila oogenesis

Saha, Acharjee and colleagues

Germline protein, Cup, non-cell autonomously limits migratory cell fate in Drosophila oogenesis

Image credit: pgen.1010631

A ZTF-7/RPS-2 complex mediates the cold-warm response in C. elegans

Xu and colleagues

A ZTF-7/RPS-2 complex mediates the cold-warm response in C. elegans

Image credit: pgen.1010628

Pharmacological Vitamin C Inhibits mTOR Signaling and Tumor Growth by Degrading Rictor and Inducing HMOX1 Expression

Qin, Wang and colleagues

Pharmacological Vitamin C Inhibits mTOR Signaling and Tumor Growth by Degrading Rictor and Inducing HMOX1 Expression

Image credit: pgen.1010629

Differential modularity of the mammalian Engrailed 1 enhancer network directs sweat gland development

Aldea and colleagues

Differential modularity of the mammalian Engrailed 1 enhancer network directs sweat gland development

Image credit: pgen.1010614

New PLOS journals accepting submissions

Five new journals unified in addressing global health and environmental challenges are now ready to receive submissions: PLOS Climate , PLOS Sustainability and Transformation , PLOS Water , PLOS Digital Health , and PLOS Global Public Health

COVID-19 Collection

The COVID-19 Collection highlights all content published across the PLOS journals relating to the COVID-19 pandemic.

Submit your Lab and Study Protocols to PLOS ONE !

PLOS ONE is now accepting submissions of Lab Protocols, a peer-reviewed article collaboration with, and Study Protocols, an article that credits the work done prior to producing and publishing results.

PLOS Reviewer Center

A collection of free training and resources for peer reviewers of PLOS journals—and for the peer review community more broadly—drawn from research and interviews with staff editors, editorial board members, and experienced reviewers.

Ten Simple Rules

PLOS Computational Biology 's "Ten Simple Rules" articles provide quick, concentrated guides for mastering some of the professional challenges research scientists face in their careers.

Welcome New Associate Editors!

PLOS Genetics welcomes several new Associate Editors to our board: Nicolas Bierne, Julie Simpson, Yun Li, Hongbin Ji, Hongbing Zhang, Bertrand Servin, & Benjamin Schwessinger

Expanding human variation at PLOS Genetics

The former Natural Variation section at PLOS Genetics relaunches as Human Genetic Variation and Disease. Read the editors' reasoning behind this change.

PLOS Genetics welcomes new Section Editors

Quanjiang Ji (ShanghaiTech University) joined the editorial board and Xiaofeng Zhu (Case Western Reserve University) was promoted as new Section Editors for the PLOS Genetics Methods section.

PLOS Genetics editors elected to National Academy of Sciences

Congratulations to Associate Editor Michael Lichten and Consulting Editor Nicole King, who are newly elected members of the National Academy of Sciences.

Harmit Malik receives Novitski Prize

Congratulations to Associate Editor Harmit Malik, who was awarded the Edward Novitski Prize by the Genetics Society of America for his work on genetic conflict. Harmit has also been elected as a new member of the American Academy of Arts & Sciences.

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