date: 2005-03-25
title: Findings of scientific misconduct.

# Findings of Scientific Misconduct

**Notice Number:** NOT-OD-05-040

**Key Dates
**Release Date: March 22, 2005

**Issued by
**Department of Health and Human Services (DHHS), ([http://www.hhs.gov](http://www.hhs.gov/))

Department of Health and Human Services (DHHS), ([http://www.hhs.gov](http://www.hhs.gov/))

Notice is hereby given that the Office of Research Integrity (ORI) and the Acting Assistant Secretary for Health have taken final action in the following case:

Eric T. Poehlman, Ph.D., University of Vermont: Based on the report of an investigation conducted by the University of Vermont (Report), admissions made by the respondent, and additional analysis conducted by ORI in its oversight review, the U.S. Public Health Service (PHS) found that Eric T. Poehlman, Ph.D., former Professor, Department of Medicine at the University of Vermont College of Medicine, engaged in scientific misconduct in research. The research was supported by National Institutes of Health (NIH) grants from the National Institute of Aging (NIA), the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), and the National Center for Research Resources (NCRR).

Specifically, PHS found that the respondent is responsible for scientific misconduct by engaging in the misleading and deceptive practices set forth herein below:

Group 1: Longitudinal study of aging; Protocol 678 and associated Excel spreadsheets

**Proposing Research (Report, pp. 22-25)**

1\. That Respondent falsified preliminary data purportedly obtained in a longitudinal study of aging in NIH grant application 1 R01 AG17906-01, submitted May 27, 1999; specifically, the claim of 130 subjects at visit one (T1) and 70 subjects at visit two (T2), mean values for total energy expenditure (TEE) obtained with a doubly-labeled water technique were falsified; additional parameters such as physical activity energy expenditure (PAEE), resting metabolic rate (RMR), fat-free mass, appendicular skeletal muscle mass, and percent body fat were falsified to show significant trends during the aging process that were not reflective of the actual data (Abstract and pp. 19, 21, 22, 23, 27, 29, 34, 41, 42).

2\. That Respondent falsified preliminary data purportedly obtained in a longitudinal study of aging in NIH grant application 1 R01 AG17906-01A1, submitted February 2000, specifically, the claim of 130 subjects at visit one (T1) and 70 subjects at visit two (T2), mean values for total energy expenditure (TEE) obtained with a doubly-labeled water technique were falsified; additional parameters such as physical activity energy expenditure (PAEE), resting metabolic rate (RMR), fat-free mass, appendicular skeletal muscle mass, and percent body fat were falsified to show significant trends during the aging process that were not reflective of the actual data (Abstract and pp. 32, 34, 38, 39, 45, 46).

**Conducting Research**

3\. That Respondent systematically falsified a number of metabolic and physical measures of subjects in the longitudinal study of aging; these falsifications of specific types of data in the Protocol 678 spreadsheet commenced immediately after he assigned responsibility for maintenance of the data to a young technician and simultaneously arranged to have personal access to the data; his widespread alteration of data in specific fields has been detected in a number of different versions, often with cumulative effect, and several were transmitted to different co-workers for specific reasons, as detailed in the following sub issues:

a\. that in the spreadsheet labeled "678data3.xls," produced during the late spring/early summer of 2000, Respondent falsified and fabricated numerous values in the fields called underwater fat mass (UWFM), underwater fat-free mass (UWFFM), leisure time activity (LTA), and maximum oxygen consumption (VO 2 Max);

b\. that on July 16, 2000, Respondent transmitted a subset of the Protocol 678 spreadsheet to Witness 1 entitled "RevisedTEE_s.xls;" that had 135 values each for T1 and T2 for TEE; many values were fabricated and most of the remaining values had been falsified by reversing the original T1 and T2 values (Report, pp. 6-8);

c\. that Respondent falsified additional data fields in the version of the 678 data set called "ExcelLongitudinal2.xls," on or about August 17, 2000; specifically values for total cholesterol, insulin, resting metabolic rate (RMR), and glucose values of the subjects with names in the second half of the alphabet were falsified (often by reversing T1 and T2) or fabricated (Report, p. 10);

d\. that Respondent gave falsified data to Witness 2 in August 2000 to provide him with data for a presentation to be given in September 2000 to UVM staff (initially postponed until February 2001); the spreadsheet given to Witness 2 contained the falsified and fabricated TEE and underwater body composition values of RevisedTEE_s.xls; the spreadsheet, when subsequently obtained by ORI, was labeled "LongitudinalBodyCompWitness2.xls";

e\. that Respondent falsified additional data in another version of "ExcelLongitudinal2.xls" that he sent to Witness 3 on or about August 22, 2000; specifically, this version contained the falsifications already described above (Issues 3a through 3c) and, in addition, the remainder of the glucose values, and individual lipid components (triglycerides, HDL, and LDL) were extensively falsified and fabricated; this spreadsheet was transmitted to Witness 3 with the expectation that he would write a paper describing the effect of aging on lipid metabolism (Report, pp. 8-10);

f\. that Respondent provided a falsified version of the Protocol 678 spreadsheet to Witness 4 in the fall of 2000 so that Witness 4 could write a review article;

g\. that Respondent, in late September/early October 2000, extensively falsified body composition data (a number of parameters including but not limited to fat mass and fat-free mass) obtained with the DEXA method in a spreadsheet transmitted to Witness 5 so that Witness 5 could write a paper using the DEXA method to demonstrate body composition changes with age (Report, pp. 5 and 39);

**Reporting Research**

h\. that Respondent reported falsified data from the longitudinal study of aging at the annual North American Association for the Study of Obesity (NAASO) meeting in October 2000, and to the Vermont community; the falsifications on his slides included falsified values for both the number of subjects tested at T1 and T2 for TEE and the claim of a significant difference between the means for TEE at T1 versus T2; values for RMR, PAEE, and body composition (fat mass and fat-free mass) were also falsely reported (Report, p. 34);

i\. From the falsified data set that Respondent provided him, Witness 4 developed a review article: Rawson, E. and Poehlman, E.T. "Resting metabolic rate and aging." Recent Research Developments in Nutrition 4, 2001, coauthored by Respondent, that included falsified yet unpublished results about the decline in RMR upon aging (p. R1792). These results, ORI determined, are very similar to the falsified results that Respondent presented at NAASO, based on the falsified Protocol 678 data set;

**Conducting Research**

j\. That on October 16, 2000, Respondent provided Witness 6 a version of the Protocol 678 data set entitled "ExcelLongitudinal4.xls" that included falsified cholesterol and individual lipid component data (as well as falsified parameters such as insulin, glucose (all subjects), TEE, RMR, PAEE, and underwater body composition data) so that Witness 6 could write a paper on the effect of aging on lipid composition (Report, pp. 8-10);

**Other**

k\. That Respondent falsely testified to the University of Vermont Investigation Committee that he had never used data from the longitudinal study of aging in grant applications or in public presentations (Report, pp. 34 and 36).

Group 2: Muscle biopsy results

**Proposing Research**

4\. That Respondent reported fabricated muscle biopsy data in NIH grant application 1 R01 AG17906-01A1 (p. 27), submitted in February 2000; specifically, he falsely claimed to have successfully tested five individuals on two occasions (1994 and 1999) when he had not (Report, pp. 25-26).

Group 3: Protocol 467, including the "longitudinal menopause study" and other falsifications/fabrications

**Reporting Research**

5\. That Respondent published falsified thyroid hormone results for women entered in a cross-sectional study (Protocol 467) (Figures 3A and 3B and related text and the portion of Table 2 related to T3 and free T3) in the following paper: Poehlman, E.T. Goran, M.I. Gardner, A.W., Ades, P.A., Arciero, P.J., Katzman-Rooks, S.M., Montgomery, S.M., Toth, M.J., and Sutherland, P.T. "Determinants of decline in resting metabolic rate in aging females." American Journal of Physiology 264(Endrocrinol Metab. 27):E450-E455, March 1993 (Correction required);

6\. That Respondent published in November 1995 falsified and fabricated data from a longitudinal study of menopause in women in the following paper: Poehlman, E.T., Toth, M.J., and Gardner, A.W. "Changes in energy balance and body composition at menopause: A controlled longitudinal study." Annals of Internal Medicine 123(9):673-675, November 1, 1995; Respondent has admitted that this longitudinal study was never conducted (the number of women seen at T1 was falsified, and there were at most 3, rather than 35, women seen at T2) (Report, pp. 27-32) (Retracted by editor; letter from Respondent required);

**Proposing Research**

7\. That Respondent repeatedly reported this non-existent longitudinal menopause study and cited the 1995 Annals of Internal Medicine paper in NIH grant applications as proof that Respondent could conduct such longitudinal studies, and the falsified and fabricated data supported his proposed hypotheses:

a\. Respondent provided for the annual report for the University of Vermont General Clinical Research Center (GCRC) grant (M01 RR00109) for the period 12/1/94-11/30/95, information about the falsified longitudinal menopause study, and the Annals of Internal Medicine paper was cited as having utilized the University of Vermont GCRC facilities;

b\. in application 5 K04 AG00564-05, submitted July 18, 1995, Respondent reported the results of a seven (7) year ^[1](#ftnref1)^ followup study of pre- and post-menopausal women, noting an article was in press in the Annals of Internal Medicine 1995 (unnumbered page 3);

c\. in application R01 AG13978-01, submitted in September 1995, Respondent reported falsified and fabricated data on menopause related changes in metabolism, body composition, and other variables in Preliminary Data (pp. 35, 41, and 42), and cited the published Annals of Internal Medicine 1995 paper;

d\. in application R01 AG13978-01A1, submitted in July 1996, Respondent reported falsified and fabricated data on menopause related changes in metabolism, body composition, and other variables in Preliminary Data (p. 33) and cited the published 1995 paper in the Annals of Internal Medicine and a submitted manuscript on the same topic (pp. 25, 29, 33, 40, 44, 49);

e\. in Project 1 of application P01 AG16782-01, submitted in June 1998, Respondent reported (p. 233) fabricated data showing that menopause led to significant changes in body composition (pp. 229, 230, 231, 232, 233, 246, 256) (Report, p. 32);

f\. in application 1 R01 AG 18238-01, submitted in April 1999, Respondent reported falsified and fabricated data from his longitudinal menopause study (RMR, leisure time physical activity, fat-free mass, fat mass, waist to hip ratio, and insulin (pp. 9, 18, 19, 20, 22, 23, 33, 37, 44);

g\. in application 1 R01 AG17906-01, submitted in May 1999, Respondent reported falsified and fabricated data in the description of his longitudinal menopause study (RMR, leisure time physical activity, and fat-free mass, p. 25);

h\. in Project 1 of application P01 AG16782-01A1, submitted in January 2000, Respondent reported the falsified and fabricated longitudinal menopause study (pp. 214, 220, 221, 228, 250) (Report, p. 32);

i\. in application 1 R01 AG17906-01A1, submitted in February 2000, Respondent reported the falsified and fabricated longitudinal menopause study (pp. 31 and 59);

j\. in application 1R01 AG19800-01, submitted in September 2000, Respondent reported the falsified and fabricated longitudinal menopause study (pp. 18 and 43).

**Reporting Research**

8\. That Respondent continued to publish papers on the fictitious longitudinal menopause study, referring to the same cohort of 35 women, 18 of whom purportedly went through the menopause transition during the six year followup period; all or parts of the following additional papers [^2^](#ftnref2) reported this non-existent study and require correction or retraction:

a\. Poehlman, E.T., Toth, M.J., Ades, P.A., and Rosen, C.J. "Menopause-associated changes in plasma lipids, insulin-like growth factor I and blood pressure: A longitudinal study." European Journal of Clinical Investigation 27(4):322-326, April 1997 (Report, p. 30) (Retraction required);

b\. Tchernof, A., and Poehlman, E.T. "Effects of the menopause transition on body fatness and body fat distribution." Obesity Research 6(3):246-254, May 1998 (pp. 249-251) (Correction required);

c\. Tchernof, A., Poehlman, E.T., and Despres, J.P. "Body fat distribution, the menopause transition, and hormone replacement therapy." Diabetes and Metabolism 26(1):12-20, February 2000 (Report, p. 31) (p. 17 Correction required);

d\. Rawson, E. and Poehlman, E.T. "Resting metabolic rate and aging." Recent Research Developments in Nutrition 4, 2001 (Correction required);

e\. Poehlman, E.T. "Menopause, energy expenditure, and body composition." Acta Obstet. Gynecol. Scand. 81(7):603-611, July 2002 (Retraction required).

f\. Poehlman, E.T. and Tchernof, A. "Traversing the menopause: Changes in energy expenditure and body composition." Coronary Artery Disease 9(12):799-803, 1998 (Correction/retraction required).

9\. That Respondent reported falsified and fabricated longitudinal menopause data in a talk presented in October 2000 at the annual NAASO meeting and to the Vermont community; specifically he reported to the NAASO falsified RMR and fat mass data on 40 women followed over six years (17 pre-menopausal, 18 post-menopausal, and 5-peri-menopausal) and RMR, FM, F-FM, PAEE, WHR, and insulin (Vermont Community) (Report, pp. 33-34).

**Other**

10\. That Respondent falsely wrote to the University of Vermont Investigation Committee that the subjects in the longitudinal menopause study had not stayed overnight in the GCRC for the second visit. In fact, no women were seen a second time at the GCRC on an in-patient or outpatient basis (Report, p. 29).

Group 4: Protocol 646 - Hormone replacement therapy and visceral fat and weight loss; the genetics of an obesity gene.

**Proposing Research**

11\. That Respondent included Protocol 646 in grant application 2 M01 RR00109-33 (funding for the University of Vermont, GCRC), submitted in February-March 1996, in which he provided falsified and fabricated data on 40 women with and without the variant gene Trp64Arg; falsified parameters included body weight, body mass index, and percent body fat that were falsely claimed to be significantly different between the two groups.

12\. That Respondent reported falsified and fabricated preliminary data and results in application 1 R01 AG18238 on HRT and its preferential effect on abdominal fat content:

a\. that Respondent, in grant application 1 R01 AG18238-01 (p. 24), submitted in April 1999, presented falsified data in Table 1, on a study of women who had reported to be on, or not on, hormone replacement therapy (HRT); specifically, he claimed that women on HRT had significantly lower intra-abdominal fat than non-users and that there was a significant difference in PAEE between the two groups;

b\. Respondent also falsely claimed to have evaluated the effect of HRT on intra-abdominal fat loss in a double blind placebo controlled study of 27 weeks duration (Figure 4); the actual study was not unblinded until 2002;

c\. Respondent also falsely claimed (pp. 36-37) to have completed a six month pilot study on the effect of exercise weight loss on postmenopausal women administered HRT, compared to women not on HRT.

13\. That Respondent, in grant application 1 P01 AG16782-01A1, submitted in January 2000, presented (p. 230) falsified data:

a\. on a study of women reported to be on, or not on, HRT; specifically the number of subjects in Table 4 was 25 for HRT users and 23 for non-users, while seven of eight values for PAEE and intra-abdominal fat (3 means and 4 standard deviations) were unchanged from Table 1 of Application 1 R01 AG18328-01, where the number of subjects was 13 for each group;

b\. Respondent repeated the false claim in the April 1999 application to have evaluated the effect of HRT on intra-abdominal fat in a double blind placebo controlled study of 27 weeks duration; the actual study was not unblinded until 2002; Respondent admitted to falsifying the figure in this application relative to the version in the 1 R01 AG18328-01 application;

c\. Respondent falsely claimed (p. 231) to have studied 8 post-menopausal women on HRT and 7 women not on HRT in a six month weight loss program, when the average ages, standard deviations and certain mean values were unchanged from the smaller and purportedly different, groups described in the April 1999 application ( *see* PHS Issue 12c above).

14\. That Respondent, in grant application 2 R01 DK052752-05, submitted in June 2000:

a\. falsified the number of subjects carrying or not carrying the Trp64Arg genotype in Tables 4, 5, and 6 (pp. 30-31); specifically in the application, he falsely claimed to have tested 40 in each group; Respondent admitted that the actual number tested varied from 8-13, depending on the group and parameter being measured;

b\. Respondent also falsely claimed that the number of women recruited to his funded grant on the menopause transition was 85 (p. 49).

15\. That Respondent, in grant application 1 R01 AG19800-01, submitted in September 2000:

a.-c. made the same three false claims with respect to HRT as in application 1 P01 AG16782-01A1 (Findings 13 a-c); in addition, Respondent falsely claimed in Table 5 that the number of subjects with and without HRT participating in the six-month weight loss program (see PHS Issue 13 c. above) was now 10 in each group rather than the group sizes of 8 and 7 claimed in Table 5 of the

1 P01 AG16782-01A1 application; many of the means and standard deviations in these two tables match the values obtained in a 6 month weight loss pilot study described on pp. 36-37 of application 1 R01 AG18238-01, where the two groups were comprised of 3 and 4 individuals; (pp. 13, 15, 17, 20, 21 and Tables 4 and 5 and Figure 6);

d\. falsely claimed (Table 3, p. 19) to have weight-reduced 70 obese women in the genetic study.

**Reporting Research**

16\. That in public presentations or material prepared for these fora, Respondent falsified or fabricated data and results of the effects of HRT and of the effects of the Trp64Arg genotype:

a\. that Respondent, at talks given at the annual NAASO meeting in October 2000, and to the Vermont Community (October 17, 2000), presented false information on the effects of HRT on visceral fat loss and glucose disposal when the HRT users and non-users were on a six-month weight loss program;

b\. that Respondent, in both the NAASO and Vermont Community talks, falsely claimed that Trp64Arg carriers have significantly lower rates of glucose disposal than non-carriers.

**Other**

17\. that Respondent falsely testified to the University of Vermont Investigation Committee that the slide shown at NAASO regarding the loss of visceral fat in women on or not on HRT during a six-month weight loss program (Issue 16a) had been labeled "hypothesized." Respondent falsely labeled the NAASO slide "hypothesized" and submitted it to the University of Vermont Investigation Committee with the intention of misleading the committee (Report, pp. 34, 37).

Group 5: Alzheimer's disease

18\. that Respondent, in applications 2 R01 AG07857-06 and 7 R01 AG07857-07, submitted June 26, 1992, and March 28, 1994, respectively, falsified certain preliminary data (average ages, height, and fat-free weight values) to show that the Alzheimer's and control patients were more closely matched for age than shown in the original data;

19\. that Respondent, in application 5 R01 AG07857-09, submitted May 18, 1995, falsified preliminary data; specifically, compared to data in the preceding 5 R01 AG07857-08 application, where the number of Alzheimer's and control subjects was 7 and 13 respectively, the number of Alzheimer's and control subjects was doubled to 14 and 26 respectively, while many of the data values and standard deviations remained unchanged; in the latter application however, Respondent claimed that Alzheimer's patients had significantly lower fat-free mass and significantly higher fat mass than control patients, while no claim of significant differences had been made in the earlier application.

Group 6: Effect of endurance training on metabolism

20\. Respondent admitted to falsifying norepinephrine data (a measure of sympathetic nervous system activity) in two papers published in 1992 and 1994 and agreed to retraction of the papers. ^[3](#ftnref3)^ Specifically:

a\. Respondent falsified norepinephrine data in Table 2 and Figure 4 of Poehlman, E.T., Gardner, A.W., and Goran, M.I. "Influence of endurance training on energy intake, norepinephrine kinetics, and metabolic rate in older individuals." Metabolism 41(9):941-948, September 1992, in order to strengthen the relationship between endurance training and increased norepinephrine levels and rate of appearance (paper to be retracted);

b\. Respondent falsified norepinephrine data in Table 2 and associated text of Poehlman E.T., Gardner, A.W., Arciero, P.J., Goran, M.I., and Calles-Escandon, J. "Effects of endurance training on total fat oxidation in elderly persons."

J. Appl. Physiol. 76(6):2281-2287, June 1994, in order to make the claims that norepinephrine concentration and norepinephrine appearance were significantly enhanced following endurance training (paper to be retracted).

Dr. Poehlman has entered into a Voluntary Exclusion Agreement (Agreement ) in which he has voluntarily agreed, beginning on March 9, 2005:

\(1\) to exclude himself permanently from serving in any advisory capacity to PHS including but not limited to service on any PHS advisory committee, board, and/or peer review committee, or as a consultant;

\(2\) to exclude himself permanently from any contracting or subcontracting with any agency of the United States Government and from eligibility or involvement in nonprocurement programs of the United States Government referred to as "covered transactions" as defined in the debarment regulations at 45 C.F.R.

Part 76; the respondent agrees that he will not petition HHS to reverse or reduce the scope of the permanent voluntary exclusion or administrative actions that are the subject of this Agreement; and

\(3\) to execute and deliver letters requesting retraction or correction to the editors of the journals that published the ten papers named in the Agreement and cited above, and to sign the letters requesting the retraction or correction prepared for his signature by ORI without alteration or modification in any way.

Inquiries

For further information contact:

Director**
** Division of Investigative Oversight**
** Office of Research Integrity**
** 1101 Wootton Parkway, Suite 750**
** Rockville, MD 20852**
** Telephone: (301) 443-5330

Footnotes

^1^ All other reports of the "longitudinal menopause study" claimed an average of six (6) years of follow-up.

^2^ The first paper describing the longitudinal menopause study, the 1995 Annals of Internal Medicine paper, was the subject of PHS Issue 6.

^3^ Both the 1992 and 1994 papers were designed to reproduce, under more controlled conditions, an earlier result, published in Poehlman, E. and Danforth, E. "Endurance training increases metabolic rate and norepinephrine appearance rate in older individuals." *Am. J. Physiol* . 261:E233-E239, 1991. These papers claimed that plasma levels of norepinephrine increased significantly in older individuals following endurance training. Because the norepinephrine results in the two carefully controlled studies conducted to verify this finding were falsified, it is apparent that this original report cannot be relied upon.


abstract: Geographic or ethnic differences in the occurrence of disease often provide insights into causes of disease and possible opportunities for disease prevention. Persons in China appear to have a consistently lower prevalence of rheumatoid arthritis and fibromyalgia than persons in the United States and Europe; reasons for these prevalence differences might include genetic differences, differences in environmental exposures or a combination of both. With increasing obesity, gout is becoming endemic in China. Finally, symptomatic knee osteoarthritis is extremely common in China and constitutes a major public health problem there.
author: David T Felson
date: 2008
institute: 1Clinical Epidemiology Research & Training Unit, 650 Albany Street, Suite X-200, Boston, MA 02118-2526, USA
references:
title: Comparing the prevalence of rheumatic diseases in China with the rest of the world

See related research by Zeng *et al.*, <http://arthritis-research.com/content/10/1/R17>

In the present issue of *Arthritis Research and Therapy* Zeng and colleagues comprehensively review studies evaluating the prevalence of rheumatic disease in China \[1\]. Their report suggests variations in the prevalence of symptomatic osteoarthritis throughout China, consistent prevalences for ankylosing spondylitis and rheumatoid arthritis, an absence of fibromyalgia, and a temporal increase in the prevalence of gout. What should we make of all of these estimates and why are they important to us outside China?

Estimates of the prevalence of rheumatic disease provide information about the burden of disease and suggest a need for provision of health services. For knee osteoarthritis, where the prevalence in China is high and knee replacements are not widely available \[2\], there may be a need for additional provision of health services.

Comparing the prevalence of disease from one region with another often provides insights into disease etiology. For example, coronary heart disease rates increased as Japanese migrated from Japan to Hawaii to mainland United States, suggesting that diet and environment were major causes of coronary heart disease. For systemic lupus, which was not evaluated by Zeng and colleagues, a prevalence gradient exists with low prevalence of lupus occurring in Africans, moderate rates along the slave trade route among African descendents living in the Caribbean, and high rates in the United States \[3\]. Using data from the article by Zeng and colleagues, we can make inferences about differences in disease prevalence that might provide clues regarding rheumatic disease etiology.

Zeng and colleagues reviewed diverse studies, many of them using the Community Oriented Program for the Control of Rheumatic Diseases (COPCORD) protocol. The COPCORD has been a joint initiative by the World Health Organization and the International League of Associations for Rheumatology whose goal was to provide information about the epidemiology of rheumatic disease in developing countries. The COPCORD methods require comprehensive population-based sampling with a rigorous protocol translated into the language of interest, and then, for persons with rheumatic complaints, there is follow-up testing and often evaluation by a rheumatologist to determine how many persons have identifiable diseases.

Estimates of rheumatic disease derived from the COPCORD and other studies have varied and, despite the impressive standardization of this protocol, variations in prevalence rates are probably still due, in part, to differences in how the studies are carried out from site to site. Also, for rheumatoid arthritis, many of these studies only detected a few cases in a community, so the estimate of prevalence is accompanied by wide confidence bounds, and there was substantial variation in prevalence from community to community. Another variation source is that crude prevalence estimates are usually presented, whereas age-standardized prevalence would often be more informative, especially since rheumatic diseases vary in prevalence by age. An additional source of variation is that some of the studies may not sample large numbers of older people who tend to have the highest prevalence of many rheumatic diseases, including rheumatoid arthritis. Finally, participation rates may differ in each study. While most COPCORD studies have reported participation rates exceeding 80% \[4,5\], some studies in Asia have not been so successful \[6\], and the failure to include persons with rheumatic diseases might seriously lower prevalence estimates.

Notwithstanding these limitations, the estimates by Zeng and colleagues suggest rheumatic disease prevalence rates in China that are different from those in the western hemisphere or Europe. Among these different rates is a prevalence of rheumatoid arthritis in China of roughly 0.2–0.3% of the population versus a prevalence of 0.8% in the western world \[7\]. The genes predisposing to rheumatoid arthritis may differ among Han Chinese versus mixed populations from the western world. Viral infectious triggers also could predominate in the West or could infect persons at an age when their response to infections was most likely to cause rheumatoid arthritis in genetically susceptible individuals, whereas similar individuals might be infected at a less susceptible age in China. Certainly, studies of Asian migrants to the western world might help determine whether Asians retain a lower prevalence of rheumatoid arthritis, or whether their rate after migration rises to that of the surrounding population, suggesting that environmental risk factors are the main cause of disease.

Zeng and colleagues report a prevalence of ankylosing spondylitis that is similar in China to reported estimates from the western world. In general, the prevalence of HLA-B27 in the community determines its prevalence of spondyloarthropathies. In the Han ethnic population HLA-B27 is present in between 4% and 6% of persons, a figure lower than that in Caucasians in the United States. This would be consistent with a lower prevalence of ankylosing spondylitis in China than the United States, rather than a comparable prevalence. Further documentation of this absence of a prevalence gradient should be confirmed with studies using broader definitions of disease (for example, undifferentiated spondyloarthropathy).

Gout has become more prevalent in China, a phenomenon also seen the United States in recent years. Causes include increasing rates of obesity and of aging in both populations, both risk factors strongly associated with the occurrence of gout. The dramatic increase in gout in China probably reflects the rapid westernization and urbanization of the society.

Finally, the absence of fibromyalgia in China could be explained by genetic differences in the processing of afferent nociceptors throughout the body or by the absence of central sensitization, a phenomenon thought to be closely tied to the development of fibromyalgia. Clearly some causes of fibro-myalgia are sociocultural, and it is possible that the different cultural environment in China might affect the acceptability of reporting chronic generalized pain. Investigation of the fibromyalgia prevalence in migrant Chinese communities might provide insights into which factors account for the exceedingly low prevalence of fibromyalgia in China. In addition, given the import of genetic variations in explaining racial and ethnic differences in chronic pain \[8,9\], an assessment of the prevalence of these genetic abnormalities in persons of Han ancestry might be of interest.

In summary, Zeng and colleagues have comprehensively summarized the prevalence of different rheumatic diseases in China. Their results present a mosaic of rheumatic disease prevalence, which differs from the rheumatic disease distributions in the western world. For those of us from the West interested in better understanding the etiology of rheumatic disease, the prevalence gradients that can be inferred from these data regarding rheumatoid arthritis and fibromyalgia, both of which may be considerably less common in China than in the western world, might provide important insights into what causes these diseases.

# Abbreviations

COPCORD = Community Oriented Program for the Control of Rheumatic Diseases.

# Competing interests

The authors declare that they have no competing interests.

### Acknowledgements

The present work was supported by NIH AR47785.


date: 2014-01-10
title: Findings of research misconduct.

# Findings of Research Misconduct

**Notice Number:** NOT-OD-14-038

**Key Dates
Release Date:** January 9, 2014

**Related Announcements
** None

None

**Issued by
**Department of Health and Human Services ([DHHS](http://www.hhs.gov/))

Purpose

Notice is hereby given that the Office of Research Integrity (ORI) has taken final action in the following case:

Timothy Sheehy, B.A., BSc., SAIC-Frederick, Inc.: Based on the report of an investigation conducted by SAIC-Frederick, Inc., and additional analysis conducted by ORI in its oversight review, ORI found that Mr. Timothy Sheehy, former Manager, DNA Extraction and Staging Laboratory (DESL), SAIC-Frederick, Inc., the Operations and Technical Services (OTC) Contractor for the Frederick National Laboratory for Cancer Research (FNLCR), Frederick, MD, engaged in research misconduct in research supported by National Cancer Institute (NCI), National Institutes of Health (NIH), contract HHSN2612008000001E awarded by FNLCR/NCI, NIH, to SAIC-Frederick, Inc., and the intramural program at the Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI.

ORI found that the Respondent engaged in research misconduct by fabricating and/or falsifying U.S. Public Health Service (PHS)-supported data in Table 1 included in Cancer Epidemiol Biomarkers Prev 19(4):973-977, 2010 (hereafter referred to as the \`\`CEBP paper').

Specifically, ORI found that Respondent fabricated the quantitative and qualitative data for RNA and DNA purportedly extracted from 900 formalin-fixed, paraffin-embedded (FFPE) colorectal tissue samples presented in Table 1 of the CEBP paper and falsely reported successful methodology to simultaneously recover nucleic acids from FFPE tissue specimens, when neither the extractions nor analyses of the FFPE samples were done. Thus, the main conclusions of the CEBP paper are based on fabricated data and are false.

Mr. Sheehy has entered into a Voluntary Settlement Agreement and has voluntarily agreed for a period of three (3) years, beginning on November 8, 2013:

\(1\) To have his research supervised; Respondent agreed that prior to the submission of an application for U.S. Public Health Service (PHS) support for a research project on which the Respondent's participation is proposed and prior to Respondent's participation in any capacity on PHS-supported research, Respondent shall ensure that a plan for supervision of his duties is submitted to ORI for approval; the supervision plan must be designed to ensure the scientific integrity of Respondent's research contribution; Respondent agreed that he shall not participate in any PHS-supported research until such a supervision plan is submitted to and approved by ORI; Respondent agreed to maintain responsibility for compliance with the agreed-upon supervision plan;

\(2\) That any institution employing him shall submit, in conjunction with each application for PHS funds, or report, manuscript, or abstract involving PHS-supported research in which Respondent is involved, a certification to ORI that the data provided by Respondent are based on actual experiments or are otherwise legitimately derived and that the data, procedures, and methodology are accurately reported in the application, report, manuscript, or abstract;

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author: Jeffrey T Leek; Steven L Salzberg
date: 2013
institute: 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 2Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA; 3McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
title: Sequestration: inadvertently killing biomedical research to score political points

The US Congress just allowed the 'sequester' to happen, which means they cut the national budget for biomedical research by 8.2%. What happened to most people? Nothing. As many pundits have already written, the sky didn't fall, and the economy didn't collapse. 'We must cut spending!' they say, 'and look: nothing happened.'

So the budget cuts must be okay. We'll just trim some wasteful spending from all that research.

Congress, here's what 'nothing' looks like: in 5, maybe 10 years from now, someone you care about gets sick, maybe from cancer. The only treatments are painful and not very effective, so the doctors decide the best option is to do nothing. Or maybe, 10 years from now, you have your first grandchild, but she is born with a life-threatening genetic disease. No treatment is known, so the doctors tell you and your children that nothing can be done.

That's what 'nothing' looks like. If we take only a short-term perspective, cutting the NIH and NSF research budgets won't seem to change anything outside of the scientific community. So the pundits are right: the sky won't fall, and the economy won't collapse. And in the long term, we'll have nothing.

So what will happen?

Well, most university research in the US is funded by 3- to 5-year grants from the federal government. Even at 3 years, it is difficult for scientists to plan: it takes about a year to get a grant, which means that when you get a new 3-year grant you only have 2 years to generate results before you have to apply for another one. And when you get a grant, it often takes time before you can find someone qualified to do the work (someone who you may have to train).

Due to uncertainty about sequestration, the NIH funded almost nothing new in the period from September until March, waiting to find out if the sequester would really happen. Literally thousands of biomedical researchers across the country have scrambled to keep funding their labs, waiting for decisions from NIH. As the cuts take effect, many of these labs will be forced to freeze hiring, cut back on experiments and reduce their scientific output.

The uncertainty is particularly devastating to young scientists. New PhD graduates continued to apply for jobs, but the jobs just haven't been there. 'Wait a while,' some of us said. 'We might have funding in the spring.' Some of the best young scientists won't wait: some have already gone to industry, and some might leave science entirely, discouraged over the sad state of fiscal uncertainty that now seems to be permanent. When highly skilled scientists leave the field, we've lost something that we will never get back.

Meanwhile the cuts from sequestration paint an increasingly grim picture for new investigators just starting their independent careers. These scientists have already poured years into training to build up enough expertise, experience and ideas to build entirely new scientific groups. But this is the most precarious time for a young scientist as they are asked to obtain and maintain funding on their own for the first time. Already, due to the sequester, the NSF announced that over 1,000 new grants will not be awarded. At the same time, the NIH may fund as few as 6% of the grants that are submitted in 2013 (<http://www.einstein.yu.edu/administration/grant-support/nih-paylines.aspx>) and ongoing grants may be reduced (<http://nexus.od.nih.gov/all/2013/03/04/nih-operations-under-the-sequester/>). The result is that it may go from being really hard to nearly impossible to get grants. If these young scientists can't support their work, we won't just lose the experiments they were going to perform this year. We may lose an entire generation of young scientists, leading to reduced American productivity in science that could last for decades (<http://www.theatlantic.com/politics/archive/2013/03/the-sequester-is-going-to-devastate-us-science-research-for-decades/273925/>).

# The worst part: they don't really mean it!

One of the more frustrating elements of the sequester fight is that no one in Congress seems to be in favor of pulling the rug out from under biomedical research. But due to their own inability to agree on larger budget issues, they are doing it anyway. And it's not just the sequester: the latest cuts come on top of a multi-year freeze in NIH funding, in which annual budgets have been flat or declining in real terms.

Science doesn't operate on a short-term basis. Cures for major diseases take years, often decades, to develop. And within the field, we can see tremendous progress on many fronts. Our understanding of cancer, heart disease, infectious diseases, aging, genetic diseases and a host of other conditions is moving ahead in leaps and bounds. This is the time to invest more, not less.

Although these cuts are devastating to NIH research, in the context of the US budget, they won't affect a thing. The entire NIH budget (<http://www.nih.gov/about/budget.htm>), supporting research on hundreds of diseases, is about \$31 billion. The total US budget last year was \$3.7 trillion. Thus an 8.2% cut from NIH saves just 0.07% of the federal budget. Or to put it another way: the Pentagon program to build the F-35 Joint Strike Fighter, which won't be ready until at least 2019, is already estimated to cost \$400 billion (<http://www.gao.gov/assets/660/652948.pdf>), equal to the entire NIH budget for 13 years. And that's just one plane.

What's more, NIH funding is a terrific investment. A nonpartisan study in 2000 (<http://www.faseb.org/portals/0/pdfs/opa/2008/nih_research_benefits.pdf>) found that publicly funded research yields a return of 25% to 40% per year. Where else can you get that kind of return on anything? But because this is a long-term investment, private businesses won't fund it, although they too benefit from the stream of discoveries and new technologies supported by public research funds.

We recently bailed out Wall Street because it is 'too big to fail.' Our biomedical research enterprise is not too big to fail - financially, that is. But how sick do we have to get before we decide we shouldn't have cut our investment in biomedical research?

Congress can do many things to show that they care about curing disease. If they really do care, they should first restore NIH's budget for this year. Then they should take action to insulate biomedical research from the endless, internecine battles between the parties. A good first step would be to approve multi-year funding plans for NIH and NSF, just as these agencies award multi-year grants to scientists. The default action should leave these multi-year plans in place. So the 'do nothing' option each year wouldn't cut the legs out from under active research projects, but instead would ensure at least level funding from year to year.

By the time scientists reach the point where they can lead productive research programs, we've already invested heavily in their training. Let's not kick them out on the street because someone in Congress wants to score political points. The health of the nation deserves better.


abstract: *Daphnia pulex* is the first crustacean to have its genome sequenced. Availability of the genome sequence will have implications for research in aquatic ecology and evolution in particular, as addressed by a series of papers published recently in *BMC Evolutionary Biology* and *BMC Genomics*.
  .
  See research articles <http://www.biomedcentral.com/1471-2148/9/78>, <http://www.biomedcentral.com/1471-2164/10/527>, <http://www.biomedcentral.com/1471-2148/9/79>, <http://www.biomedcentral.com/1471-2164/10/175>, <http://www.biomedcentral.com/1471-2164/10/172>, <http://www.biomedcentral.com/1471-2164/10/169>, <http://www.biomedcentral.com/1471-2164/10/170> and <http://www.biomedcentral.com/1471-2148/9/243>.
author: Angelika Stollewerk
date: 2010
institute: 1Queen Mary, University of London, School of Biological and Chemical Sciences, Mile End Road, London E1 4NS, UK
references:
title: The water flea *Daphnia* - a 'new' model system for ecology and evolution?

One of the major questions in evolutionary biology is to understand how species have adapted to different environments and how the underlying changes in morphology, physiology and behavior relate to modifications in the corresponding genes. The publication of the first crustacean genome sequence, that of *Daphnia pulex* \[1,2\], is part of an effort by the members of the *Daphnia* Genome Consortium to establish *Daphnia* as a model system for evolutionary environmental genomics. But can *Daphnia* rise to the challenge?

The vast number of publications on *Daphnia* in the literature prove that this animal is no newcomer to scientific research. *Daphnia* is most probably one of the best-studied subjects in ecology \[3\]. Populations can be found in freshwater environments ranging from huge lakes to small temporary pools and seasonally flooded depressions. The ecology of *Daphnia* has been studied from the point of view of its role as a primary consumer in aquatic food chains, its phenotypic plasticity, and its behavior, toxicology and the evolution of sexual and asexual reproduction. Extensive studies on the population genetics of *Daphnia* have addressed migration and gene flow, hybridization and inbreeding, among other topics. With the availability of the genome sequence, *Daphnia* research has now the potential to reach a new level. A number of papers on the *D. pulex* genome in relation to different aspects of *Daphnia* biology have been published in *BMC Evolutionary Biology* and *BMC Genomics* to accompany the genome release \[4-11\]. These constitute an initial exploration of the genome, and in this article I review some of the highlights and questions raised.

# *Daphnia* ecology and life style

*Daphnia* are filter feeders that direct small suspended particles into their mouth by a water current produced by their leaf-like legs (Figure 1). *Daphnia*'s common name of 'water flea' comes from its jump-like movement, which results from the beat of the large antennae used for swimming (Figure 1). In a normal growth season *Daphnia* generates diploid eggs by asexual reproduction (parthenogenesis). These eggs develop directly into larvae in the female brood chamber and are released into the water after about 3 days. In most species the larvae go through four to six larval stages before developing into sexually mature adults. However, the *Daphnia* life cycle is adapted to extreme environmental conditions such as cold winters or summer droughts. If triggered by external stimuli such as high population density and a scarcity of food, *Daphnia* can produce haploid resting eggs by meiosis; these require fertilization and a period of extended dormancy in order to develop \[3\]. Resting eggs are distributed by wind or animals and development is resumed in response to external stimuli (for example, rising temperature). Cyclic parthenogenesis, in which parthenogenesis and sexual reproduction alternate, is common in most *Daphnia* species, but lineages have been described that exclusively reproduce asexually (obligate parthenogenesis). Cyclic parthenogenetic *Daphnia* must contain the molecular tools for the production of both haploid gametes (by meiosis) and diploid eggs (by mitosis), the latter developing parthenogenetically into diploid zygotes. This makes *Daphnia* an ideal system to study the evolution of the molecular processes of parthenogenesis.

In this regard, Eads and co-workers (Schurko *et al*. \[4\]) suggest that differences between sexual and asexual reproduction most probably relate to mechanisms that differ between meiosis and mitosis, such as kinetochore orientation, DNA recombination and sister-chromatid cohesion, and have screened the *D. pulex* genome for genes associated with meiosis. The authors report an inventory of 130 *D. pulex* genes that are homologous with known genes in other organisms and which represent more than 40 distinct protein-coding genes with diverse roles in meiosis. The majority of these genes are present in multiple copies, and Schurko *et al*. \[4\] speculate that the extra copies may be partly responsible for changes to these meiotic processes that enable parthenogenesis. Parthenogenetic species are present in all major animal phyla and future comparison of the genomes of cyclic and obligate parthenogenetic lineages will shed light on the evolution of the underlying molecular processes.

The offspring produced in parthenogenetic cycles are genetic clones of their mother \[3\]. This includes the males, as sex is environmentally determined in *Daphnia*. The existence of clonal reproduction is a powerful tool for quantitative genetic studies because it facilitates the analysis of genetic variation within and between populations. Genetic variation has been reported in *Daphnia* for a vast number of traits such as size, aging, behavior (for example, vertical migration, fish-escape behavior), morphology (for example, defensive spines, helmets), and the immune system (for example, resistance against parasites, immune responses), and the great number of duplicated genes in *Daphnia* seems to correlate with *Daphnia*'s considerable phenotypic plasticity \[3\].

# Predators and other enemies

Interestingly, several of the above-mentioned traits can be induced by environmental cues. Changes in both the behavior and the morphology of *Daphnia* can, for example, be affected by predator-borne chemical cues (kairomones). In the presence of fish kairomones, *Daphnia magna* gives rise to smaller offspring, whereas chemical cues from the phantom midge *Chaoborus flavicans* induce the generation of larger progeny. This has been shown to be an adaptive phenotypic plasticity that helps avoid predation as fish and midges prefer different sizes of prey. These observations raise the question of the nature of the molecular response to kairomones. Schwarzenberger *et al*. \[5\] have addressed this question by comparing the expression levels of genes involved in protein biosynthesis and catabolism in *D. magna* in the presence or absence of kairomones. Interestingly, they found that expression of the cyclophilin gene, which encodes an enzyme involved in protein folding, is upregulated in the presence of fish kairomones but downregulated by *Chaoborus kairomones*, which correlates with the opposite effects of these kairomones on progeny size. The authors used the *D. magna* cyclophilin sequence to search the *D. pulex* genome and identified 16 paralogs, which showed a very high variability. Future research will show whether the differences in cyclophilin expression levels can be linked to the observed phenotypic variations and if additional paralogs are involved in the process.

The first step in kairomone-mediated adaptive changes in behavior and morphology is obviously the reception of the chemical signal by specialized sensory structures of the prey. Our knowledge about chemoreception in aquatic organisms is fragmentary, however. In insects, a conserved chemoreceptor superfamily has been identified which can be subdivided into the gustatory (taste) receptor family and the odorant (smell) receptor family. It is obvious that the sensing of odorants will be different in water than in air as aquatic odorants are hydrophilic molecules dissolved in water whereas airborne odorants are mainly hydrophobic volatile molecules in gaseous form. Penalva-Arana *et al.* \[6\] have identified 58 orthologs of the insect gustatory receptor family in the *D. pulex* genome. Interestingly, they found no evidence of genes homologous with insect odorant receptor genes and suggest that the odorant receptor family evolved concomitantly with the transition from sea to land in the lineage leading to the insects.

Predators are not the only natural enemies of *Daphnia*. The study of parasites (viruses, bacteria and multicellular parasites) has also gained momentum as a result of their influence on *Daphnia* ecology and evolution \[3\]. Parasites can directly or indirectly affect host population dynamics, extinction, and maintenance of genetic diversity, among other features. It has been suggested that hosts continuously evolve to reduce parasite virulence, whereas parasites evolve to keep virulence as close as possible to an optimum level. Variation in resistance to infection has indeed been observed in natural fruit fly populations and has been associated with genetic polymorphisms \[12\].

All metazoans seem to have an innate immune system, and in insects, at least four different signaling pathways are involved in the immune response and mediate pathogen recognition, attack on the pathogen, and antiviral RNA interference, among other responses. McTaggart *etal.* \[7\] analyzed the *D. pulex* genome for genes related to the immune system and identified genes homologous with those in other arthropods. The authors found that the Toll signaling pathway, which is activated by the presence of pathogens, is conserved between insects and *Daphnia*. The activation of this pathway results in the production of antibacterial and antifungal proteins. These antimicrobial peptides could not be recovered from the *D. pulex* genome and thus seem to be less well conserved. In addition, McTaggart *et al.* \[7\] report considerable variation in gene family copy number in *Daphnia* and insects. These differences might reflect the evolutionary history of host-parasite interactions in the individual lineages. Further comparative studies are needed to uncover evolutionary changes in genes that mediate immune responses as well as taxon-specific expansions of gene families, which will contribute to our understanding of how host genes are evolving in response to parasites.

Although a vast number of ecto-and endoparasites have been described for *Daphnia*, the non-parasitic symbionts have not been analyzed in detail. Ebert and co-workers (Qi *et al*. \[8\]) have used metagenomics to address this question. Metagenomes genetic material recovered directly from environmental samples are sequenced and compared to the databases in order to characterize the biological community of a given habitat. One of the advantages of this approach is the recovery of DNA sequences from organisms that cannot be cultured. Ebert and co-workers \[8\] searched the shotgun sequences of three clones of three different *Daphnia* species (*D. pulex, D. magna* and *D. pulicaria*) for indications of bacterial and plastid symbionts and found sequences representing a large number of bacterial species in each dataset. The majority of the sequences were from the Proteobacteria but many other taxa were also detected. No clear evidence was found for the presence of symbiotic cyanobacteria or of plastids. Interestingly, the composition of the bacterial communities was similar at genus and higher taxonomic levels in all three *Daphnia* clones, but different bacterial species were present in individual clones. The *D. pulex* and *D. pulicaria* DNA used in this study was isolated from clones cultured in North America, whereas the *D. magna* cultures originated from a laboratory in Switzerland. Since contamination of the *Daphnia* cultures by cross-Atlantic exchange is unlikely, the authors suggest that the similarities between the symbiont communities in European and North American *Daphnia* samples indicate a long-term stability of symbiotic associations.

# Environmental challenges

*Daphnia* species have been studied extensively because of their importance to aquatic ecosystems, and they show a striking ability to contend with environmental challenges. The availability of the *D. pulex* genome should now be able to provide insights into the adaptation to specific environmental conditions, from the ecological to the genetic level. On screening the *D. pulex* genome for genes involved in the biochemical response to toxicants, Baldwin *et al.* \[9\] identified 75 genes of the cytochrome P450 family, a protein family important in tolerance and resistance to environmental chemicals. The authors report that the same subgroups of cytochrome P450 genes are present in the *Daphnia* genome as in insects and nematodes, but they discovered distinct changes in the size and gene composition of each group. Dean and co-workers (Sturm *et al*. \[10\]) screened the *Daphnia* genome for the presence of members of the ABC transporter superfamily (ATP-binding cassette membrane transport proteins), which are also involved in biochemical defense against toxicants. They found that ABC family representation in *Daphnia* is as complex as in other metazoans, and that *Daphnia* most resembles the fruit fly in respect of its ABC transporter genes. Future studies on the expression and function of these genes will uncover their importance in the adaptation of *Daphnia* to environmental toxicants.

# *Daphnia* and arthropod phylogeny

The *D. pulex* genome also has the potential to contribute to resolving long-standing debates on arthropod phylogeny. Current views of arthropod phylogenetic relationships are based mainly on two types of datasets - molecular genetic data and morphological characters - and this has led to partly contradictory concepts of arthropod phylogeny. There is now almost universal agreement that arthropods derive from a common ancestor, and that crustaceans and insects are sister groups \[13\]. However, some issues of arthropod relationships remain controversial, for example the question of whether insects, crustaceans and myriapods form a monophyletic group. Crustaceans show the greatest diversity of body organization and development among arthropods \[14\] and therefore the phylogenetic relationships within the crustaceans are far from being resolved. Several morphological and molecular studies have questioned the monophyly of crustaceans, and either Branchiopoda (such as *Daphnia*) or Malacostraca (lobster, shrimps) has been hypothesized to be the sister group to insects \[15\]. Some recent molecular analyses suggest a sister group relationship of myriapods (millipedes) and chelicerates (spiders) \[16\]. Interestingly, this suggestion is supported by recent morphological and molecular studies on the development of the nervous system that reveal a surprising degree of similarity between myriapods and chelicerates \[17,18\]. The morphological support for an insect-crustacean sister-group relationship is mainly based on the comparative analysis of neural characters in higher crustaceans (malacostracans) and insects. For example, in both insects and malacostracans, stem-cell-like neuroblasts have been detected that divide asymmetrically to generate the cells that contribute to the nervous system \[14\]. But are these neural characters representative of all crustacean groups? Are homologous genes required for the development and the function of the nervous system? With the availability of a branchiopod genome and the development of genetic tools for *Daphnia* these questions can now be addressed.

Furthermore, using genome sequences of a wide range of organisms, the origin and evolution of neural signaling pathways can be traced, which will broaden our understanding of the evolution of nervous systems. The neurotrophin signaling pathway plays a role in neural development, regeneration and neural plasticity in mammals. Analyzing the *Daphnia* genome, Wilson \[11\] shows for the first time that a neurotrophin and both a tyrosine receptor kinase (Trk) and a p75-type neurotrophin receptor (p75NTR) are present in a protostome, indicating that this pathway existed in the last common ancestor of protostomes and deuterostomes.

To conclude, the initial exploration of the *D. pulex* genome outlined above proves that with the availability of the genome sequence *Daphnia* research has entered a new era. New and long-standing questions in ecology and evolution can be addressed and it may finally be possible to link evolutionary and environmental adaptations to the underlying genetic processes.

## Acknowledgements

I am grateful to Dieter Ebert for discussions and comments on the manuscript and I thank Petra Ungerer for the scanning electron micrograph of *Daphnia*. The work was supported by a grant to AS from the BBSRC.


abstract: Technical wizardry has made possible the combination of two powerful methodologies, optical trapping and single-molecule fluorescence, to measure precisely the force required to unzip DNA.
author: Jonathan B Weitzman
date: 2003
references:
title: A marriage of techniques

Combining scientific techniques that measure different parameters can create powerful and effective experimental methodologies. Indeed, many scientific advances are driven by the success of new technological achievements. But marrying techniques that might have very distinct technical requirements is often extremely challenging. The article by Matthew Lang and colleagues in this issue of the *Journal of Biology* \[1\] describes the first successful combination of **optical trap** and **single-molecule fluorescence** technologies in a single experiment and on a single molecule (see 'The bottom line' box for a summary of the work and the 'Background' box for definitions).

These two key technologies, optical traps and single-molecule fluorescence, have dominated the field of single-molecule research ever since they were independently developed over 15 years ago. Optical traps use the radiation pressure from a focused laser beam to trap and move single molecules. They can generate forces in the piconewton range, can measure displacements of nanometers, and have been creatively applied to the study of molecular motors such as kinesins moving along microtubules and to actin-myosin dynamics. Attaching fluorophores to biological macromolecules (protein or nucleic acids) allows investigators to image individual molecules by fluorescence microscopy. Single-molecule fluorescence has provided insights into many biological systems; for example, pioneering studies by Toshio Yanagida and colleagues in Osaka, Japan, investigated the kinetics of myosin binding to ATP using Cy3-labelled fluorescent ATP analogs \[2\]. Modern single-molecule fluorescence applications include **FRET (fluorescence resonance energy transfer)**, to measure nanometer distances between different fluorescent moieties, and fluorescence quenching.

It has been clear for some time that combining optical trapping and single-molecule fluorescence would offer a powerful approach to monitoring spatial or conformational changes in a temporal manner at the single-molecule level. But several inherent features of the two techniques have prevented this marriage. "The infrared optical trapping light is extremely intense", explains Steven Block of Stanford University, senior author of the *Journal of Biology* article. In contrast the single-molecule fluorophores have a relatively weak signal. The flux of the high intensity optical traps causes photobleaching of the fluorophores, and any leakage into the light channel drowns out the fluorescent signal. This probably explains why previous attempts to combine optical traps and single-molecule fluorescence failed. Some studies have circumvented these difficulties by sequential use of optical trap and single-molecule fluorescence, or the use of beads or microneedles to spatially separate the trap from the fluorophore \[3\]. But these solutions create their own constraints on experimental design and interpretation.

Lang *et al*. managed to overcome these technical problems and apply the two techniques simultaneously to measure the force required to prize apart two strands of DNA. They used complementary DNA strands labeled with rhodamine dye; one of the DNA strands was attached to a polystyrene bead and the other to a glass coverslip. The fluorescence of the dyes was quenched as a result of their proximity. The bead was trapped by the laser beam and the stage moved so as to 'unzip' the DNA strands. As the two strands are ripped apart the fluorophores separate, relieving the auto-quenching and releasing a short burst of light.

"It's a technical *tour de force*", says Brandeis University's Jeff Gelles. "This is an important technical development because it integrates two technologies that need to be done in very different conditions." Although there are lots of groups using optical traps and lots using single-molecule fluorescence, few have the technical know-how to overcome the challenge of combining them. "The Block lab has always been a leader in showing us all how to do these things," says Gelles. Block explains that his group's success required bringing together expertise from several fields. He says "It has all been made possible by extremely careful optical design, the use of special filters, a judicious choice of fluorescent dyes and the speed gained by automating many aspects of the experiment." (See the 'Behind the scenes' box for more of the background to the work.)

Now that combined single-molecule fluorescence and optical trapping has been achieved, single-molecule researchers are predicting that it will find applications in a wide range of fields. In fact, it may be useful in any system that explores the conformational changes and biochemical steps involved in macromolecular function. Recent applications of optical trapping include studies of catalytic RNA functions, the chemical properties of DNA and RNA molecules, protein folding and the activities of DNA polymerases. And the combined optical trap and single-molecule fluorescence methodology will enhance the traditional single-molecule study of proteins such as the molecular motors kinesin and myosin. The marriage of optical trapping and single-molecule fluorescence opens up a wealth of new potential applications. Now that the two are finally together, the months ahead should prove to be an exciting honeymoon period for single-molecule research.

## Figures and Tables


abstract: # Background
  .
  There is considerable interest in the development of methods to efficiently identify all coding variants present in large sample sets of humans. There are three approaches possible: whole-genome sequencing, whole-exome sequencing using exon capture methods, and RNA-Seq. While whole-genome sequencing is the most complete, it remains sufficiently expensive that cost effective alternatives are important.
  .
  # Results
  .
  Here we provide a systematic exploration of how well RNA-Seq can identify human coding variants by comparing variants identified through high coverage whole-genome sequencing to those identified by high coverage RNA-Seq in the same individual. This comparison allowed us to directly evaluate the sensitivity and specificity of RNA-Seq in identifying coding variants, and to evaluate how key parameters such as the degree of coverage and the expression levels of genes interact to influence performance. We find that although only 40% of exonic variants identified by whole genome sequencing were captured using RNA-Seq; this number rose to 81% when concentrating on genes known to be well-expressed in the source tissue. We also find that a high false positive rate can be problematic when working with RNA-Seq data, especially at higher levels of coverage.
  .
  # Conclusions
  .
  We conclude that as long as a tissue relevant to the trait under study is available and suitable quality control screens are implemented, RNA-Seq is a fast and inexpensive alternative approach for finding coding variants in genes with sufficiently high expression levels.
author: Elizabeth T Cirulli; Abanish Singh; Kevin V Shianna; Dongliang Ge; Jason P Smith; Jessica M Maia; Erin L Heinzen; James J Goedert; David B Goldstein
date: 2010
institute: 1Center for Human Genome Variation, Duke University School of Medicine, Box 91009, Durham, NC 27708, USA; 2Infections and Immunoepidemiology Branch, Division of Cancer Epidemiology and Genetics, US National Cancer Institutes of Health, 6120 Executive Boulevard, Rockville, MD 20852, USA
references:
title: Screening the human exome: a comparison of whole genome and whole transcriptome sequencing

# Background

The study of common human diseases is rapidly moving away from an exclusive focus on common variants using genome-wide association studies and toward sequencing approaches that represent most variants, including those that are rare in the general population.

Although rapidly falling, the per base costs of next generation sequencing platforms still preclude the generation of large sample sizes of entirely sequenced genomes at high coverage. In addition to this economic constraint, it is widely appreciated that the very large number of variants identified in such studies will make it difficult to use association evidence alone to identify causal sites. For these reasons, there has been considerable interest in focusing attention on coding variants as a first step at complete representation of human variation. Part of the motivation for this approach stems from the experience with Mendelian diseases, in which 59% of the causal variants are either missense or nonsense mutations \[1\]. Although there has been considerable speculation on the topic, there are in fact no solid data showing that the picture is any different for common diseases, which may also be influenced by variants that are in or near protein coding sequence \[1\].

The most comprehensive approach for focusing on exons alone is clearly exome capture, where regions matching a defined set of coding exons are pulled from the genomic DNA (gDNA) using microarrays and then sequenced. However, this approach requires an initial and costly hybridization step. The cost of exome sequencing has contributed to the interest in sequencing the transcriptome (RNA-Seq) as an alternative, and possibly easier and less expensive strategy \[2\]. While this approach will clearly miss poorly expressed genes in whatever tissue is being studied, it does have the advantage of generating additional information, such as gene expression level and splicing patterns.

Although exome capture was demonstrated to identify approximately 95% of genomic single nucleotide variants (SNVs) in curated and non-paralogous exons \[3\], it is not currently known to what extent SNVs identified by RNA-Seq capture the full set of exonic SNVs identified by genomic sequencing. If the ability to capture SNVs by RNA-Seq is highly dependent on expression level, then this method would be useful only when performed in the appropriate tissue type. If, on the other hand, RNA-Seq at high coverage allows SNVs to be captured even in genes that are not highly expressed, then both methods could be useful for opening up sequencing studies to larger datasets in more diverse scientific studies.

Here, we have sequenced the entire genome and transcriptome of a single individual to high coverage. By comparing the SNVs identified in the transcriptome at different levels of coverage to those identified in the gDNA, we are able to directly evaluate how well RNA-Seq captures genomic variants.

# Results

## Alignment and coverage

Both DNA and RNA were extracted from peripheral blood mononuclear cells (PBMCs) from the same individual. Both the cDNA and gDNA were sequenced using the Illumina Genome Analyzer II. Sequencing of the gDNA produced 1,450 million reads, each 75 bp long. Ninety percent of these reads were aligned to the human reference genome by BWA \[4\], and after removing potential PCR duplicates, the remaining 980 million reads produced a coverage of at least 5× for 94% of the bases in the genome (gaps in reference genome excluded), and the mean coverage for these bases was 24×.

Sequencing of the cDNA produced 280 million reads, half 75 bp long and half 68 bp long. TopHat \[5\] was used to align these reads to the reference genome, with exons and splice junctions restricted to the 45,455 protein-coding transcripts annotated in Ensembl version 50. Sixty-nine percent of these reads gave unique alignments, aligning to exactly one location in the specified transcriptome. Reads aligning to more than one location were discarded. After removing potential PCR duplicates, the remaining 81 million reads produced a mean coverage of above 5× for 51% of exons: these exons had a median coverage of 51×.

## Single nucleotide variants and overlap between datasets

We used SAMtools to call SNVs in our aligned gDNA and cDNA sequences. Indels and large structural variants were not analyzed. SAMtools called 51,055 SNVs in protein-coding exons in gDNA and 64,128 in cDNA. Of these, 48,740 in gDNA and 40,605 in cDNA passed quality control filters, and 19,054 of these overlapped between the two datasets in terms of position. When considering overlap between cDNA and genomic SNV calls, two measures were examined: sensitivity and specificity. Sensitivity was defined as the number of true positives (SNVs overlapping between the two datasets) divided by the number of true positives plus the number of false negatives (SNVs existing in the gDNA but not the cDNA). Specificity was defined as the number of true positives divided by the number of true positives plus the number of false positives (SNVs existing in the cDNA but not the gDNA). Quality control filters were optimized to maximize both the sensitivity and specificity in this study. In this dataset the sensitivity was 0.39 and the specificity was 0.47. If an exact match of the genotype was required as well as location, then the sensitivity fell to 0.35 and specificity to 0.42.

SNVs called in the gDNA and cDNA were also compared with entries in dbSNP. It was found that 90% of the gDNA exonic SNVs corresponded to a dbSNP entry, while this was true of only 56% of the cDNA SNVs. However, a further breakdown revealed that 94% of the true positive cDNA SNVs corresponded to a dbSNP entry, while only 23% of the false positives did the same. The false negatives corresponded to dbSNP entries 89% of the time.

## SNV identification at different levels of expression and coverage

Many of the exons in Ensembl's transcript library are hypothetical and not confirmed to be expressed. A list of core exons as defined by the Affymetrix (Santa Clara, California, USA) Human Exon 1.0 ST Array was utilized to focus on exons with better curation. This list was further screened to only include exons present in Ensembl's list of canonical transcripts, resulting in 172,739 core exons. When focusing on just core exons, sensitivity and specificity rose to 0.44 and 0.55, respectively (Figure 1), which coincided with the percentage of exons having at least 5× coverage rising to 61% (median coverage for these exons was 57×).

We then evaluated how the number of reads and the level of expression affected the specificity and sensitivity of cDNA sequencing. Using data from previous studies on the level to which most of the transcripts containing these core exons are expressed in PBMCs \[6\], we defined expression level for each transcript as a percentage of the most highly expressed transcript in that tissue. For exons in our dataset the sensitivity and specificity both rise as known PBMC expression increases, until an expression level of 4% of the most highly expressed transcript, at which point both measures asymptote, with variants called about equally well for all expression levels above this (Figure 2). Ninety-four percent of exons from genes above this expression level, or 'PBMC-expressed genes', had at least 5× coverage, and the median coverage for exons with at least 5× coverage was 126×. The sensitivity also rose to 0.81 and the specificity to 0.67.

We also evaluated how the absolute number of true positive SNVs called depends on the amount of sequence data, in lanes, for all exons and for exons from PBMC-expressed genes (Figure 3). Seventy-nine percent of the 6,434 true positive variants identified in PBMC-expressed genes were identified with even one lane of sequence data, which is approximately 35 million reads in this instance. The total number of variants identified in all genes, however, increased substantially as more lanes were added. For the approximately 4,500 PBMC-expressed genes (Figure 4), even a single lane can be expected to capture most of the coding variants present.

We also found that the percent overlap with dbSNP changed as expression level and coverage changed. Although the percentage of SNV calls with a corresponding dbSNP entry remained relatively stable at all expression and coverage levels for the true positives and false negatives in our dataset, this was not true of the false positives. The percentage of false positives that overlapped with dbSNP decreased as coverage increased (Supplemental figure S3 in Additional file 1) and increased as expression level increased (Supplemental figure S4 in Additional file 1).

## SNV identification in genes with and without paralogs

An inspection of false positive SNVs identified in the cDNA revealed that some arose from alignment of a read to the wrong gene. In these cases the correct gene and the gene chosen for alignment always had very similar sequences. To determine if specificity would increase in a set of unrelated genes, SNVs were called separately for two groups of genes: those with paralogs, as annotated by Ensembl, and those without. When SNVs were restricted to exons in 12,124 transcripts from genes without annotated paralogs, the overall sensitivity rose from 0.39 to 0.42 and the specificity rose from 0.47 to 0.54. If restricted to PBMC-expressed genes without paralogs, the sensitivity actually dropped slightly from 0.81 to 0.80, but the specificity again rose from 0.67 to 0.72. In contrast, for SNVs in exons of 33,331 transcripts from genes with paralogs, the sensitivity was 0.38 and the specificity was 0.45 (sensitivity 0.81 and specificity 0.65 in PBMC-expressed genes with paralogs).

## Single nucleotide variant identification at different read depths

We studied the effect of read depth on specificity by examining the read depth at individual SNV calls in our complete RNA-Seq dataset of eight lanes. We found that at a read depth of 3 (the minimum required for a variant to be called), the specificity for SNVs found in the complete set of exons was only 0.28, but that as read depth increased, so did specificity, until it reached a plateau of between 0.6 and 0.75 for read depths between 50 and 1,200 (Supplemental figure S1 in Additional file 1). The 13,892 SNVs called between these read depth levels had a specificity of 0.67 (sensitivity of 0.19). However, above a depth of 1,200, the specificity fell again, becoming as low as 0.05 for the 94 SNVs with read depths greater than 2,000. Similar trends were found for core exonic SNVs (sensitivity 0.22 and specificity 0.77), SNVs in PBMC-expressed genes (sensitivity 0.59 and specificity 0.84), and SNVs in PBMC-expressed genes without paralogs (sensitivity 0.58 and specificity 0.90) when restricting to SNVs with a read depth between 50 and 1,200 (Supplemental figure S1 in Additional file 1).

We also studied the effect of read depth on specificity as the dataset increased from one to eight lanes. We found that at only one lane of RNA-Seq data, the specificity was 0.5 for SNVs with a read depth of 3, which was far better than the value of 0.28 found when using all eight lanes of data. Again, the specificity increased as the read depth increased, but in this lower coverage dataset the specificity reached a plateau of between 0.6 and 0.75 at greater than 10 reads, far earlier than the 50 reads needed for specificity stability in the eight lane dataset. The specificity also decayed at a much lower read depth in this smaller dataset, becoming less than 0.6 when the read depth was greater than 150. As the dataset increased from one lane to all eight lanes, the overall specificity continually decreased (Figure 1), and the minimum and maximum read depth value required for specificity to remain stable continually increased (Supplemental figure S2 in Additional file 1).

# Discussion

If one simply considers all coding SNVs, our study suggests that about 40% can be identified by RNA-Seq using PBMCs as the RNA source. If we focus, however, on only PBMC-expressed genes, we find that 81% of coding variants are identified. This suggests that RNA-Seq may be a workable alternative for identifying exonic variants when performed in the appropriate tissue for the trait of interest.

One limiting factor in variant identification by RNA-Seq is the ability to uniquely align a given read. Although we were able to align 78% of our reads to a location in the transcriptome, only 69% aligned to exactly one location and could be kept in the analysis. Because exons are some of the most conserved regions of the genome, without the help of intervening and variable intron sequences it is much harder to align a read to the correct gene, and especially to have it align to only one location. This limits one's ability to identify variants in certain genomic locations, lowering the sensitivity of this method. Furthermore, if a read is uniquely aligned to the wrong gene, such as a paralog, then this can result in false positive SNVs being called in the cDNA. Restricting SNV calls to genes without paralogs did increase specificity from 0.47 to 0.54 and sensitivity from 0.39 to 0.42.

Another limiting factor is coverage. Because genes are expressed at different levels, in the random sampling of transcripts that are sequenced there will be gross imbalances. Some transcripts will have more than 1,000-fold coverage while other transcripts, although also expressed to some level in that tissue, will have coverage that is too low for variants to be accurately called. Given the diminishing returns of additional sequencing in terms of variants called (Figure 3), it simply will not be possible to use RNA-Seq to capture all exonic variants in genes with low expression levels. RNA-Seq is most useful for identifying variants in a tissue type that highly expresses genes related to the trait under study. One interesting possibility to improve the proportion of SNVs that can be called would be to use more than one tissue type as a source for the RNA. For example, data available on expression of core exons in muscle shows that adding cDNA from this tissue to PBMC cDNA would increase the number of adequately expressed transcripts by 68% \[6,7\]. There are no expression data for a more easily accessible tissue such as skin for this exon array publicly available; however, one can extrapolate that adding cDNA from almost any tissue would be similarly beneficial to analysis.

A large number of false positive SNVs were identified in this dataset: even when restricted to PBMC-expressed genes without paralogs the specificity was only 0.72. Many of these false positives, however, were due to the very high coverage produced in this study. At low levels of coverage, reads that could produce false positive calls are not yet abundant enough to pass the quality control filters used, and the specificity remains high. However, as more reads are added to the dataset, the number of incorrect alignments and sequencing mistakes increases, pushing more and more of these false positive calls over the quality control filters. Thus, the more coverage that is added in the search for true positives, the more false positives that will appear in the data. We found that the specificity for the dataset as a whole could be increased from 0.47 to 0.67 (and from 0.72 to 0.9 for PBMC-expressed genes without paralogs), at a substantial cost to sensitivity, by restricting the permissible read depth range for SNV calls. The specificity was low at low read depths, as is expected when a call is supported by less data; interestingly, the minimum read depth required for high specificity increased as coverage increased, supporting the view that high coverage introduces more noise and comes with a requirement for stricter quality control. An additional support to this view was the finding of low specificity at very high read depths. Both a minimum and a maximum read depth cutoff are advisable for increasing the specificity.

Some of the false positives found in this dataset are certainly due to the incorrect alignment of reads, as even when a gene has no annotated paralog it can have sections with sequence similarities to other genes that permit errors during alignment. Another alignment problem that is unique to RNA-Seq is incorrect alignment at the very ends of a read due to splicing. Because reads that span exons require a certain number (four in our study) of bases to fall on both sides of the exon-exon boundary for proper alignment, reads that cross an exon boundary at the very edge of the read will have between one and three bases aligned to the wrong location. Our study removed most of this type of false positive SNV during the quality control process. Another scenario that would produce a seemingly false positive SNV would be sufficient coverage in the cDNA to call the variant but insufficient coverage in the gDNA sequencing to do the same: however, in our study such bases had a median coverage of 27× in the gDNA, which does not support this explanation. Also, previous studies have shown that variants can be present in the RNA but not the gDNA due to RNA editing \[2,8\].

There are also likely to be disagreements between gDNA and cDNA sequencing stemming from expression differences. For example, some SNVs found in the genomic sequence may be missed in the cDNA due to expression balances, when an individual is heterozygous for a given SNV yet the reference allele is much more highly expressed. Also, an SNV may be called heterozygous in the gDNA but homozygous in the cDNA due to expression imbalances. In our dataset, 10% of the 19,054 SNVs that overlapped by location between gDNA and cDNA were heterozygous in gDNA and yet homozygous in cDNA. Differences in zygosity between the two methods can also result from insufficient coverage in one dataset or the other: for example, 1% of these 19,054 SNVs were homozygous in gDNA and yet heterozygous in cDNA, and the median coverage for these SNVs in gDNA was only 12×, compared to 28× for SNVs that matched for zygosity. It is likely that many of the discrepancies where the SNV was heterozygous in gDNA and homozygous in cDNA also resulted from low coverage, as the median coverage for this group was only 8× in the cDNA. It should also be noted that only 13 of the 19,054 SNVs that overlapped by location had completely mismatched alleles, such as the cDNA being homozygous for a G and the gDNA homozygous for C when the reference allele was A.

Our study found that although the percent overlap of our false positive SNVs with dbSNP entries was far less than that of true positive (94%), false negative (89%), or all exonic gDNA (90%) SNVs, it was still substantially greater than zero (23%). Furthermore, we found that the percent of false positives corresponding to dbSNP entries increased as the PBMC expression level increased (Supplemental figure S4 in Additional file 1), implying that the 'false positives' seen at high expression levels may actually be true positives that were simply not seen in the gDNA due to issues with coverage or alignment or because of RNA editing. Additionally, we found that only 13% of the false positive SNVs found in dbSNP had been genotyped in HapMap, compared with 69% of the true positives found in dbSNP. This suggests that many of the dbSNP entries matching false positives are less well-curated, with less supporting evidence. A brief inspection of a subset of the SNVs showed that false positives were more likely to have cDNA evidence (as opposed to gDNA evidence) supporting their dbSNP entry than were true positives; this could be a reflection of either misalignment of the cDNA reads for dbSNP entries, or RNA edits that would not be seen in the gDNA. Finally, we found that the percent overlap of false positives with dbSNP decreased as the number of lanes of sequence data increased (Supplemental figure S3 in Additional file 1). This finding corresponds with the fact that the overall specificity decreased as coverage increased (Figure 1); these phenomena are likely caused by the increasing ability of the random noise inherently present in the data to overcome quality control cutoffs as more and more reads were added, as described above.

Two previous studies have also looked at using RNA-Seq to identify SNVs. Chepelev *et al*. \[2\] used a dataset of 27 million uniquely aligned 30-bp RNA-Seq reads; while they detected 50% of known exons with at least 1× coverage, and identified approximately 11,000 SNVs, they did not compare these data to gDNA sequence and thus could not calculate sensitivity or specificity of their methods. Shah *et al*. \[8\] used a dataset of 183 million RNA-Seq reads of 38.9 bp each, 55 million of which aligned to exons or exon junctions. They compared these data to 2.5 billion aligned gDNA reads of 48.2 bp to better understand the evolution of mutation in a lobular breast tumor. Although they showed that the number of SNVs called through RNA-Seq increased as the number of reads increased, they did not discuss the sensitivity or specificity of SNV calls when compared to whole genome sequencing, nor did they analyze how the number of SNVs changed as known expression level changed.

Another technology that has been used to sequence coding variants is exome capture. Because this method sequences reads that are from gDNA but enriched for the portions of interest, there are no complications with aligning splice junctions or being limited by expression level. A recent study showed that when restricted to the non-paralogous exons of 16,496 curated protein coding genes, exome capture utilizing 41 million 76-bp reads (one quarter the number of bases we aligned) captured SNVs with a sensitivity of 95% and a specificity of 90% \[3\]. Although this performance outshines the RNA-Seq data presented here, RNA-Seq does have some advantages beyond the ability to inexpensively genotype exonic SNVs. It can also be used to identify expression differences between individuals or even between alleles within an individual, which can lead to discovery of a nearby causal variant even if it is not exonic. RNA-Seq may also provide insight into novel exons, splice junctions or splice forms in the tissue or cell type being studied that might not be recognized as protein coding in genomic sequencing, or captured with targeted exomic sequencing.

While it is most useful to perform RNA-Seq in a tissue relevant to the trait under study, other tissues can also be of some use. For example, data from Heinzen *et al*. \[6\] revealed an r^2^ of 0.23 between transcript expression in PBMCs and in brain, and that 63% of the transcripts highly expressed in brain were also expressed in blood to a level that allowed for consistent SNV detection by RNA-Seq (defined as expression level of at least 4% of the most highly expressed transcript in both).

# Conclusions

Here we show that RNA-Seq captured 81% of the exonic variants from genes that were well expressed in the source tissue. Although its usefulness is limited to these genes, the cheaper cost involved, as well as the extra information gained about expression and splice variants, may make this method a workable alternative to genomic sequencing or exome capture for groups that have access to the right types of tissue.

# Materials and methods

## Sample preparation and sequencing

DNA was extracted from PBMCs using the QIAGEN Autopure LS (Venlo, The Netherlands). RNA was extracted from viable PBMCs using the Qiagen RNeasy kit. The DNA was prepared for sequencing according to Illumina's gDNA sample prep kit protocol: randomly fragment the DNA by nebulization, end repair, add a single A base, adaptor ligation, run a gel to isolate 300-bp fragments, and PCR amplification. The total RNA was prepared according to the Illumina RNA seq protocol: briefly, globin reduction, polyA enrichment, chemical fragmentation of the polyA RNA, cDNA synthesis, and size selection of 200-bp cDNA products. Next, the size-selected libraries were used for cluster generation on the flow cell. All prepared flow cells were run on the Genome Analyzer II using the paired-end module: nine flow cells (with eight lanes each) for the gDNA and one flow cell for the cDNA. The paired-end reads for the gDNA were each 75 bp long, although one flow cell only produced single reads of 75 bp each. Due to a machine error near the end of the read 1, the paired cDNA reads were not able to be matched to each other and read 1 was only 68 bp long while read 2 was 75 bp. The reads are available in the NCBI Sequence Read Archive \[9\], under study ID SRP001691. The Illumina GA Pipeline version was 1.4.0. This pipeline produced the quality score for each nucleotide in standard Illumina format where the base was 64 (Illumina quality score = Qphred +64, where Qphred = -10log10(e) and e = estimated probability of a nucleotide being wrong). We converted the quality scores for each nucleotide to standard Sanger fastq format, where the base was 33.

## Alignment and single nucleotide variant identification

gDNA was aligned to the reference genome (NCBI build 36 Ensembl release 50) using the BWA software (version 0.4.9) \[4\]. cDNA was aligned to the reference genome using TopHat \[5\]. The -GFF function utilized a transcript library downloaded from Ensembl to specify known protein-coding transcripts and splice junctions, and the library was screened to remove contigs and mitochondrial DNA. The --no-novel-juncs option was used to restrict alignment to those exons and splice junctions included in this transcript library. To assist in alignment to small exons, the 75-bp reads were broken down into three 25-bp segments (68-bp reads into two 34-bp segments), which were then joined back together after being individually aligned. Two mismatches were permitted per 25-bp (or 34-bp) segment, and no mismatches were permitted in the 4-bp anchor region on either side of a splice junction. Introns were permitted to range in size from 10 bp to 500 kb. Only unique alignments were kept: that is, reads that aligned to exactly one location. Reads mapping to multiple locations were excluded using in-house software.

SAMtools (version 0.1.5c) was used to remove potential PCR duplicates via the rmdup (paired reads) and rmdupse (single reads) command \[10\]. It was also used for SNV identification, using the pileup command with the -c option and default settings. The SNVs were then filtered using SAMtool's variation filter with the default settings but removing the filter for a maximum allowed coverage per variant by setting it to 10 million for gDNA and 1 million for cDNA. SNVs lying outside exons as defined by the transcript library were removed. Indels were not considered. All SNVs were further screened for quality by only keeping those above a minimum SNP quality score: 30 for cDNA and 20 for gDNA. This score is calculated by SAMtools and is the Phred-scaled probability that the base at that location is identical to reference, with higher scores being less likely to be reference. SNVs were also excluded if there were fewer than three reads supporting the non-reference allele. cDNA SNVs were further screened to exclude all SNVs where more than 20% of the reads supporting the non-reference allele were from the first or last base of a sequence read.

## Coverage

Coverage of cDNA sequencing for each exon was calculating using in-house software. For each exon in the transcript library, coverage was calculated as the average number of reads covering each base within that exon.

## Paralogous genes

Genes were designated as paralogous using ENSG IDs as input in Genecards' paralog finder \[11,12\]. The list of paralogs from Ensembl was utilized, and genes were split into two groups: those with paralogs and those without. The 42 ENSG IDs not recognized by Genecards were individually examined for paralog status using Ensembl directly.

# Abbreviations

bp: base pair; gDNA: genomic DNA; PBMC: peripheral blood mononuclear cell; SNV: single nucleotide variant.

# Authors' contributions

ETC participated in the design of the study, performed analyses, and drafted the paper. AS performed analyses and processed the cDNA reads. KVS supervised the sequencing of gDNA and cDNA. DG performed analyses and processed the gDNA reads. JPS sequenced the gDNA and cDNA. JMM performed analyses and processed the gDNA reads. ELH provided expression data. JJG collected the cohort, prepared the samples, and reviewed and edited the paper. DBG designed and supervised the study and helped to write the paper. All authors read and approved the final manuscript.

# Supplementary Material

###### Additional file 1

**Supplemental figures S1 to S4**. Showing the specificity at different read depth levels and the overlap with dbSNP entries at different coverage levels and different expression levels.

Click here for file

## Acknowledgements

Funding was provided by the NIAID Center for HIV/AIDS Vaccine Immunology grant AI067854 and the Bill and Melinda Gates Foundation grant 157412. We also acknowledge C Gumbs, K Cronin and L Little for DNA and RNA extraction.