Paracanthonchus gynodiporata associated with seaweeds in northeastern Brazil. Nematodes are generally believed to have a limited dispersal capacity because of the lack of planktonic larvae. Yet, they can drift on seaweeds, and water currents might be a natural barrier for their dispersal. Populations of P. gynodiporata were sampled over more than 1000 km coastline in regions across major oceanic currents with and without historical exploitation of seaweed.The resilience of ecosystems to negative impacts is generally higher when high gene flow, species diversity and genetic diversity are present. Population genetic studies are suitable to investigate genetic diversity and estimate gene flow between populations. Seaweed beds form a dynamic shallow water ecosystem influenced by climate change and human exploitation, as such, seaweed beds are a particularly powerful model to investigate ecosystem resilience in coastal areas. We studied the population genetic structure of the new nematode species P. gynodiporata is described in an integrative way using mitochondrial and nuclear sequences and morphological data. The 3D model of the head region shows for the first time a detailed view of the ventrosublateral teeth, a character often overlooked in older taxonomic studies of the genus. A total of 17 mitochondrial COI haplotypes were found with one haplotype representing 63 to 83% of the frequencies in each population. AMOVA showed overall little population genetic structure (FST = 0.05204), and no genetic subdivision between the populations under the influence of the two different water currents were found. Effects of historical seaweed exploitation on population genetic diversity were not detected. In contrast, significant differences between populations were found in morphometric characters. This discrepancy in genetic and morphological differentiation between populations across 1000 km of coastline is surprising in view of the frequently observed presence of several cryptic species at small geographical scale in other macroalgal associated nematodes.Our results show that cryptic species are not omnipresent in marine nematode species, suggesting that nematodes associated with seaweeds have been able to disperse over large distances across well-known biogeographic barriers. Morphologically similar but genetically distinct species, i.e. cryptic species, are prevalent in many taxonomic groups  and haveMany marine organisms were initially thought to have high dispersal capacity because of the passive dispersal potential via currents and the perceived ‘homogeneity’ of marine habitats over extensive spatial scales . HoweverLutjanus fulvus [Dispersal of marine organisms can be hampered by biogeographic barriers which may result in genetic breaks within species . Well kns fulvus ), and Pos fulvus ). Along s fulvus , 20. In s fulvus  forms twSeaweed beds can cover areas of thousands of square kilometers  but are Paracanthonchus Mikoletzky  [Paracanthonchus gynodiporata sp. n. which has hitherto only been found associated with seaweeds, in an integrative way based on a large number of specimens (38) from four different populations spanning a wide geographical distribution (> 1000 km). Mitochondrial (COI) and nuclear (18S and the D2/D3 fragment of the 28S rDNA) sequences were obtained and morphometric variation across populations was addressed to capture morphological variation. In addition, a 3D reconstruction of the mouth structure, one of the most important diagnostic characters within the genus, was made. Second, genetic structure and diversity of this new species were investigated using mitochondrial COI sequences of nematodes occurring on seaweed beds separated by the north Brazil and Brazil current [One of the most abundant and widespread nematode genera on seaweeds along the NE Brazilian coast is aimidae) , 28. Theaimidae) . Here, w current . In viewSargassum C. Agardh, 1820 and Gracilaria Greville, 1830 and five sediment samples were collected from natural seaweed beds on each of four beaches along the northeastern coast of Brazil, spread over a distance of about 1040 km , followed by a period of 11 years with no harvesting. Currently, in both locations the natural seaweed beds are no longer harvested. However, in Flecheiras (CE), seaweed cultivation outside the natural seaweed bed started in 2003 in an area smaller than the natural bed, and is still ongoing. The cultivation technique consists of floating ropes of about 25 m long to which the seaweeds are attached. The two southern beaches, Cupe (PE) (sampled in 2011 and 2012) and Ponta Verde (AL) (sampled in 2012), are under the influence of the Brazil current and algal beds are dominated by Sargassum. No historical or contemporal harvesting or seaweed cultivation has taken place in these southern locations. Distance between sampled seaweed beds ranged from about 167 to 1045 km . Five samples of the top 5 cm of the adjacent bottom sediment were collected using a plastic cylinder with inner diameter of 3.6 cm that was vertically pushed into the sediment. Three seaweed samples and three sediment samples were used for characterization of the nematode community [Five seaweed samples of the genera ommunity , and twoParacanthonchus gynodiporata sp. n. was one of the most abundant species on the seaweed samples from the four locations. This new species was systematically absent from all the sediment samples from all four locations in the current study and also in four other locations  along the Brazilian coast [The new species an coast , and wasP. gynodiporata sp. n. from two out of five seaweed samples per beach were taken for molecular processing. No other Paracanthonchus species was recorded in our samples. Each individual was stored in 0.5 mL centrifuge tubes with 25 μL Worm Lysis Buffer  and stored at -20 °C until DNA extraction. The samples were digested for 1 h at 65 °C and for 10 min at 95 °C with 1 μL of Proteinase K (10 mg mL−1). Tubes were centrifuged at maximum speed (21 000 g) for 1 min and stored at -20 °C. DNA was subjected to PCR to amplify a 396 bp fragment of the cytochrome oxidase c subunit I (COI) gene with the primers JB3 (5’-TTTTTTGGGCATCCTGAGGTTTAT-3’) and JB5 (5’-GCACCTAAACTTAAAACATAATGAAAATG-3’) [2, 2.5 μL PCR coral load concentrate, 2 μL MgCl2 25 mM, 0.5 μL deoxynucleotide triphosphate (10 mM), 0.125 μL of each primer (25 μM), 1 μL DNA and 16.125 μL sterile distilled water. For COI, the thermocycling conditions were: 94 °C for 5 min; 35 cycles of 94 °C for 30 s, 50 °C for 30 s and 72 °C for 30 s, plus a final extension step at 72 °C for 10 min. The D2D3 region of the large subunit of the nuclear ribosomal DNA was amplified with the primers D2A (5′-ACAAGTACCGTGAGGGAAAGTTG-3′) and D3B (5′-TCCTCGGAAGGAACCAGCTACTA-3′) [Because of the very high number of juveniles at different developmental stages and the paucity of adults in the four populations, we were unable to use the same individuals for morphometry and molecular analyses. Instead, individuals of a mix of adults  and juveniles of ACTA-3′)  with ampACTA-3′) . The seqP. gynodiporata sp. n. from the type location Cupe (PE) mounted in permanent slides were taken at different magnifications using a light microscope (Leica DAS microscope type R) with differential interference contrast (DIC), and equipped with a Leica DFC420 camera. The entire habitus and selected body regions  with important taxonomic structures were photographed and measured using Leica Application Suite v. 3.4.1. Slides were deposited in the Royal Belgian Institute of Natural Sciences under the numbers RIT848 (holotype), RIT849 and RIT850 (Paratypes) and in the Zoology Museum at the Faculty of Sciences, Ghent University, under the numbers UGMD 104316. In addition to the type specimens, four, six and four males from Flecheiras (CE), Muriú (RN) and P. Verde (AL), respectively, were measured and 15 somatic and six sexual characters were used to investigate morphometric variability. Within the genus Paracanthonchus, there is substantial variation in stoma structure (armature) according to previous descriptions, varying from a single hollow dorsal tooth to one hollow dorsal tooth and two pairs of small ventrosublateral teeth combined or not with three cuticular ridges [P. gynodiporata sp. n. from the type location Cupe (PE) from 2012 was mounted in a glycerol-gelatin mixture  and observed under a light microscope. In total, 52 pictures at different optical sections of the head were taken. The pictures were used for constructing the 3D model in the software AMIRA 3.1.1. .Digital pictures of eight males and 12 females of r ridges . The stoπ) and haplotype diversity (h) according to [FST [P. gynodiporata sp. n., a hierarchical AMOVA was conducted by grouping Flecheiras (CE) and Muriú (RN) in a northern group under the North Brazil current and Cupe (PE) and P. Verde (AL) in a southern group under the Brazil current. The pairwise FSTp-values were corrected based on the sequential Bonferroni method [The sequence chromatograms from the three markers COI, D2D3, and 18S were investigated with DNASTAR LASERGENE SeqMan v. 7.1.0. Sequences from the three markers were separately aligned by ClustalW. COI sequences were translated to amino acid sequences before the alignment to ensure that no stop codons would be present. P-distances and the number of variable sites were calculated using the software MEGA 6 . Geneticrding to –36. Loweto [FST . The levto [FST : little to [FST . This isi method . Additioi method  and editParacanthonchus [All morphometric data analyses were performed with the software PRIMER v. 6.1.6 . In all thonchus  . The best blastn identity for all COI sequences was Paracanthonchus sp. . In total, 17 COI haplotypes were found, with haplotype C8 being the most abundant in all four locations (average 77% ± 8). Only four other haplotypes were shared between at least two beaches, while the remaining 12 haplotypes were restricted to a single location and occurred at low frequencies . The pairwise analysis showed moderate separation between the populations of Flecheiras (CE) and Muriú (RN), and between Flecheiras (CE) and Cupe (PE) 2012 .In total, 27 (Flecheiras (CE)), 24 (Muriú (RN)), 25 (Cupe PE 2011)), 33 (Cupe (PE 2012)) and 20 (P. Verde (AL)) individuals from 1), 33 (Ch = 0.5783 ± 0.0961), where the seaweed bed was considered to be most impacted because of historical and ongoing seaweed exploitation; however, the standard deviation overlapped with those of the diversity estimates observed in the other locations except Muriú (RN)  and Cupe (PE - 2011 and 2012) . A significant but little genetic structure was observed  between the northern group under the influence of the north Brazil current (Flecheiras (CE)) and Muriú (RN)) and the southern group under the influence of the Brazil current  and P. Verde (AL)), indicating that the split of the South Equatorial Current along the Brazilian coast imposes only a weak biogeographical barrier for the P. gynodiporata sp. n. populations. The D2D3  and 17 in P. Verde (AL)) and 18S  and 19 in P. Verde (AL)) sequences were identical. The best blastn identity for D2D3 sequences was Paracanthonchus sp.  while for 18S it was Paracyatholaimus intermedius .Haplotype networks for each location showed the same pattern as the overall network, with one dominant haplotype and a low number of rare haplotypes with few mutations between them. Genetic diversity appeared to be higher in Flecheiras (CE) Beach h = 0.578 ± 0.0961F = 4.7107; p < 0.001; Fig. F = 5.657; p = 0.006), distance of the amphidial fovea from anterior end , and cephalic setae length  were significantly different and non-overlapping between Flecheiras (CE) and Muriú (RN); Muriú (RN) and Cupe (PE); and Muriú (RN) and P. Verde (SIMPER and Tukey pairwise comparison). In contrast, specimens between Flecheiras (CE) and P. Verde (AL) and between Cupe (PE) and P. Verde (AL) did not differ significantly. With respect to sexual characters, substantial variability was observed in the precloacal supplement length . The anteriormost precloacal supplement (SP4) was the top ranked character that contributed to the differences between populations: SP4 of individuals from Cupe was longer than that of individuals from Flecheiras (SIMPER: Cupe (PE) x Flecheiras (CE) = 25.65% contribution). In addition, the posterior most precloacal supplement (SP1) of individuals from P. Verde (AL) was longer than the one of individuals from Flecheiras (CE) (SIMPER: P. Verde (AL) x Flecheiras (CE) = 23,03% contribution), and SP3 of the individuals from Cupe (PE) was longer than the one of specimens from Muriú (SIMPER: Cupe (PE) x Muriú (RN) = 30% contribution). Spicule and gubernaculum did not differ significantly among populations. Because of considerable overlap, no single sexual character by itself could distinguish individuals of one location from those of other locations. The character measurements are presented in Table Morphometric variation among males from different populations was significant .Sargassum polyceratium.Type locality. Brazil, Pernambuco State, Cupe Beach (8°27′29′S 34°58′58″W), subtidal zone, associated with brown seaweed Other Localities. Praia de Flecheiras, Trairí – Ceará – Brazil (3°13′08″S 39°16′18″W); Praia de Muriú, Ceará-Mirim – Rio Grande do Norte – Brazil (5°33′43″S 35°14′21″W), Praia de Ponta Verde, Maceió – Alagoas – Brazil (9°39′55″S 35°41′54″W).Sequences from type location:Type Haplotypes: COI – KX352225 (C2), KX352226 (C3), KX352227 (C4), KX352228 (C5), KX352229 (C6), KX352230 (C7), KX352231 (C8), KX352232 (C9), KX352233 (C10), KX352235 (M2).Type Genotypes: 18S - KX352221; D2D3 - KX352222Life Science Identifier (LSID): urn:lsid:zoobank.org:pub:B6FBE6B8-B482-4206-A9D3-842A7014A505.Etymology: The species name refers to the pre- and post-advulvar body pores in the female Fig. .Paracanthonchus is very restricted in GenBank. COI (396 bp): 317 conserved and 76 variable sites compared to Paracanthonchus sp. (FN998914.1) 18S (914 bp): 848 conserved and 61 variable sites compared to P. caecus (AF047888.1) D2D3 (747 bp): 532 conserved and 127 variable sites compared to Paracanthonchus sp. (KJ638031.1).The number of sequences available for the genus Paracanthonchus gynodiporata sp. n. appears morphologically similar to P. perspicuus Kito, 1981 by the presence of a small dorsal tooth, overall spicule shape and gubernaculum structure with a crura ridge with dorsal thorn. However, P. gynodiporata sp. n. can be distinguished from P. perspicuus by the smaller body length (779 – 1120 μm vs 1269 – 1287 μm), presence of ventrosublateral teeth vs absence in the latter, the presence of only one instead of two crura dorsal thorns, and the presence of two weakly developed precloacal supplements instead of one weakly developed precloacal supplement near the cloacal opening in P. perspicuus. Finally, the presence of the pre- and post-advulvar body pores observed in P. gynodiporata sp. n. has not been reported in any other species in the literature.Body medium-sized (779.3 – 1058.5 μm), largely cylindrical with rounded truncated head with conical tail; body cuticle with transverse rows of fine dots, slightly larger at level of lateral field, more visible posterior to the neck region; amphidial fovea ventrally spiral, smaller in females with 3.5 turns and four turns in males. Buccal cavity with a small dorsal tooth and two pairs of minute ventrosublateral teeth. Spicules paired and slightly ventrally bent, 34 – 46 μm long, gubernaculum with double apophyses and complex crura ridge dorsally with large thorn and lateral protuberances; four large well sclerotized tubiform precloacal supplements; and two short weekly developed tubiform supplements with similar structure between SP1 and cloacal opening. Females with vagina flanked by a pre- and post-vulvar body pore. Body largely cylindrical, slightly narrowing in anterior neck region but more pronounced in conical tail. Punctated cuticular ornamentation, consisting of transverse rows of dots, forming the tip of inner cuticular struts; at the level of the lateral field punctation slightly larger though hardly differentiated in the neck region. Eight longitudinal rows of body pores, at mid body the largest pores bordering the lateral field. Somatic setae arranged in four sublateral longitudinal rows; setae longest (6 μm) and most numerous in neck region. Head region anteriorly rounded and truncated; lip region with six separate lips. Anterior sensilla arranged in two crowns: an anterior crown of six inner labial papillae and an outer crown of six external labial setae (4.5 μm) and four slightly longer cephalic setae (3.7 μm); both types of setae bipartite with open tip. Amphidial fovea spiral (four turns), ventrally wound and surrounded by punctation. No ocelli present. Buccal cavity with cheilostome reinforced by 12 cheilorhabdia and wide cup-shaped; pharyngostome short funnel-shaped with a small well developed dorsal tooth and two pairs of minute ventrosublateral teeth. Pharynx largely cylindrical, just posterior mid-way surrounded by the nerve ring. Outlet of a pair of posterior ventrosublateral pharyngeal glands at the level of the nerve ring; outlet of dorsal gland far anteriorly. Cardia surrounded by intestinal cells and apparently with associated glands; intestine usually with diatoms visible in its lumen. Secretory-excretory pore at short distance from anterior end (26 μm); short outlet sclerotized, swollen anterior ampulla; ventral gland at level of anterior intestine. Tail conical with three well developed caudal glands and nucleus, anteriorly extending along rectum. Spinneret well developed.Male reproductive system diorchic, anterior testis outstretched on the right side of the intestine, posterior one reflexed and lying on the left side of the intestine; sperm cells small globular (1.5 μm); vas deferens surrounded by muscular sheath and showing differentiation in granulation; spicules paired, slightly ventrally bent, strongly sclerotized with capitulum narrower than blade; blade about equally wide but tapered distally. Gubernaculum, strongly sclerotized and complex structure, composed proximally of a pair of (slightly twisted) apophyses with narrower tip, and crura  embracing retracted spicules, dorsal wall of the crura provided with dentate ridge with one larger thorn and laterally pointed protuberance visible in ventral view. Four oblique anteriorly orientated large, well sclerotized mid-ventral tubiform precloacal supplements; the posteriormost one (SP1) at about 30 μm from cloacal opening; each supplement with a central sensillar canal surrounded by a cuticular wall. In between SP1 and cloacal opening, two short, weakly developed tubiform supplements with similar structure Fig. .General appearance , digestive and secretory-excretory system as in male. Head region similar but smaller , fovea with 3.5 turns. Reproductive system didelphic-amphidelphic with antidromously reflexed ovaries, anterior ovary right of the intestine, posterior on the opposite side; uteri with up to three developed oocytes observed in both uteri together; well-developed muscles at level of ovejector. Vagina, rather short, surrounded by vaginal constrictor muscles; vulva at mid-body and flanked by a pre- and post-advulvar body pore. No sperm observed.Paracanthonchus are the number of teeth in the buccal cavity . This result was remarkable considering that nematodes in general lack planktonic larvae and dispersal is likely to be limited [Halomonhystera disjunta species complex in the region of the Westerschelde estuary  [Litoditis marina species complex in the North Sea along the Belgian and Dutch coast , from the Bay of Biscay to the Baltic Sea , and at transatlantic distances  [Thoracostoma trachygaster species complex  [Our  limited . Previou< 0.001) , in spec< 0.001) , 13. How< 0.001) .Fst values despite a lack of ongoing gene flow [The northern most population, Flecheiras, was differentiated from Muriú (RN) and Cupe. The latter two populations had significant negative neutrality tests and fitted the recent expansion model, suggesting that the differentiation with Flecheiras may be caused by a rapid expansion of the latter two populations. In addition, Flecheiras was the closest population to the border between the northeastern Brazil province and the Amazon province , 46. Theene flow , 48. EspFST value in the pairwise comparison with Flecheiras (CE) . However, the FST values appear to decrease with distance and become insignificant. Such chaotic patterns are not uncommon in marine environments, and adding local environmental data might shed light to understand this apparent chaos [Although data on the age of seaweed beds in the northeast of Brazil is extremely limited, seaweed beds have a close relationship with coral reefs because many seaweed species need hard substrate to attach and develop . Reef ecnt chaos .P. gynodiporata sp. n. as observed by the weak genetic structure between those two regions (FST = 0.05204). Our sampling area covered only one biogeographical province, the Northeastern Brazil province. Provinces are classified upon a hierarchical system based on taxonomic configurations, influenced by evolutionary history, patterns of dispersal, and isolation [P. gynodiporata sp. n. agrees well with the above mentioned biogeographical province. In all, our data point to a very low genetic differentiation across a large geographic area suggesting that P. gynodiporata sp. n. has performed long-distance dispersal during some time along its evolutionary history. Since the studied species has not been found in the sediment so far [Paracanthonchus is a frequent genus found associated with turtles [Acanthonchus nematode species associated with the sea turtle Eretmochelys imbricata have shown the presence of an identical mitochondrial DNA haplotype in two beaches more than 900 km apart. Whether the amount of nematodes using this particular dispersal mechanism would be sufficient to establish a population in the new patch remains unclear, as priority effects may hamper the establishment of newly arriving individuals [The two main currents at the Brazilian northeastern coast did not appear to constitute a strong physical barrier for solation . The lact so far , driftint so far , 54 inclt so far . Howevert so far , 55, fort so far , and migt so far . Paracan turtles  and coul turtles . Personaividuals .P. gynodiporata sp. n. was found. Considering that seaweeds were harvested monthly during the peak production period in the 1980’s in the northeastern coast of Brazil [P. gynodiporata sp. n. has not been observed in the sediment in the studied locations nor in four other locations along the Brazilian coast (Pirambu (CE), Icapuí (CE), São Sebastião (SP) and Ubatuba (SC)) [Colonization dynamics can strongly impact the mitochondrial haplotype diversity over time , 60. Yetf Brazil , the seaba (SC)) . Also, hParacanthonchus shares the presence of tubular precloacal supplements with Acanthonchus, differing from the latter by the presence of lateral differentiation in cuticular punctation, but no synapomorphy is observed [Paracanthonchus, the difficulty in interpretation of the stomatal armature, especially with respect to the presence and number of ventrosublateral teeth, the number of precloacal supplements, the structure of the gubernaculum as well as the interspecific overlap of morphometric features such as body length and spicule length, complicate species differentiation based on light microscopic observations. This also hampers comparison with older descriptions, in which some features were overlooked or misinterpreted [P. batidus [P. digitatus [P. cochlearis [P. batidus; 4 in P. digitatus; 4 + 2 P. gynodiporata n. sp.) and the number of turns of the amphidial fovea . Substantial overlap exists for other characters (e.g. body length). One possible example of phenotype misinterpretation concerning diagnostic characters is the poorly developed precloacal supplement near the cloaca. In P. perspicuus, which is very similar to P. gynodiporata sp. n., Kito [Paracanthonchus species are a result of misinterpretation or not. The combination of the following characters could be used to distinguish between species from the genus Paracanthonchus: 1) number of precloacal supplements including the poorly developed tubular supplement near the cloaca, 2) the mouth armature including the number of ventrosublateral teeth, 3) the ornamentation of the gubernaculum  and 4) the number of loops in the amphidial fovea. Body length, body width and pharynx length should be given less weight.There are no synapomorphies at subfamily and genus level within the family Cyatholaimidae . The subobserved . At specerpreted . In Braz batidus , P. digiigitatus  and P. cchlearis , which cn., Kito  claimed P. gynodiporata sp. n. A threshold between intra and interspecific genetic distances for the COI gene in marine nematodes has been set at 4.8% p-distance [P. gynodiporata sp. n. populations and non-overlapping morphometric characters such as buccal cavity width, distance of amphidial fovea from anterior end, and cephalic setae length were able to differentiate populations. It has been demonstrated that body and tail length can substantially vary within a single species progeny [P. gynodiporata n. sp. during voucher procedure, we have not used the same individuals for DNA sequencing and morphometry. However, because of the low haplotype richness (maximum of 6 haplotypes per location) and the high dominance of a single haplotype, at times representing more than 80% (Muriú (RN)) of the haplotype frequencies, it is very likely that the individuals used for the morphometry are from the same or similar haplotypes. Because of initial differences in number of adults among the four populations, we have added a number of juveniles to increase the balance of our design for the molecular analysis. However, because 1) only one species of the genus Paracanthonchus occurred associated with seaweeds in our samples in the four studied locations, and 2) very few overall mutations were present among their sequences , it is highly unlikely that the added juveniles belonged to a different species.Interestingly, the observed phenotype variability is not accompanied by genotypic variability in distance , 67. In  progeny  and even progeny  can be a progeny , includi progeny , 71, 72.Drosophila melanogaster strain [Many morphological traits are encoded by multiple genes, and it is possible that other regions of the genome could show more variation than the three genes we have studied. Moreover, environmental factors can play a role in nematode phenotypic variation without similar levels of genetic differentiation as a result of epigenetic mechanisms (e.g. methylation of CpG sites - cytosine followed by a guanine with one phosphate in between) . For insr strain , showingr strain , and epiParacanthonchus presents a wide variation in mouth structure [Clearly, an integrative approach using independent data sources can lead to scientifically valid species delineation. Some morphological characters of nematodes are difficult to observe. Similarly, the genus tructure . The paitructure , 75. IdeNematodes associated with seaweeds can show low genetic structuring over large distances (>1000 km), suggesting dispersal capacity of nematodes can be high throughout the evolutionary history of the species. There is no evidence that historical seaweed exploitation has affected genetic diversity or haplotype frequencies of epiphytic marine nematodes. Morphometric variation in natural populations can be substantial, showing interspecific overlap, and one should combine at least molecular and morphological data in an integrative way to establish species boundaries and describe diversity.


DNA‐sequenced data, the genetic structure of populations can now be inferred and studied in unprecedented detail. Across social science, this innovation is shaping new bio‐social research agendas, attracting substantial investment in the collection of genetic, biological and social data for large population samples. Yet genetic samples are special because the precise populations that they represent are uncertain and ill‐defined. Unlike most social surveys, a genetic sample's representativeness of the population cannot be established by conventional procedures of statistical inference, and the implications for population‐wide generalisations about bio‐social phenomena are little understood. In this paper, we seek to address these problems by linking surname data to a censored and geographically uneven sample of DNA scans, collected for the People of the British Isles study. Based on a combination of global and local spatial correspondence measures, we identify eight regions in Great Britain that are most likely to represent the geography of genetic structure of Great Britain's long‐settled population. We discuss the implications of this regionalisation for bio‐social investigations. We conclude that, as the often highly selective collection of DNA and biomarkers becomes a more common practice, geography is crucial to understanding variation in genetic information within diverse populations.Following the increasing availability of  Recent social scientific interest in bio‐social relations – the ways in which individual biology interacts with social environments to produce specific patterning of health and social outcomes – have spurred more efforts to take into account biological information about individuals, including their DNA. Increasing data availability and continuous advances in computational power have made feasible the analysis of human DNA for a variety of research problems, and substantial investments are being made in the UK to assemble DNA records into purpose‐built genetic databases . Although no longer prohibitively expensive for bio‐social investigations, cost issues nevertheless restrict the size of attainable samples, and participation in investigations may systematically exclude some groups . Surname‐based studies are based on an indirect indicator of genetic structure, although they rest on specific assumptions about migration and may be subject to other confounding factors. Yet these studies are able to draw on nearly complete population registers, making it possible therefore to identify the geographical extent of sub‐populations within a population‐wide representation.In this paper, we seek to combine the strengths of both types of studies and relate a collection of DNA samples to Great Britain's regional surname geography. We argue that as biological concerns become part of social science research agendas, so hitherto neglected aspects of geographic sample designs need to be addressed in order to enable valid inferences about the population at large in any specific time period. Social surveys often seek to establish a basis to generalisation through prior stratification or posterior sample weights using indicators of demographic and social similarity that are deemed material to the survey's objectives. In biological and bio‐social research, however, it remains an open question as to what should be regarded as a representative biological sample that accommodates the effects of population structure. This question does not, of course, bear on the general problem that defining populations according to national boundaries or other geographical delimitations creates uncertainties with respect to non‐territorial shared population characteristics, such as genetics, identity or social attitudes Nash . This pret al. et al. et al. We drew on genotyped DNA‐sequences of 2039 volunteers, which were collected in 2008/09 as part of the Wellcome Trust‐funded study of ‘The People of the British Isles’ (POBI). The sample was designed with a view to representing known population subgroups as well as to identify fine population structure within the broader British population ;it should allow removal of surnames known to have been imported from abroad in the more recent past and which may be considered to be ‘native’ as a consequence;it should map to the spatial extent of the sample with similar inclusion/exclusion criteria, and more specifically it should only contain rural records according to the definition of ‘rural’ used in the sample design.In order to provide a population‐wide basis to comparison, an appropriate population register should satisfy the following criteria:www.ukdataservice.ac.uk). There are no 1881 Census data available for Northern Ireland. Records that refer to populations that likely have a recent migration history were removed using a surname classification tool (Mateos Population registers that hold surnames (criterion 1) and at the same time provide a nearly complete set of micro‐data for the time period of interest (criterion 2) are the digital micro datasets of the 1881 Censuses of England, Wales and Scotland  and merge the isonymy groups that show a low value of Regional Integrity. Iteratively, we arrive at the optimal regionalisation of Great Britain, which best spatially represents population structure based on the evidence found in the genetic sample and the population register.R . The surname mixes are calculated by inverting the isonymy matrix to a distance matrix  and running Ward's hierarchical clustering algorithm on it. The result is a taxonomy of areas by their surname compositions, available for any number of isonymy groups R is the actual index value, E(R) the expected value, max(R) the maximum possible value, n all observations and ncons observations in consonant pairs.The generic formula of the Adjusted Rand iset al. , which is a quantity that is widely applied in geography to describe relative density of phenomena in one region relative to other regions  at which individual areas (at the bottom) are assigned to iteratively increasing clusters . We then append to the volunteer information the membership of each isonymy group k (with k ϵ {2..80}) based on the location of the grandparents’ birth places. Since there are four grandparents who may each reside in a different isonymy group, the most common group among the four grandparents is assigned to the participant. There can be ties in relatively rare circumstances (7.5 per cent for k = 20 isonymy groups) when two grandparents or each single one belonged in a different isonymy group. In this case, we selected one of the groups randomly.Based on a similar clustering procedure, study volunteers have been assigned a genetic cluster (a group of volunteers whose genetic characteristics indicate that they likely share similar ancestry) at successive stages. At each level, participants have been grouped into 2, 3, 4, etc. up to 53 clusters resulting in an assignment matrix, developed and provided by Leslie et al. . Togethel and k using the Adjusted Rand similarity index  of genetic profiles 5 Figure b. The neFigure k = 3 isonymy groups divides Great Britain into three regions: Wales, England and Scotland  of 0.61, this combination indicates high certainty about the presence of population structure between those regions.The partition with Radj of .284 comprises a combination with k = 7 surname regions and l = 11 genetic clusters  and a northern (18) cluster with two additional isonymy groups  along the border between England and Wales. These four clusters in and around Wales correspond geographically to genetic clusters 1 and 14, 4 and 6. The south‐west of England now splits into two distinct isonymy groups covering Cornwall (19) and Devon (12) and thereby corresponding to genetic clusters 3 and 10. The isonymy groups in central and southern England suggest much greater population heterogeneity than is indicated by co‐ancestry: here the genetic cluster 15 prevails. The opposite can be observed in Orkney: here we find more genetic heterogeneity than surnames would suggest.Figure The succession of isonymy maps suggests different levels of regionalisation that best reflect the population in genetic terms. Yet, a regionalisation purely based on global correspondence of genes and surnames conceals local variations in the heterogeneity of each measure: it is clear from Figure Distinctiveness) and simultaneously similar to each other (Dominance). Together the two components convey a sense of confidence we can ascribe to the degree to which an isonymy group truly represents population structure. We call this the Regional Integrity of an isonymy group in terms of the population structure found in the sample.Based on this reasoning, we define high local correspondence as a property of an isonymy group when it encompasses observations that, in terms of their co‐ancestry, are distinct from other isonymy groups , separate analyses for each of the regions or other ways of accounting for the regional manifestation of population structure may be useful.The regionalisation of population structure also identifies the parts of Great Britain where we should be relatively more wary of generalising about bio‐social or biological phenomena. Where isonymy and co‐ancestry align, we can be more confident in attributing biological phenomena (e.g. phenotypes) to the regional sub‐population in general, and bearers of regionally concentrated names (and their relatives) in particular. Where they do not align, we have less basis to ascertain the degree to which a phenomenon is present in the sub‐population. This applies, for instance, to Scotland and southern Wales, where we do not know whether the genetic clusters found are characteristics of particular, sampled individuals, sampling sites, a locality or the wider region. This uncertainty limits our ability to assess the importance of phenomena for the population at large.Whichever the strategy, triangulation by using ancillary data is necessary to establish a valid basis for inference and generalisations. Genetic samples are likely to remain constrained by prior assumptions on the genetic structure of populations, arising out of the results of previous research. They are thus likely to be highly selective and geographically skewed and may also lack a clearly defined source population in time and space. Our findings suggest that this selectiveness can be countered to some extent by use of surnames as population‐wide genetic indicators, thus providing a more robust basis for inductive generalisations.Some limitations of this approach should be noted. First, surname geographies are shaped by processes that may not accompany genetic diffusion. The consistency of surname adoption across space and time and the nature of naming conventions affect the signal quality of a surname with respect to population structure. In Wales, for example, where a relatively small number of surnames were adopted at a later period than in the rest of the country, surname geographies are much more invariant and thus less likely to pinpoint population structure. The wide use of diminutive surnames  and common occupation‐related metonyms  further obscures detail – diminutive surnames are very common right across Wales and only indicate specificity to this territory, while metonyms are often common across the entirety of Great Britain. Thus while our analysis is informative for most names, this is not the same as saying that it holds for a similar proportion of name bearers. Although grouping of spatially co‐occurring surnames should at least in part counter these difficulties, the impact of naming conventions and histories on surname–gene correlations at the area level requires further research. The scale of migration and mixing between migrants and longer settled residents will further weaken the ecological link between genes and surnames over time. Indeed, surnames only function as ancillary correlates to genetic population structure where surnames are hereditary, where naming conventions have been regionally variant at a sufficiently granular geographic scale and where the majority of the population does not migrate or migration origins and destinations are sufficiently definite that they can be accommodated. Existing studies on surname geographies suggest that most European and American countries, and Japan, satisfy these criteria Cheshire . While aSecond, the result of combining data on surname geographies and genes is in part a manifestation of the sample design of the POBI project. The degree to which a region is distinct and homogeneous is still affected by the number of observations that fall within a region and how many genetic clusters can be identified; both characteristics are in turn a result of sample design. It should be noted, however, that the Distinctiveness index penalises small numbers of observations to some extent. For instance, if the sample had been more skewed within England with the same observed genetic clusters, some English sub‐regions with more observations would have increased in their Distinctiveness. Conversely, regions with fewer observations would have lost Distinctiveness and thereby Regional Integrity. Since lower values result in mergers, and since more observations tend to produce higher values of Distinctiveness, regions with few observations tend to merge to larger entities, which is a desirable effect of the decision algorithm. In all of this, the sample size required to represent the parent population depends on the heterogeneity of the characteristic that is being measured: in the case of genes, this is unknown at the sample design stage, and in the case of surnames, this depends on geographies of naming conventions that at best bear only indirect and partial correspondence with genetic structure of the population. Bootstrapping may be a useful extension of the procedures set out here, in order to assign standard errors and confidence intervals to regional estimates. Yet, given sufficient observations in each isonymy group (at least 105), this was not felt necessary.Our view is that the analysis set out here combines the strengths of in‐depth genetic surveys with the pervasiveness of (historic and contemporary) population registers. In follow‐up genetic research it will be essential to devise sampling strategies that build on the results of the POBI project in the interests of improved generalisation. Given that bio‐social research straddles the purview of social similarity and biology, geography will have a crucial role to play in bridging the disciplines involved and offer solutions to inform research designs and interpretation of bio‐social phenomena, in particular as bio‐social research agendas become more developed and produce an increasing number of unstructured, highly selective samples that significantly depart from standards of social representativeness. In this sense, the POBI sample is not unlike many other population datasets. The three‐stage approach presented in this paper – measuring global correspondence, identifying local correspondence and bespoke regionalisation – may be an effective way to deal with such types of unstructured samples and support contextualised interpretations and valid generalisations that take into account the indefinite diversity of human populations.


The knowledge of the extent and pattern of diversity in the crop species is a prerequisite for any crop improvement as it helps breeders in deciding suitable breeding strategies for their future improvement. Rice is the main staple crop in India with the large number of varieties released every year. Studies based on the small set of rice genotypes have reported a loss in genetic diversity especially after green revolution. However, a detailed study of the trend of diversity in Indian rice varieties is lacking. SSR markers have proven to be a marker of choice for studying the genetic diversity. Therefore, the present study was undertaken with the aim to characterize and assess trends of genetic diversity in a large set of Indian rice varieties (released between 1940–2013), conserved in the National Gene Bank of India using SSR markers.A set of 729 Indian rice varieties were genotyped using 36 HvSSR markers to assess the genetic diversity and genetic relationship. A total of 112 alleles was amplified with an average of 3.11 alleles per locus with mean Polymorphic Information Content (PIC) value of 0.29. Cluster analysis grouped these varieties into two clusters whereas the model based population structure divided them into three populations. AMOVA study based on hierarchical cluster and model based approach showed 3 % and 11 % variation between the populations, respectively. Decadal analysis for gene diversity and PIC showed increasing trend from 1940 to 2005, thereafter values for both the parameters showed decreasing trend between years 2006-2013. In contrast to this, allele number demonstrated increasing trend in these varieties released and notified between1940 to 1985, it remained nearly constant during 1986 to 2005 and again showed an increasing trend.Our results demonstrated that the Indian rice varieties harbors huge amount of genetic diversity. However, the trait based improvement program in the last decades forced breeders to rely on few parents, which resulted in loss of gene diversity during 2006 to 2013. The present study indicates the need for broadening the genetic base of Indian rice varieties through the use of diverse parents in the current breeding program.The online version of this article (doi:10.1186/s12863-016-0437-7) contains supplementary material, which is available to authorized users. Oryza sativa L.) is a staple food crop in India and many parts of the world. In India, it occupies the largest area under cultivation and has maximum share in grain production . Except 1940–1965, all intervals showed approximately comparable number of genotypes. The value of gene diversity, heterozygosity, major allele frequency and PIC of rice varieties falling in six time intervals were analyzed using the Power Marker. The mean value of gene diversity, heterozygosity, major allele frequency and PIC were plotted with years  on X axis and values of gene diversity, heterozygosity, major allele frequency and PIC on Y axis (Fig.


Coix larchryma-jobi L.) was the commonly used Traditional Chinese Medicine (TCM) with high content of seed storage protein. The hydrolyzed bioactive oligopeptides of adlay have been proven to be anti-hypertensive effective components. However, the structures and anti-hypertensive mechanism of bioactive oligopeptides from adlay were not clear. To discover the definite anti-hypertensive oligopeptides from adlay, in silico proteolysis and virtual screening were implemented to obtain potential oligopeptides, which were further identified by biochemistry assay and molecular dynamics simulation. In this paper, ten sequences of adlay prolamins were collected and in silico hydrolyzed to construct the oligopeptide library with 134 oligopeptides. This library was reverse screened by anti-hypertensive pharmacophore database, which was constructed by our research team and contained ten anti-hypertensive targets. Angiotensin-I converting enzyme (ACE) was identified as the main potential target for the anti-hypertensive activity of adlay oligopeptides. Three crystal structures of ACE were utilized for docking studies and 19 oligopeptides were finally identified with potential ACE inhibitory activity. According to mapping features and evaluation indexes of pharmacophore and docking, three oligopeptides were selected for biochemistry assay. An oligopeptide sequence, NPATY (IC50 = 61.88 ± 2.77 µM), was identified as the ACE inhibitor by reverse-phase high performance liquid chromatography (RP-HPLC) assay. Molecular dynamics simulation of NPATY was further utilized to analyze interactive bonds and key residues. ALA354 was identified as a key residue of ACE inhibitors. Hydrophobic effect of VAL518 and electrostatic effects of HIS383, HIS387, HIS513 and Zn2+ were also regarded as playing a key role in inhibiting ACE activities. This study provides a research strategy to explore the pharmacological mechanism of Traditional Chinese Medicine (TCM) proteins based on in silico proteolysis and virtual screening, which could be beneficial to reveal the pharmacological action of TCM proteins and provide new lead compounds for peptides-based drug design.Adlay ( Colla corii asini by Wu [Cornu bubali by Liu [Sorghum bicolor [Pennisetum glaucum [Traditional Chinese Medicines (TCMs) are commonly used natural products, and some of them have high protein content. However, pharmacological mechanism exploration of these TCMs is always difficult because of the isolation difficulty of proteins. Actually, the prejudice that primary metabolites, including proteins and nucleic acids, have the low bioactivities is also not conducive to the study on pharmacological mechanism of TCM proteins. With in-depth research of protein and peptide drugs, the pharmacological activity of TCM-derived proteins is also gradually addressed by researchers. Especially, many oligopeptides or peptide fractions are discovered from animal drugs of TCM, and the studies on their biological activities are usually guided based on the traditional pharmacological effect. For instance, two hematopoietic peptides were identified from ni by Wu  and threi by Liu . Compare bicolor , Pennise glaucum , and so Coix larchryma-jobi L.) is a commonly used TCM for the clinical treatment of cardiovascular diseases (CVD) and hypertension [Adlay . Biochemistry assay and molecular dynamics (MD) simulation of potential ACEIs were carried out to validate predicted bioactivities and binding stability between oligopeptides and ACE. This study provides a new research mode for revealing the action mechanism of TCM proteins. Combining in silico proteolysis, molecular simulation and biochemistry assay, TCM oligopeptides could be obtained and used to discover their bioactivities rapidly.According to in silico gastrointestinal digestion proteolysis, 222 peptides were obtained and their sequence distribution is shown in Pharmacophore is an important method to reverse identify action targets for candidate compounds , and is As for the ten known ACEIs from oligopeptides library in 2+ was also overlapped well in active sites  standard eluted out of the C18 column at 5.5 and 9.2 min, respectively (−9 mol/L) manifested a strong ACE inhibition ratio of 77.86% ± 0.15%  method was utilized to estimate ACE inhibitory activity. Hippuric acid (HA) and hippuryl-ectively , and werectively A. The co ± 0.15% B. Then, 2+; and (3) hydrophobic interactions with VAL518. Hydrogen bond, electrostatic and hydrophobic effects were the main ligand–enzyme interactions, which were consistent with the pharmacophore results of present study, and provided more structural information of ACE peptide inhibitors  simulation was implemented to validate the docking results and evaluate the stability of ACE-inhibitor complexes under dynamic conditions in this paper. Four initial complexes conformations of 1O86–lisinopril, 1O86–NPATY, 1O86–NCHEF and 1O86–VSAIGF were acquired from the molecular docking of CDOCKER. The RMSD trajectories of ACE protein backbone and total energy profiles of each complex are shown in The RMSD of four ligands was also analyzed and are displayed in 2+ were crucial to the ACE inhibitory activity and beneficial to the stability of complexes. Therefore, the atom distances of 1O86–lisinopril and 1O86–NPATY were further analyzed during the simulation time . The purity levels of the synthesized oligopeptides were all greater than 98% by HPLC analysis. ACE from rabbit lungs and HHL was supplied by Sigma-Aldrich . Acetonitrile of HPLC grade was purchased from Fisher Scientific . Lisinopril was supplied by Beijing HWRK Chem Co., Ltd.  with more than 98% purity. All other used chemicals were of analytical grade.v/v) acetonitrile in water with 0.05% trifluoroacetic acid. Lisinopril and borate buffer solution were used as positive and negative control, respectively. The formula (1) for computing ACE inhibitory rate is shown as follows. Wherein, Acontrol is the peak area of HA by negative control and Asample is the peak area of HA by the sample. The IC50 was calculated by the dosage and efficiency curve based on the 50% inhibition of ACE.The assay of in vitro ACE inhibitory activity was performed by RP-HPLC method by Yuan . InitialThree potential oligopeptides and positive ACEI of lisinopril were further implemented to molecular dynamics simulation. The best binding modes of 1O86–lisinopril, 1O86–NPATY, 1O86–NCHEF and 1O86–VSAIGF were selected as the start pose of MD with water environment. The MD simulations were run with GROMACS 5.0.2 using the GROMOS96 43a1 force field . The topA 500 ps NVT equilibration was performed at temperature of 300 K with position restraints applied to protein and ligand in order to relieve any bad contacts at the residues solvent interface ,52. The 50 = 61.88 ± 2.77 μM) was verified as the definite ACE inhibitory oligopeptide from adlay. According to the pharmacophore and docking models with initial ligand and oligopeptide, hydrogen bond with ALA354, hydrophobic effect with VAL518, and electrostatic network with HIS383, HIS387, HIS513 and Zn2+ were regarded as the key features for the ACE inhibitory peptides. MD study of NPATY was implemented to analyze the binding stability, and key residues obtained by pharmacophore and docking were found the stable binding of ACE.To explore the anti-hypertensive bioactive components of adlay, in silico proteolysis and virtual screening were implemented to discover the ACE inhibitory oligopeptides. In this paper, 10 sequences of adlay prolamin were in silico hydrolyzed, and 134 oligopeptides were obtained to construct the peptide library. The reverse target identification of this peptide library was implemented based on pharmacophore, and ACE was identified as the main potential target for adlay bioactive oligopeptides. Three crystal structures of ACE were used to refine the pharmacophore results, and three oligopeptides were selected due to the score and binding mode of docking and pharmacophore. Then, ACE inhibitory activities of three oligopeptides were assayed by RP-HPLC. NPATY (ICThis study presented the novel research method for discovering the active oligopeptides from TCM. Compared with the methods of extraction, isolation and random bioactivity assays, the combination of in silico proteolysis, virtual screening, biochemistry assay and MD study could obtain the bioactive oligopeptides from TCM rapidly and efficiently. Actually, if the absorption and metabolism of oligopeptides could be considered more, it would be more accurate to clarify the pharmacological mechanism of TCM proteins and peptides. In this paper, the oligopeptides from TCM were again identified with better biological activity. Therefore, it is necessary for TCM primary metabolites to discover their pharmacological activity in the future research.


Phaseolus vulgaris L.) is among the most important grain legumes for human consumption worldwide. Portugal has a potentially promising common bean germplasm, resulting from more than five centuries of natural adaptation and farmers' selection. Nevertheless, limited characterization of this resource hampers its exploitation by breeding programs. To support a more efficient conservation of the national bean germplasm and promote its use in crop improvement, we performed, for the first time, a simultaneous molecular marker  and seed and plant morphological characterization (14 traits) of 175 accessions from Portuguese mainland and islands traditional bean-growing regions. A total of 188 different alleles were identified and an average pairwise Cavalli-Sforza and Edwards' chord genetic distance of 0.193 was estimated among accessions. To relate the Portuguese germplasm with the global common bean diversity, 17 wild relatives and representative accessions from the Andean and Mesoamerican gene pools were evaluated at the molecular level. No correlation was detected between the variability found and the geographic origin of accessions. Structure analysis divided the collection into three main clusters. Most of the Portuguese accessions grouped with the race representatives and wild relatives from the Andean region. One third of the national germplasm had admixed genetic origin and might represent putative hybrids among gene pools from the two original centers of domestication in the Andes and Mesoamerica. The molecular marker-based classification was largely congruent with the three most frequent phaseolin haplotype patterns observed in the accessions analyzed. Seed and plant morphological characterization of 150 Portuguese common bean accessions revealed a clear separation among genetic structure and phaseolin haplotype groups of accessions, with seed size and shape and the number of locules per pod the most discriminant traits. Additionally, we used molecular and morphological data to develop a series of smaller core collections that, by maximizing the genetic and morphological diversity of the original collection, represents the Portuguese common bean germplasm with minimum repetitiveness. A core collection with 37 accessions contained 100% of the genetic variation found in the entire collection. This core collection is appropriate for a more detailed characterization and should be explored, as a priority, in national and international common bean breeding efforts. Furthermore, the identified intermediate accessions (with admixed genetic origin) may have novel genetic combinations useful in future bean breeding.Common bean ( Phaseolus vulgaris L., 2n = 2x = 22), from now on designated as bean, is a predominantly self-pollinated herbaceous annual plant, grown worldwide for its edible green pods and dry seeds. As a member of the Fabaceae (legume) family, it has an important role in sustainable agriculture due to its capability for fixing atmospheric nitrogen, allowing for the reduction in fertilizer use. Bean has recognized benefits to human health and nutrition, with its high protein content, dietary fiber, and essential vitamins and minerals .In Portugal, bean represents about 75% of the grain legumes consumed by humans but, although there are many bean landraces still in cultivation, especially in the northern and central regions of Portugal, national production covers only 9.4% of the country's demand , and Nueva Granada, Peru and Chile (within the Andean pool). In general, the Mesoamerican germplasm have small (<25 g 100-seed weight−1) or medium (25–40 g 100-seed weight−1) seed sizes and S or B phaseolin types (the major seed-storage proteins of beans). In contrast, in the Andean germplasm, seeds are predominantly large (>40 g 100-seed weight−1) and have T, C, H, or A phaseolin types , 6 were representatives of the Andean gene pool  and the 6 wild accessions were from Argentina, Bolivia, Colombia, Guatemala, Mexico and Peru. A complete list of the accessions studied, along with their “passport” information, is available in Supplementary Table The 175 Portuguese bean accessions used in the genetic diversity study were selected based on the national geographical distribution of their collecting sites. One hundred and seven accessions were collected by visiting small farms in a field expedition that took place in 2005  were sown. Individual young leaves were collected, then immediately frozen in liquid nitrogen and stored at −80°C until DNA isolation. DNA was isolated using a modified CTAB protocol developed by Torres et al. . DNA quaThe selection of the 21 microsatellite markers  used was made based on their uniform distribution and coverage of the 11 chromosomes of the bean genome  of PCR products were estimated using SAGAA DNA marker for phaseolin-type diversity analysis was used  and the observed (HO) and expected heterozygosity  of each accession. The effective number of alleles (Ne) was estimated using GenAlEx 6.5 , as the measure of the number of alleles per locus independent of sample size was calculated using FSTAT v2.9.3.2 program package  within each accession was estimated after controlling for differences in sample size using the rarefaction method  of each microsatellite marker was calculated by PowerMarker v3.23 software  as well as among accessions originating from different regions in Portugal, Kruskal-Wallis  and Wilcoxon  non-parametric tests were performed using SAS/STAT® Software  were tested on individual level across microsatellite markers by repeated measures analysis of variance using PROC GLM in SAS/STAT® Software  among Portuguese accessions was tested using the method of Rousset . For thiTo test for the existence of genetic structure in this collection, an analysis of molecular variance (AMOVA) was performed, based on SSR markers, using ARLEQUIN v3.0  replicates assuming admixture model and correlated allele frequencies. No prior information was used to define the clusters. The choice of the most likely number of clusters (K) was carried out by comparing the average estimates of the likelihood of the data, ln[Pr(X|K)], for each value of K, as well as by calculating an ad hoc statistic ΔK, based on the rate of change in the log probability of data between successive K values as described by Evanno et al.  and Wilcoxon  non-parametric tests were performed using SAS/STAT® Software  in 3.0 m-rows spaced 50 cm using traditional farmers' production management. The soil was a haplic luvisol, and during the vegetative cycle (May to August/September) the temperature ranged from 18°C to 28°C . Accessions revealing a climbing habit were stacked to permit a full development of the plants.Phaseolus vulgaris L. .In total, nine quantitative  and five qualitative  traits were measured  was performed using PROC PRINCOMP from SAS software.Univariate analysis of variance (ANOVA) using PROC GLM in SAS software was conducted to test the mean differences between true-type groups for the nine quantitative traits. F-test significance level from the analysis of covariance was used as the selection criterion. The STEPDISC procedure allowed the selection of a subset of quantitative traits for use in discriminating among the groups, through a stepwise discriminant analysis. Partial r2 statistics accounted for the significant discriminative potential of each trait, while Wilks' lambda was used to denote the statistical significance of the discriminatory power of the model function. The discriminant function, with the chosen subset of quantitative traits, was then evaluated for its performance in classifying the accessions correctly into their respective true-type group. Error rates (probabilities of misclassification) were estimated using cross-validation. The discriminant function was finally applied to the total data set (including offtype accessions), once again using the DISCRIM procedure.Discriminant analysis (DA) using SAS software was performed to determine which of the nine quantitative traits were the most useful for maximum discrimination among true-type groups of accessions. With DISCRIM procedure, a discriminant criterion was developed to classify each accession into one of the groups. For the five qualitative traits, a likelihood-ratio chi-square test in SAS software was used to test for differences in the number of accessions having a particular trait state across true-type groups. The same procedure was used to test for differences across the seven defined Portuguese regions of origin of the accessions evaluated: the north coast, northern interior, central north, central south, south, in Portugal mainland, and the two Autonomous Regions, the Azores and Madeira.To select a core collection that could represent the whole collection with minimum repetitiveness in future studies, we used molecular marker and morphological data. Based on the allelic diversity profile obtained in the Portuguese bean collection after the screening with 21 SSRs, one representative individual per accession was chosen. This choice relied on an individual with the most frequent genotype among each accession. Following this procedure, we used the molecular data from the 150 representative individuals of the same accessions that were phenotyped in the subsequent analysis.The qualitative traits used were growth habit, seed shape, seed coat color, seed coat pattern, as already defined, and seed size. Seed size  was estimated from the originally measured 100-seed weight trait, by converting it into a categorical variable, based on a classification widely used on bean by several authors for instance, Singh et al. : (1) smaTwo different complementary algorithms were used based on a maximization (M) strategy. On the first approach, we employed the standard M strategy  as well as observed (HO) and expected heterozygosity (HE), calculated using GENEPOP v4.0. Repeated measures analysis of variance was carried out using PROC GLM in SAS. Post-hoc Bonferroni's adjustments were used to compare the means of diversity estimates from different core subsets, with the entire collection as control. Morphological diversity of core subsets and of the entire collection was assessed by calculating the average number of traits states and Shannon's diversity index (HSh). The principal coordinate analysis (PCoA) based on molecular data was performed using NTSYS-pc v2.10  . All 21 SSRs analyzed were polymorphic with an average of 10.71 alleles per locus, varying from 2 to 26 alleles each (Supplementary Table Ne) was 1.113 for both groups (Table Nar) among the two groups: 1.098 in the Portuguese accessions and 1.100 in the race representative and wild relative bean accessions  among groups. No significant differences [PKW)( = 0.000] for the Andean and Mesoamerican race representatives and wild bean relatives than for the Portuguese accessions (Supplementary Table The diversity parameters PKW)( = 0.147] and expected heterozygosity   and 60 presented a patterned seed coat. The chi-square test indicated that there were no significant differences in the number of accessions having or not a seed pattern among the true-type groups . Accordingly, the AP1 group of accessions had no red-colored accessions and most of its accessions were white, whereas both the B1P3 and B2P2 groups presented red seed coat accessions. Moreover, the majority of the accessions from the B2P2 group were pink.The four most frequent colors of plain seed coats among the Portuguese beans were white, pink, brown and red. For this trait, there were significant differences among the true-type groups of accessions   [P(χ2) = 0.211].Moreover, there were not significant differences in the distribution of accessions from each region of origin when comparing true-type and offtype groups [r = 0.706), elongation (r = 0.841) and flatness index (r = 0.759). Not surprisingly, there were also strong positive correlations between the number of locules per pod and the number of seeds per pod (r = 0.773), between elongation and flatness index (r = 0.771) and between flatness and flatness index (r = 0.740). On the other hand, there was a strong negative correlation between flatness index and seed width (r = −0.704) .The biplot projecting the 150 bean accessions (96 true-type and 54 offtype) and the nine quantitative morphological traits was constructed using the two first principal components explaining 70.8% of the variance found among accessions Figure . PC1 difr2 statistics, the most useful traits in discriminating accessions among the three true-types  were misclassified (data not shown).The discriminant function, resulting from these five traits, was applied to the total data set (including offtype accessions) resulting in a clear separation among the true-type groups, with offtype accessions in intermediate positions Figure .To form core collections based on 150 individual genotypes representing the Portuguese bean germplasm, molecular  and morphological data  were used. The five qualitative traits used were growth habit, seed shape, seed coat color, seed coat pattern and seed size.Of the Portuguese accessions analyzed, 73.3% were considered of large seed size (100-seed weight >40 g), 23.3% of medium size (100-seed weight 25–40 g) and 3.3% of small size (100-seed weight < 25 g)  was observed. Accessions from this group represented 14% of the entire collection while, for instance, in core sets with 37 and 20 accessions, this percentage increased to 27 and 35%, respectively , based on proportion-of-shared-alleles distance matrix, was performed. Two axes were generated with a relatively large eigenvalue, capturing 72.03% of the variation in our data Figure . A clearTo enrich the current knowledge of molecular and morphological diversity of the Portuguese bean germplasm, we performed, for the first time, a simultaneous molecular marker screen and seed and plant morphological characterization of representative accessions of all national bean-growing traditional regions. Moreover, this bean collection was compared at the molecular level with representatives of the Andean and Mesoamerican gene pools. On the basis of these data, core subsets of the Portuguese germplasm were developed. High genetic and morphological diversity was detected among accessions of this collection. The majority of the Portuguese accessions analyzed were more related to the Andean gene pool. From the entire collection, 37 accessions were selected to form a core collection that retains all the diversity within this germplasm, with minimum repetitiveness. This subset of accessions will allow an increased efficiency of the forthcoming more detailed characterization of this valuable resource. This information is vital to support a more effective conservation of the Portuguese bean germplasm and to promote its use in both national and international breeding programs of this important crop.The study of the genetic structure and phaseolin type diversity of the collection showed its extent of admixture and the bean gene pool more related to the Portuguese germplasm. One- third of the accessions evaluated were of an intermediate nature. By examining the position of the Portuguese accessions on the Neighbor-joining tree, we conclude that most of the national germplasm is genetically closer to the Andean gene pool, despite the existence of accessions that were distributed with representatives from both gene pools (Mesoamerican and Andean). The analysis of genetic structure and phaseolin types divided the collection into three main clusters: 14.8% of accessions into type AP1 , 20.6% of accessions into type B1P3 and 32% of accessions into type B2P2 . The remaining 32.6% of the accessions had an intermediate or admixture nature and were classified as offtypes.P. vulgaris collections from all over Europe, from the Mediterranean countries Portugal, Spain, France and Italy , with novel genetic combinations not typical from the primary centers of domestication, emphasize the potential value of the Portuguese germplasm, providing an additional level of complexity and new gene combinations not yet explored in breeding. In fact, introgression between the Mesoamerican and Andean gene pools had already been detected in the Iberian Peninsula using phaseolin, allozymes and morphological data . Similar genetic diversity values for these microsatellites were observed by other authors, in studies with wild and domesticated bean accessions from the Andean and Mesoamerican gene pools  proved to be sufficient for discriminating true-type groups of accessions. The discriminant model, built on these five traits, was validated with 86.5% of accessions correctly assigned to the corresponding true-type group, previously classified based on genetic structure and phaseolin type analysis. This procedure was applied only for true-type groups, since accessions classified as offtypes possess intermediate forms between true-type groups that could bias the discriminant function. The discriminant analysis was subsequently applied to all the collection, and the offtype accessions were positioned among the true-type groups, as expected.Isolation by distance analysis showed that there is no correlation between the geographic distance and the genetic differentiation among accessions. This absence of correlation may be due to an extensive gene flow resulting from traditional seed exchange practices such as trade at local markets or between farmers' family and neighbors. Those practices led to a maintenance of common bean genetic diversity in Portugal, denoted by the presence of almost all identified true-type groups in each region of the country.On the other hand, we observed that the larger proportion of accessions came from the north central and north interior regions . This fact is expected since those are the regions with a more suitable climate to cultivate bean, with higher values of annual precipitation associated with warmer summers, reducing the need for additional costly irrigation.In order to take full advantage of this valuable germplasm, it is extremely important to complement this initial molecular and morphological characterization with more detailed phenotypic evaluations. This will allow the identification of accessions with increased breeding value; for instance, those more adapted to biotic and abiotic stresses, or possessing particular consumer-oriented quality traits. Yet, this is a time-consuming task in an extended collection such as this. To better exploit this germplasm collection, we developed smaller-size core collections representative of all the diversity from the original collection, with no redundancy. To select the accessions member of the core collection, we combined morphological diversity data  with molecular marker data. Additionally, Shannon's diversity index was used to evaluate if there were significant differences in the morphological diversity of different core collection sizes. With this combined approach, we had the advantage of using not only simple easy-to-score phenotypic or passport data, normally more prone to missing data and environmental interaction dependence, but also DNA markers that can more accurately represent the genetic relationships and diversity of the entire collection (Díez et al., Furthermore, the developed core collections, containing high genetic and morphological diversity, are potentially useful for conducting more detailed phenotypic assessments to deepen the characterization of this germplasm and speed up its use in breeding. This is especially important for traits highly influenced by the environment, such as yield potential. In the present study, since data was collected from one single environment, we limited the characterization mainly to highly heritable morphological traits that generally display low genotype by environment interaction (Debouck and Hidalgo, Moreover, the Portuguese core collection developed in this work may now integrate larger international bean collections to deepen the coverage of the worldwide diversity of this important crop. This could be of extreme importance to enrich international breeding or genetic programs and overcome several constraints affecting bean production in different parts of the world. Representative core collections have already proved to be useful tools for conservation purposes and to study resistance to abiotic and biotic stresses or other agronomic traits in several crop collections, such as pea (Grünwald et al., Finally, with this study, the complexity of the Portuguese bean germplasm, reflected by its genetic structure, was revealed. This knowledge is of vital importance for future genetic studies, using this underused but promising germplasm, such as association mapping studies. Those genome-wide studies might contribute to uncovering the genetic basis of several interesting traits and develop molecular tools to assist the selection of bean accessions with improved quality and agronomic performance.STL and MD performed the DNA isolation and the SSR genotyping. STL participated in the seed morphological characterization, data analysis, and drafted the manuscript. MMV coordinated the field experiment and performed the plant agronomic traits evaluations. ZS performed the statistical analysis of the molecular and phenotypic data and revised the manuscript critically. MCVP designed and coordinated the study and participated in the drafting and revising of the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Researchers have identified subtle differences in blood measures of methylation between exposed and unexposed populations, with differences in methylation ranging from less than 2% up to 10%.IGF2 gene halved transcription, while a 1% decrease doubled transcription.Whether such small changes are biologically meaningful is very much an open question in environmental health research. Previous genetic research has suggested that the location of epigenetic alterations can be a crucial determinant of the overall effect. For example, one study estimated that a 1% increase in methylation in a specific area of the However, it is important not to lose sight of the fact that epigenetic changes occur against the backdrop of the entire genome. “We can’t forget that it’s still a layer of information on top of our genetic code,” says Breton. She says that genome-wide association studies and many years of looking at genetic variation have shown that some diseases have an important genetic component.“You really need to start thinking about how the epigenome responds to the environment and affects disease risk—but on top of the backbone of the genetic code,” Breton says. “You also need to think about measuring and adjusting for genetic variation or looking at interactions between the genetic code and the epigenetic code.”Ultimately, researchers hope to be able to determine if specific epigenetic alterations bridge the gap between an environmental exposure and a particular outcome. “I think this is a very useful paper to a lot of people in terms of having a kind of common lexicon to work with [in terms of how to identify and describe small changes], and I like that they encourage discussion about small effect sizes,” says Daniele Fallin, a professor at the Johns Hopkins Bloomberg School of Public Health who was not involved in the review. “We are at that stage where there is still so much to learn; bringing people together and then communicating a common landscape are important.”


The analysis of population structure has many applications in medical and population genetic research. Such analysis is used to provide clear insight into the underlying genetic population substructure and is a crucial prerequisite for any analysis of genetic data. The analysis involves grouping individuals into subpopulations based on shared genetic variations. The most widely used markers to study the variation of DNA sequences between populations are single nucleotide polymorphisms. Data preprocessing is a necessary step to assess the quality of the data and to determine which markers or individuals can reasonably be included in the analysis. After preprocessing, several methods can be utilized to uncover population substructure, which can be categorized into two broad approaches: parametric and nonparametric. Parametric approaches use statistical models to infer population structure and assign individuals into subpopulations. However, these approaches suffer from many drawbacks that make them impractical for large datasets. In contrast, nonparametric approaches do not suffer from these drawbacks, making them more viable than parametric approaches for analyzing large datasets. Consequently, nonparametric approaches are increasingly used to reveal population substructure. Thus, this paper reviews and discusses the nonparametric approaches that are available for population structure analysis along with some implications to resolve challenges. Population structure analysis is a major area of interest within the field of genetics and bioinformatics. Population structure is the grouping of individuals into subpopulations based on observable characteristics, such as culture, language, geographical region, and physical appearance . Since pThe inference of population structure from genetic markers is very helpful in different applications, such as genome-wide association studies (GWAS) –8 and fon individuals using m genetic markers to K subpopulations. Therefore, research in population structure addresses the following problems: how to detect population structure, how to assign individuals to their corresponding subpopulation, how to determine the optimal number of subpopulations, how to reduce the number of genetic markers needed for inference of population structure, how to infer population structure at a fine scale, and finally, how to handle large genetic datasets 75], the ster. In , the Cluster. In  was usedt method .Nonparametric approaches are increasingly being used to reveal population structure because of their great advantages of efficiency in handling high-dimensional genetic datasets . Due to PCA is considered computationally efficient and performs well in detecting the genetic structure of populations. However, it is also argued that PCA not be efficient when used with correlated markers that naturally arise in any genetic data, especially in densely genotyped data. The problem is that a large number of redundant and correlated markers may mask the real structure of data. In practice, with large genotype data, there are linked markers due to linkage disequilibrium (LD) , which iMany distance-based methods have been developed to resolve the problem of clustering individuals into subpopulations. These methods have utilized different clustering techniques that required a matrix of pairwise distance/similarity between individuals. Allele-sharing distance (ASD) is widely used for this purpose. In , it is sK . In con K-means , SHIPS [ K-means , NETVIEW K-means , ipPCA [ K-means , 16, 58, K-means ). HierarA major challenge in population structure analysis is the estimation of the optimal number of subpopulations . Gap statistics  have oftThe analysis of population structure is used to obtain a clear insight into the underlying genetic population substructure and is a crucial prerequisite for any analysis of genetic data, such as genome-wide association studies, to eventually reduce false positive rates, and for forensics to develop reference panels that provide information on an individual’s ancestry. Single nucleotide polymorphisms (SNPs) are the most widely used markers to study the variation of DNA sequences between populations. Data preprocessing is a necessary step to assess the quality of the data before analysis, including the assessment of the call rates of both SNPs and individuals, minor allele frequency, and relatedness between individuals, where a threshold is set to eliminate SNPs/individuals that do not meet that threshold. Additionally, the selection of ancestry informative markers (AIMs), which are the minimal set of markers required to derive population structure, is considered important in preprocessing to improve the accuracy of clustering results.After preprocessing, several analysis methods, including parametric and nonparametric, are used. Parametric approaches are impractical for large datasets because of their intensive computational cost, genetic assumptions that must be held, and sensitivity to sample size. In contrast, nonparametric approaches have the advantage of efficient computational cost with no modeling assumption requirements, making them more viable than parametric approaches for analyzing large datasets. Nonparametric approaches can be categorized into dimension reduction-based and distance-based methods. On the one hand, dimension reduction techniques are used to reduce the dimensions of genetic markers before conducting a clustering. The most used dimension reduction technique is principal components analysis (PCA), as it is implemented in EIGENSTRAT\smartpca. On the other hand, distance-based methods include computing dis/similarity matrices of the data where the clustering method is applied, such as AWclust, SHIPS, NETVIEW, and iNJclust. In these methods, similarity is measured using allele-sharing distance (ASD). ASD is a measure to determine how genetically close each pair of individuals is.All in all, as evident in the challenges introduced by the ever-growing sizes and complexity of genetic datasets, accurate and efficient analysis methods are increasingly desirable to take full advantage of these available genetic datasets.


This study examined the heritability of brain grey matter structures in a subsample of older adult twins  from the Older Australian Twins Study. The heritability estimates of subcortical regions ranged from 0.41  to 0.73 (hippocampus), and of cortical regions, from 0.55  to 0.78 . Corresponding structures in the two hemispheres were influenced by the same genetic factors and high genetic correlations were observed between the two hemispheric regions. There were three genetically correlated clusters, comprising (i) the cortical lobes ; (ii) the basal ganglia  with weak genetic correlations with cortical lobes, and (iii) the amygdala, hippocampus, thalamus and nucleus accumbens grouped together, which genetically correlated with both basal ganglia and cortical lobes, albeit relatively weakly. Our study demonstrates a complex but patterned and clustered genetic architecture of the human brain, with divergent genetic determinants of cortical and subcortical structures, in particular the basal ganglia. In the search for genes related to brain structures, it is important to know whether the relevant genes are shared by different structures or are unique to each structure. Additionally, cortical and subcortical structures are structurally and functionally related, and activity in one can help pattern the other. For instance, thalamocortical afferents are important in the ‘extrinsic’ patterning of the cortex through thalamic input10Brain structure is under strong genetic control, but how genes influence the organization of the brain is only beginning to be understood2567The adult brain undergoes structural and functional changes with ageing, and these age-related changes are influenced by both genetic and environmental factors. The regional patterning of the older brain is therefore likely to be the result of a complex interplay of development and ageing-related genes and interaction with extrinsic factors. Furthermore, developmental and lifespan changes in cortical thickness was found to associate with the underlying genetic organizational principles of cortical thicknessThe study of monozygotic (MZ) and dizygotic (DZ) twins permits the estimation of heritability of a particular phenotypic measure, which is the proportion of the phenotypic variance accounted for by genetic factors. Twin studies of the cortex and subcortical structures using neuroimaging phenotypes have shown strong genetic influences on the volumes of these structures7141517185We used the grey matter volumes in our study and both cortical and subcortical structures were extracted using FreeSurferThe descriptive statistics of our samples are given in 2 = 0.79). More finely defined individual cortical ROIs (region of interest) based on the Desikan-Killiany atlasThe heritability estimates for total (left plus right hemisphere) volumes of the subcortical regions ranged from 0.41 for the amygdala to 0.73 for the hippocampus see . For theG = 1 tenable. The ROIs of caudal anterior cingulate, cuneus cortex, parahippocampal gyrus, rostral middle frontal gyrus, superior frontal gyrus, insula cortex, thalamus, pallidum and amygdala, the specific genetic components of the left and right regions were found to be different and hence rG were not unity.The heritabilities of the corresponding ROIs of the two hemispheres were found to be similar. To compute genetic correlations and test the significance of bilateral genetic sharing an Independent Pathway Model (IPM)We evaluated the genetic correlations among different ROIs  of the whole brain. The heatmap of genetic correlations among all the ROIs is presented in c1 divided by the sum of squares of path coefficients corresponding to ac1, ac2, ac3 and the eleven specific genetic components) with the majority (89%) explained by the hippocampus, amygdala and thalamus , compared to 9.5% attributed to the cortical lobes. Similarly, the second factor explained 15.8% of the total genetic variance which represented the caudate, pallidum and putamen and the third factor explained 13.2% of genetic variance and included the four cortical lobes.Central to the aim of our study was the genetic sharing of the whole brain grey matter, including cortex and subcortical grey matter structures. A multivariate SEM was used to study the patterns of genetic correlations among the four cortical lobes and seven subcortical regions. Starting from the saturated Cholesky modelWe observed that genetic and environmental correlations between cortical lobes had similar magnitude, with the highest genetic correlation observed between the parietal and temporal lobes . In geneThe cluster analysis of the genetic correlation matrix based on the three-factor IPM also demonstrated three distinct clusters. Similar clustering was observed based on the phenotype correlation matrix obtained using the three factor IPM factor analysis .We report a comprehensive examination of the genetic influence on cortical and subcortical structures in older adults of both sexes. In general, cortical and subcortical structures had moderate to high heritability, suggesting a strong underlying genetic basis for these brain phenotypes. Our analyses examined human cortex and subcortical structures together and showed that in the context of whole brain grey matter structures, cortical lobes were genetically correlated with each other and formed a genetic cluster. The basal ganglia structures were highly correlated among themselves, forming a second cluster, and the amygdala, hippocampus, thalamus and nucleus accumbens formed a third cluster. The hierarchical division of the eleven structures into three clusters demonstrated fundamentally different genetic associations for cortical regions, basal ganglia and the other subcortical structures that are functionally and anatomically close to each other.The fact that our study demonstrated that human brain cortex genetically formed a cluster of its own and the cortex was only weakly correlated to subcortical structures provided good evidence for investigating the genetics of these two divisions of cerebral grey matter independently. The vast majority of the literatures so far have indeed examined human brain cortex10111718192425265689Our heritability estimates were moderate to high for both cortical and subcortical structures as has been previously reported141558Structures in the two hemispheres showed similar levels of heritability, and modelling showed a common genetic factor accounting for much of the heritability in corresponding regions bilaterally. This is again consistent with previous reports which showed that the two hemispheres were genetic mirror images of each other71718A previous study has shown that the influences of several common genetic polymorphisms on the brain are region specific and bilateralThe most noteworthy finding of this study is the distinct genetic influences on cortical and subcortical structures, with little genetic covariation between any of the four cortical lobes and the seven subcortical regions. In our study, the shared genetic variance for the cortical regions, to some extent, had a lobar pattern, with regions within a lobe having higher genetic correlations . For the17The basal ganglia  formed a genetic cluster and the parts of the basal ganglia had either no genetic correlation  or a weak but negative one (caudate) with cortical volume measures. The cluster comprising the amygdala, hippocampus, and thalamus had moderate genetic correlations with the cortical regions, and the correlation between hippocampus and temporal lobe was relatively strong. Within the cluster that comprised the amygdala, hippocampus, nucleus accumbens and thalamus, the amygdala and hippocampus had a high genetic correlation between them, and both structures, along with the nucleus accumbens, had moderate genetic correlations with the thalamus.The correlations were estimated with ICV as a covariate. This is because a major proportion of the shared genetic variance between different brain regions is explained by a common genetic factor which also accounts for head size, and its proxy, the ICV333536The regionalisation of the brain occurs early in development and the precursor of both cortical and subcortical structures is the prosencephalon, which divides into the telencephalon and diencephalonThe human brain distinguishes from that of other species with the enormous expansion of the neocortex relative to total brain volume. With evolution, cortical thickness increases together with more dramatic enlargement in cortical surface size,For the subcortical structures, Our findings suggest that the volumes and sizes of cortex and subcortical structures in ageing stages are not determined by a single set of genes, which are consistent with previous reports, but with a few notable differences. A previous study of middle aged male twins556The OATS cohort had mean age of 70 years (range 65–85), and one would expect that the volumes of brain structures at this age are an end result of early developmental and later degenerative changes. Age-related changes in cortical thickness were found to follow closely the genetic organization of the cerebral cortex, and genetic factors contributed to cortical changes through life4531Our study has a number of limitations. First, we subdivided the cortex using a non-genetically based atlasIn conclusion, this study is the first attempt to examine the genetic correlations between human cortex and subcortical structures, using the twin design. The data showed that cortical and subcortical structures had moderate to high heritability, and formed three genetic clusters. The cortical lobes  were genetically correlated with each other and formed one cluster. The basal ganglia  were highly correlated with each other, forming a second genetic cluster, and the amygdala, hippocampus, thalamus and nucleus accumbens formed a third cluster. Additionally, the genetic influences on brain structures were bilaterally symmetrical. This patterning of the heritability of brain structures has important implications for investigations into the genetic blueprint of the human brain.Our study cohort was drawn from Wave 1 of the Older Australian Twins Study (OATS), a study of twins aged 65 years or older living in the three Eastern states of Australia  primarily recruited from the Australian Twin Registry (ATR). Methodology of OATS has previously been described in detailMRI data were obtained on three 1.5 Tesla scanners and a 3 Tesla scanner owing to the multi-site nature of this study. Siemens Magnetom Avanto and Sonata scanners  with similar years of manufacture and upgrade were used in centres 2 (Victoria) (114 participants) and 3 (Queensland) (92 participants), respectively. In centre 1 , a 1.5 T Philips Gyroscan scanner  (80 participants) was used initially, followed by a 3 Tesla Philips Achieva Quasar Dual scanner (36 participants). The acquisition protocols and parameters were tested and matched between the centres through standardization of spatial resolution and slice thickness, using a 3D phantom to correct geometric distortions, and using five volunteers who were scanned on the four scanners3. Two 3D T1-weighted scans were acquired for each participant for an increased signal-to-noise ratio (SNR).The 3D T1-weighted MRIs scans were used for computing the neuroimaging phenotypes for cerebral cortex and subcortical structures. 3D T1-weighted volumetric sequence was performed using a similar protocol for the 1.5 Tesla scanners in the three centres with in-plane resolution = 1 × 1 mm, slice thickness = 1.5 mm, slice number = 144, TR (Repetition time) = 1530 ms, TE (Echo time) = 3.24 ms, TI (Inversion time) = 780 ms, and flip angle = 8. The acquisition parameters for the 3 Tesla Philips scanner in centre 1 were: TR/TE = 6.39/2.9 ms, in-plane resolution = 1 × 1 mm, slice thickness = 1 mm, slice number = 190, resulting isotropic voxels of 1 × 1 × 1 mmScans were excluded if they failed visual quality control. Both cortical and subcortical structures were extracted using FreeSurfer v5.3.05457Equality of means between the two zygosity groups for the continuous measurements  was assessed by t-test, and equality of proportion of sexes was assessed by chi-square test. Since the t-test and chi-square test may not be valid in the case of dependent observations (among MZ and DZ pairs), p-values were computed using a permutation procedure . Zygosita, c, and e with the corresponding variance components asThe phenotypic covariance between the twin pairs can be modelled as a function of additive genetic (A), shared environmental (C) and unique environmental (E) components using the mixed effects linear model or SEM. Under SEM, the model containing the three latent factors , known as the ACE model, was fitted to estimate the heritability . For parG) between all possible pairs of the seven subcortical and thirty-four cortical regions. Likelihood ratio tests were used to compare the bivariate ACE model with AE, CE and E models. Since the AE model was not parsimonious for all pairs of correlations, the genetic correlations were finally reported based on the ACE model  and three specific factors  for each ROI was used to assess the genetic common and specific factors between the corresponding ROIs of the two hemispheres (c and cs)c1 = ac2; cc1 = cc2; ec1 = ec2) and then compared with a reduced model AE  and CE  using the likelihood ratio test. For most of the ROIs, the AE model was as good as the ACE model (p-AE > 0.5 and minimum Akaike information cirteria (AIC); G = 0 and bilateral symmetry (rG = 1).In addition to the genetic correlation analysis using a bivariate Cholesky model, an independent pathways model with three common factors  R packageTo evaluate the pattern of genetic clustering among the cortical and subcortical ROIs, we have undertaken a multivariate SEM analysis. Based on the factor and cluster analysis of the phenotype correlation matrix, we decided a three factor IPM for the eleven ROIs. A comparison within the three factor IPMs showed that the model with a single common shared environmental and three genetic and unique environmental factors . Within the Cholesky models, the model with three latent components was not comparable to the eleven latent component model . HoweverHow to cite this article: Wen, W. et al. Distinct Genetic Influences on Cortical and Subcortical Brain Structures. Sci. Rep.6, 32760; doi: 10.1038/srep32760 (2016).