Progress in genome-wide association studies of age at natural menopause

Menopause not only represents the end of a woman's reproductive lifespan, but is also closely related to her health. Traditional epidemiological data show that early menopause can increase the risk of abnormal lipid metabolism, fractures, atherosclerotic cardiovascular disease, osteoporosis and other diseases, and can increase female all-cause mortality (). Studies on age at natural menopause (ANM) are helpful for women to understand their fertility potential and disease risk, to plan their reproductive life and actively prevent related diseases, and to improve their quality of life. Heredity is an important factor affecting ANM. With the rapid development of genetic epidemiology over the last 10 years, genome technology combined with epidemiology has been widely applied in ANM and related diseases. This review collates the historical progress of genome-wide association studies (GWAS) on ANM and explores the impact and significance of ANM-related gene changes on women's health.

Menopause is defined by the World Health Organization (WHO) as the cessation of menstruation for more than 12 months (; ). Normal menstruation is the result of ovulation from the dominant follicle. During the embryonic stage, the oocyte in the fetal ovary stops mitosis, enters the meiosis stage and stops at the prophase of meiosis I (). There is essentially a continuous process of oocyte depletion during a female's life. The American Society for Reproductive Medicine (ASRM) defines the quantity and quality of remaining oocytes as the ovarian reserve (). The decline in ovarian reserve is positively correlated with age. If the rate of decline is in the age-specific range, it is called normal physiologic ovarian ageing (NOA). Compared with same-age women, decreased fertility associated with ovarian function but regular menstruation is suggestive of diminished ovarian reserve (DOR). In the event of further amenorrhoea and a rise in FSH, early menopause can be considered if it occurs earlier than 45 years of age, while premature ovarian insufficiency (POI) or premature ovarian failure (POF) can be considered if it occurs earlier than 40 years of age. Age at menopause is crucial for assessing the decline in ovarian reserve and determining the diagnosis (). Spontaneous POI/POF is considered to be a special condition of ANM, which is the extreme of earlier menopause age and is defined to be a disease with abnormal menopause age. Patients with POF commonly experience POI before their ovaries fail entirely. In 2015, the ESHRE defined POI as oligo/amenorrhoea for more than 4 months before the age of 40 and FSH above 25 IU/l twice within 4 weeks, a diagnostic threshold lower than the 40 IU/l that defines POF ().

The oldest and most cited classic study of the incidence of POF was the ANM study of 1858 women in Rochester, Minnesota, USA, in 1986. The survey found that the annual incidence of natural menopause between the ages of 15 and 29 years old was 10/100,000 person-years. For those aged 30–39 years old, it was 76/100,000 person-years. In the population aged 40–44 years old, it was 881/100,000 person-years. Consequently, the prevalence of POF was about 1% (). In subsequent studies, it came to be recognized that there were racial differences in ANM. A prospective multiracial study of 11,652 women indicated significant racial differences in the prevalence of POF, as low as 0.1% in Japanese, 0.5% in Chinese, about 1% in European, and about 1.4% in African-American and Hispanic-American populations (). As early as 1997, some scholars proposed that the odds ratio (OR) of premature menopause in daughters was 6.02 (95%CI: 3.39–10.66) if the mother had a premature menopause (). In 2007, a study investigated the prevalence of POF in 832 female twin-pairs from Australian and UK twin research databases and revealed a stronger ANM association in monozygotic twins than in dizygotic twins (). These studies suggested a significant genetic effect of ANM. In 2011, the Breakthrough Generations Study in Britain used logistic regression, a variance component model and other statistical methods to study the heritability of ANM in 2060 people. The results showed that among the factors causing a difference in menopause age, genetic effects accounted for about 42% and shared environmental factors accounted for about 14% (). These studies tell us that heredity is a major determinant of ANM.

The traditional indicators of ovarian reserve can only indicate abnormalities when approaching menopause, which has some limitations. With the development of genetic research, genetic technology has promoted research into ANM. In 1999, a study of 900 post-menopausal women aged 55–80 years old showed that the Pvu II polymorphism in the oestrogen α receptor (ESR1) gene was associated with menopausal age (). Subsequent studies focused on candidate genes for the oestrogen pathway. In 2003, a study involving 317 menopausal women over 46 years of age showed that the ESR1 Pvu II polymorphism did not cause differences in menopausal age or reproductive lifespan, and the CYP17 gene involved in oestrogen metabolism was only found to be related to age of menarche (). In 2005, a study of ANM in 385 people of European descent showed that neither the ESR1 Pvu II polymorphism nor the CYP17 gene was significantly associated with ANM (). Studies on the correlation between candidate genes and ANM yielded contradictory results. Some scholars proposed that the reason for this dilemma was that the genetic variation of ANM was not caused by single-gene mutation, and such complex clinical phenotypes had multi-gene effects ().

Spontaneous POI/POF and ANM have different genetic factors. Studies of POI/POF need to be conducted in women under 40 years old with amenorrhoea, not in the general population with natural menopause (). According to statistics, genetic factors account for 20–25% of all causes of POI, including chromosomal defects and gene defects (). In 2012, Jiao et al. reported the results of karyotype analysis of chromosome G bands in 531 patients with POF recruited from four medical centres in China. Of the 64 (12.1%) POF patients with abnormal karyotypes, 93.7% were characterized by X chromosome abnormalities, including number abnormalities, structural abnormalities and chromosome translocations (). In the study of gene defects, the number of candidate genes of POI/POF was higher than that of ANM. These mutant genes are located in X chromosomes, autosomal and mitochondrial. The X chromosome contains the most POI genes, including BMP15, FMR1, PGRMC1, AR, FOXO4, DACH2, etc. Candidate genes located in autosomes include FSHR, GDF9, NOBOX, PTEN, AMHR2, etc. (; ; ; ). Although single-gene techniques explored some genetic causes of POI, these candidate genes explain only a small fraction of the cause.

As the traditional candidate gene studies first adopted the hypothesis and then verified the method, this led to limitations in the studies of complex clinical phenotypes such as ANM, and the research methods on polygenic effects arose at that historic moment. With the emergence and rapid development of whole genome sequencing and exon sequencing, Nature published the second generation of the human genome haplotype map (HapMap) on 18 October 2007 (). It provided more sophisticated single nucleotide polymorphism (SNP) data for genome studies, and offered a way to compare large numbers of genes simultaneously and to verify genome-wide disease-causing associations. GWAS are a direct comparison of high-throughput genetic data between the population with clinical phenotypes and the control population, to reveal the genetic mechanism of clinical phenotypes. Unlike candidate gene research, this method does not need to put forward hypotheses in advance, but can directly find multiple different molecules, which greatly improves the research efficiency. The application of GWAS in research into the reproductive lifespan has become a new hotspot in the study of ovarian reserve.

In 2009, a GWAS combined data from the Rotterdam Study and the Twins UK Study on 2979 European women. The results suggested that six SNP on human chromosomes 19q13.4, 20p12.3 and 13q34 might be related to ANM, and the minor allele frequency (MAF) ranged from 0.12 to 0.48. BRSK1 (MAF = 0.39, P = 6.3 × 10^–11) might affect ovarian function by regulating AMPK-related kinase, which was associated with ANM advance. TMEM150B might be related to FGF receptor activation. MCM8 might be involved in DNA replication damage at the hypothalamic–pituitary and ovarian levels, thus regulating ovarian ageing (). In the same year, another large GWAS analysed the Nurses’ Health Study (NHS) and the Women's Genome Health Study (WGHS). This study identified 13 SNP clustered in five genes: 19q13.42/BRSK1, 20p12.3/MCM8, 5q35.2/RAP80 and HK3, and 6p24.2/SYCP2L (). In these two large GWAS in 2009, BRSK1 and MCM8 were replicated, but SNP on 13q34 was not.

The GWAS of ANM identified new loci by comparing SNP in a large sample population. The number of associated loci that GWAS can identify is related to the size of the sample. When the sample size of GWAS was increased to the threshold value, the number of new loci detected would increase significantly (). Therefore, the data sharing of multiple large cohorts can increase the number of gene loci detected by increasing the sample size. Based on this principle, public data from some large cohort studies were summarized and analysed. The Reproductive Genetics Consortium (ReproGen) is an international website that shares reproductive genetic data (https://reprogen.org/). In 2012, Stolk et al. laid the foundation for ReproGen's ANM dataset by conducting a large GWAS that meta-reviewed 22 studies of mostly women of European descent. The studies included AGES (Age, Gene/Environment Susceptibility Study), ARIC (Atherosclerosis Risk in Communities), CHS (Cardiovascular Health Study), deCODE (a commercial company from Iceland), EGCUT (Estonian Genome Centre University of Tartu), ERF (Erasmus Rucphen Family study), FHS (Framingham Heart Study), HAPI (Heredity and Phenotype Intervention) Heart Study, InChianti (Invecchiare in Chianti), NHS, QIMR (Queensland Institute of Medical Research), the Rotterdam Study, SardiNIA (GWAS of the Ogliastra region of Sardinia, Italy), SHIP (Study of Health in Pomerania), TwinsUK and WGHS. The total sample size reached 38,968, and the number of identified new loci increased to 17 ().

This GWAS, which combined multiple studies, also found that the effect of each allele ranged from 8.7 weeks to 50.5 weeks, explaining 2.5–4.1% of the variation in menopausal age. The associations in four of the 17 genes replicated the two 2009 GWAS, but the SNP on 13q34 were still unreplicated. Of the 13 newly discovered regions, eight genes (HELQ and FAM175A on chromosome 4, PRIM1 on chromosome 12, POLG and FANCI on chromosome 15, EXO1 on chromosome 1, UIMC1 on chromosome 5, TLK1 on chromosome 2) were involved in DNA repair. Three genes (IL11 and NLRP11 on chromosome 19 and BAT2 on chromosome 6) were associated with immune function. ASH2L on chromosome 8 was associated with X chromosome inactivation. There were four main functional networks within the ingenuity pathway analysis (IPA). One was the HNF4A centred network on ‘lipid metabolism, molecular transport and small molecule biochemistry’, which contained 14 genes and was suggested to be involved in diabetes. The second was ESR1, centred on the ‘cell cycle, cell death and tumour’ network, involving 12 genes that might be affected by oestrogen signalling. The third was the TNF and NF-κB related ‘cell death’ network. The fourth involved networks of ‘infection, DNA replication, recombination, repair and gene expression’. Gene-set enrichment pathway analyses (GSEA) suggested that ANM was associated with biological processes such as exodeoxyribonuclease (P = 0.0005), the NF-κB pathway (P = 0.0006) and mitochondrial dysfunction (P = 0.0001). The false discovery rate (FDR) of multiple hypothesis testing was <0.05. This study showed that ANM genes were mainly related to DNA damage response (DDR), and defects in the DNA repair mechanism resulted in decreased oocyte quality accumulated with age and increased follicle loss, which was an important cause of ovarian function decline. In addition, this study also emphasized that genes associated with autoimmune diseases could also affect ANM, such as BAT2 associated with type I diabetes and rheumatoid arthritis, whose missense mutation could cause inflammation in oocytes ().

The expanded GWAS substantially increased the sample size, but the average age of menopause in these studies ranged from 48.20 to 50.78 years. Most women experienced menopause at a normal age. In the same period as the ReproGen study, conducted another GWAS in 371 patients with POF and 800 control women in China. The mean age of secondary amenorrhoea in POF patients was 24.87 years. ESR1, BRSK1 and HK3 were found to be associated with POF. SNP associated with ANM might promote the occurrence of POF, but do not play a major role in it (). In addition, it is important to note that POF/POI and early menopause were defined as two diseases with different clinical traits, although both have early menopausal age. Studies have shown that FSH might rise more rapidly in patients with POI than early menopause, and patients with POI might have menarche earlier than early menopause (). A meta-analysis showed that the relative risks (RR) of all-cause death and ischaemic heart disease (IHD) in women with POI were 1.39 and 1.48, which were higher than those in women of normal menopausal age. The RR of IHD in early menopause women was 1.09, which was slightly higher than that in women with normal menopausal age, while all-cause mortality was not significantly different (). There might be differences in genetic variants among women with unexplained different menopause ages. In 2013, ReproGen continued to compare GWAS data from 3493 post-menopausal women under 45 years of age (early menopause and POI) with 13,598 post-menopausal women between 50 and 60 years of age (controls). This study calculated the OR values of the 17 ANM genes that were known to predict early menopause and POI. The OR value of early menopause ranged from 1.09 to 1.55, and the OR value of POI ranged from 1.04 to 1.17. All 17 genes of ANM were correlated with early menopause or POI, and early menopause and POI might be associated with the additive effect of different gene variants ().

Menopausal age and reproductive lifespan have different meanings. Menopausal age represents the end of reproductive life, while reproductive lifespan is the period from menarche to menopause. The reproductive lifetime in women with earlier ANM might not be changed due to earlier age at menarche, but might have left shift in reproductive lifespan. In 2014, through bivariate GWAS meta-analysis of age at menarche and natural menopause, ReproGen found that 6q21.32/PRRC2A and 2p16.3/MSH6 might cause left shift in reproductive lifespan, and each allele of MSH6 could reduce ANM by 1.3 months (MAF = 0.83). It was the 18th gene identified by the ReproGen Consortium to be associated with ANM advance, and its function was also associated with DNA repair defects ().

ReproGen expanded the sample size to 69,360 in 2015 and further identified 44 ANM-related genes with MAF ranging from 0.07 to 0.49 and effect size of each allele ranging from 0.07 to 0.88 years using a large-scale genetic analysis in women of European descent (). Three low-frequency variants associated with ANM were identified by exon array meta-analysis, including the organic anionic transporter gene 20q13.33/SLCO4A1 (MAF = 0.008) and two SNP (MAF = 0.036 and 0.025) on the DNA helicase gene 12q14.3/HELB. Pathway enrichment analysis revealed 29 genes in or near DDR regions. HELQ, MCM8, RAD51, MSH5, BRE, UIMC1, FANCI, RAS54L, DMC1, BRCA1, FAM175A and FBXO18 were most involved in homologous recombination after double strand break. UIMC1 and BRCA1 participated in DDR through the oestrogen α receptor. Other DDR mechanisms included DNA damage sensors BRSK1, DNA damage transducers and effectors CHEK2, mismatch repair genes MSH5 and MSH6, base excision genes APEX1 and PARP2, etc.

More ANM-associated genes were discovered, which may help reveal the genetic causes of ANM-associated diseases. In the expanded ReproGen GWAS in 2015, a partial genetic mechanism of some ANM-associated diseases such as breast cancer was identified using bioinformatics methods. STRING signalling pathway analysis showed that breast cancer gene BRCA1 is associated with 15 ANM genes, such as BRE, MSH6, POLR2H, FAM175A, UIMC1, RAD51, CHEK2, etc. The gene effect of ANM was positively correlated with the gene effect of breast cancer. In women with a menopausal age ≥55 years old, the breast cancer OR value was 1.06 (95%CI: 1.04–1.10) with P = 2.23 × 10^–7, while the OR value was 1.00 (95%CI: 0.97–1.05) with P = 0.95 in women with menopausal age ≤45 years. This explained why the younger the age at menopause, the lower the risk of breast cancer from the perspective of genetic epidemiology. Disease association analysis of POI also showed that recessive mutation of MCM8 was associated with primary amenorrhoea, pituitary amenorrhoea and hypothyroidism. Recessive mutation of EIF2B4 was associated with ovarioleukodystrophy and vanishing white matter syndrome. POLG mutation was associated with POI and neurological conditions. MSH5 was not only associated with POI, but also with many other diseases. TDRD3 was related to POI caused by pre-mutation of FMR1 (). The ReproGen Consortium study provided a wealth of genetic data for GWAS of reproductive longevity and sets the stage for the following research.

The studies performed by the ReproGen Consortium were a landmark in ANM research, but they were based on women of European descent. Whether these genetic loci can be replicated in other populations needed to be confirmed in different populations. In 2016, a study of reproductive lifespan in East Asia included 16,395 women from Shanghai in China and South Korea, and replicated part of the ANM genes identified in European descent. In view of the correlation between ANM and breast cancer, endometrial cancer and type II diabetes, the Shanghai Genome-wide Association Studies (SGWAS), including multiple related diseases genomic data of 8073 patients, were used to correlate ANM genes with diseases in the first phase of the project. A total of 1638 women aged 50 to 70 in the Nutrition and Health of Ageing Population in China (NHAPC), 2164 new breast cancer patients and 2051 control women in the Seoul Breast Cancer Study (SeBCS), as well as 2877 healthy control women in the Shanghai Women's Health Study (SWHS) were added in the second phase. 22q12.2/SFI1 was found in SGWAS as a novel ANM gene locus, and each allele could advance the age of menopause by 0.454 ± 0.089 years. SFI1 was found to regulate the dynamic structure of centrosome-related fibres by encoding a spindle assembly related sfi1 homolog. Five ANM genes of East Asian descent were copied among 20 ANM genes of European descent, namely TMEM150B, RHBDL2, UIMC1, NLRP11 and POLG (). TMEM150B was located on chromosome 19, near BRSK1, encoding transmembrane protein and participating in autophagy regulation. RHBDL2 was located on chromosome 1 encoding a serine kinase associated with epidermal growth factor receptor activation.

In 2018, the GWAS results of the BioBank Japan Project (BBJ), which included 67,029 women of Japanese descent, identified 16 ANM genes, of which eight were novel loci and different from those of European descent, suggesting widespread genetic differences in allele frequencies between Japanese and European populations. The eight novel gene loci were 10q24.1/CCNJ, 3q21.3/H1FX, 4p11/ZAR1, 8p21.2/GNRH1, 18q21.33/ZCCHC2, 8q24.11/RAD21, 4q23/EIF4E and 14q24.2/DCAF4. In terms of the analysis of cell and tissue type enrichment, significant enrichment was also found in the hypothalamic–pituitary–ovarian (HPO) axis involved by GNRH1 in addition to the DDR mechanism ().

Studies in women of Japanese descent showed ethnic heterogeneity of ANM genes, so further trans-ethnic studies need to be developed. However, genetic heterogeneity was also one of the difficulties in multi-population genome-wide meta-analyses, and so the GWAS meta-analysis method should be developed to increase detection ability. On the other hand, it was necessary to continue conducting larger sample GWAS in multiple regions and ethnic groups (). In November 2012, Nature published the ‘1000 Genomes Project Consortium’ data involving 1092 people from 14 ethnic groups, which formed a large international scientific collaboration in Europe, America, Asia and Africa. The establishment of gene databases could help to find trans-ethnic gene variations and analyse the general applicability of the association between diseases and genomes.

The Population Architecture using Genomics and Epidemiology (PAGE) project reported the results of a multi-ethnic study of reproductive age in 2013. The study involved 42,251 women from American Indian, African, Asian, European, Hispanic/Latino and Native Hawaiian populations. Three ANM genes, 6p24.2/SYCP2L, 5q35/UIMC1 and 20p12.3/MCM8, were replicated in non-European populations. However, 19q13/BRSK1 was not replicated significantly in other populations (). In 2018, PAGE used a variety of meta-analysis techniques to revalidate ANM genes of European descent in multiple populations. After excluding smaller sample sizes of American Indian/Alaska Native populations, 23 ANM studies were included from three large groups of Asian, African and Hispanic/Latino women. Trans-ethnic analysis showed that BRSK1 and MCM8 genes were associated with ANM and could be applied to non-European populations. The modified random effect association showed that FRMD5 and GPRC5B were associated with ANM genes in Asian and African populations, but there was heterogeneity in Hispanic/Latino populations (). FRMD5 is related to triglyceride metabolism, while GPRC5B has DNA enzyme activity in the ovary and is, for example, related to obesity and age at menarche.

The high-test efficiency of multi-ethnic GWAS needs to include a larger biological sample size. In the process of the development of GWAS, the national biological bank provides extensive data. The UK Biobank (UKBB) is based on a large prospective cohort study, which recruited about 500,000 participants aged 40–69 years old from 2006 to 2010 and recorded detailed health information, disease phenotypes and genomic data. The interim UKBB release in May 2015 contained data from 150,000 participants. In the full UKBB release database, 88.26% were of British background. The rest included genetic data from other ethnic groups, such as Asians and Africans (). In 2019, a new GWAS method, functionally informed novel discovery of risk loci (FINDOR), was reported. This method used multi-gene functional enrichment for association analysis of UKBB. Among the traits of ANM, about 44,000 participants of the interim UKBB release and 143,000 participants of the full UKBB release were included, and 53 new ANM-associated gene loci were detected (). Biobanks and advancing technologies of bioinformatics have improved the power of GWAS.

In 2021, the genetic data of 225,200 people were combined with the ReproGen, UKBB and BBJ. ReproGen and UKBB were derived from the European genetic data, while BBJ studied an East Asian population. The study reported 49 novel loci associated with ANM. Bioinformatics assessment was carried out and candidate genes were prioritized based on functional annotation. The trans-ethnic meta-analysis identified 127 loci, of which 43 were novel loci. Most ANM genes of European and East Asian populations were not found to be heterogeneous. In molecular network analysis, TP53BP1 had the most contact with other genes, which was related to breast cancer and ovarian cancer. FANCM was followed, which was associated with breast cancer risk. In addition, this GWAS also correlated genetic data with some clinical characteristics, such as ‘ever used hormone replace therapy’ and ‘age started hormone replacement’, adding evidence of ANM gene association ().

Polygenic score (PGS) is the weighted sum of the effect sizes of the associated alleles. In recent years, PGS was used to assess the genetic risks of different menopause ages. In 2021, further explored the genetic risks of POI, early menopause and ANM by calculating PGS in a large GWAS. This study identified 209 SNP associated with ANM in 201,323 women of European ancestry and were replicated in women of East Asian ancestry (the China Kadoorie Biobank study and BBJ), although with different effect sizes. The effects of each allele ranged from 3.5 weeks to 74 weeks. This study calculated the PGS of 108,840 women aged 34–61 years in the UKBB and used it to assess the association between POI and common ANM variants. The results showed that the PGS of common ANM genes had a low ability to predict early menopause and POI; the area under the receiver operating characteristic curve (ROC-AUC) was 0.65 and 0.64. Nevertheless, PGS of common ANM were still able to identify individuals at high risk of POI. ANM genes with higher PGS could predict the risks of POI and early menopause. The identified genes are mainly enriched in functions related to DNA damage repair, meiosis recombination and apoptosis across the life course ().

Although GWAS discovered more new related gene loci, its clinical predictive value is still limited. Because neighbour SNP are highly associated, it is often difficult to assess the validity of the new gene loci (). In recent years, in addition to improving the research methods and expanding the sample size to find new ANM loci, more in-depth studies combined with clinical trials have been carried out to target the identified genes. For example, a candidate genome for ANM was established based on GWAS results. Targeted sequencing of genes associated with DDR, hormone regulation, mitochondrial function, etc., was conducted in POI patients to explore the internal relationship between the causes and clinical manifestations of diseases from the perspective of gene regulation (). Post-menopausal women may have different clinical characteristics. The ANM gene might be associated with menopausal symptoms or disease experience. The ANM gene may be involved in menopausal symptoms or enduring diseases. Mendelian randomization, linkage disequilibrium score regression (LDSC) and other methods were used to demonstrate the disease-associated genetic mechanism between ANM and coronary atherosclerotic heart disease (), rheumatoid arthritis (), chronic kidney disease () and other diseases, but the conclusion was still controversial. In addition, there might be variations in laboratory indicators among women with different ANM, especially some important tested molecules that could indicate ovarian reserve. Genes associated with these molecules might affect ANM or ANM-associated genes. For example, recently reported age-stratified GWAS studies further found age specificity in four loci (MCM8 and in or near AMH, TEX41 and CDCA7) associated with anti-Müllerian hormone (). These studies not only explored the genetic mechanism of natural menopause age, but also provided new ideas and directions for research on the pathogenesis and genetic epidemiology of menopause-related diseases. Although GWAS have established the association between genes and clinical phenotypes, the details of these molecular mechanisms are largely based on bioinformatics reasoning, so further laboratory studies, animal experiments and clinical data are needed for verification.

This paper has reviewed the history of the genetic epidemiology of ANM. Initially, genetic traits of ANM were discovered; the limitations of traditional candidate gene studies then promoted the emergence of GWAS. GWAS verified the association between ANM and cardiovascular diseases, abnormal lipid metabolism, osteoporosis, diabetes, breast cancer, ovarian cancer and other diseases from the perspective of genes and molecular functions, and speculated on the related molecular mechanisms (). Given the importance of related biological information, the following websites were consulted: DisGeNET (https://www.disgenet.org/), NCBI (https://www.ncbi.nlm.nih.gov/), UniProt (https://www.uniprot.org), the Human Protein Atlas (https://www.proteinatlas.org) and GeneCards (https://www.genecards.org), and the identified ANM genes detected in the important literature were summarized (Table 1). This list will help identify genes associated with menopause and provide clues for clinical prediction and scientific research. A summary of relevant abbreviations is provided as Table 2.