코로나19는 비말을 통해 전염되는 호흡기 질환으로 건물의 실내 공간은 코로나19의 대규모 감염에 매우 취약한 곳이다. 집약된 토지 이용으로 인해 수많은 사람들이 고층의 건물에 밀집해 있는 도시 환경은 이러한 질병에 더 취약할 수 있다. 뿐만 아니라 도시의 인구 분포는 시간에 따라 역동적인 변화를 보이기 때문에 코로나19와 같은 전염병에 대한 역학 조사의 성공은 도시 인구의 시공간적 변화를 얼마나 잘 이해하는지에 달려있다. 하지만 특정 시간대에 특정 건물에 분포하고 있는 현재 인구 밀도를 파악하는 것은 무척 어려운 일이다. 따라서 본 연구는 특정 시간대의 도시 인구의 수평적, 수직적 분포를 보다 정확하게 추정하기 위한 대안을 제시하고자 한다. 보다 구체적으로 지리가중회귀(GWR) 모델에 기반한 대시메트릭 매핑 기법을 이용하여 건물 단위의 현재 인구를 추정하였다. 일반적으로 대시메트릭 매핑 기법은 보조 자료를 사용하여 기존의 공간 스케일을 넘어 보다 상세한 수준의 인구 분포를 추정할 수 있도록 해준다. 본 연구에서는 건물의 용도와 연면적을 보조 정보로 활용하였으며, GWR 모델을 이용하여 지역적으로 이질적인 인구 분포 특성을 반영하였다. 연구 결과, 서울시 전체에 걸쳐 집계구보다 상세한 건물 단위 수준의 인구 분포를 추정할 수 있었다. 건물 단위의 현재 인구 추정은 코로나19와 같은 팬데믹 전염병의 역학 조사나 효과적인 방역 대책 수립을 위한 중요한 기초 자료로 활용될 수 있을 것으로 기대한다.

본 연구의 주된 목적은 벡터-기반 보조 정보를 사용하는 대시메트릭 매핑의 방법론을 정련화하고, 그것의 GIS-기반 실행 프로그램을 개발하여, 2000년 서울시 인구밀도 분포도 제작에 적용하는 것이다. 대시메트릭 매핑은 해당 변수와 공간적 연관성을 가지는 보조 정보를 사용하여, 해당 변수의 분포 패턴을 보다 정확하게 재현하는 지도화 방식을 의미한다. 즉, 대시메트릭 매핑은 임의적인공간단위의 데이터를 보조 자료를 이용하여 변환함으로써 기저에 있는 통계적 밀도면(statistical density surface)를 복원하여 제시하는 주제도 제작 기법이다. 중요한 연구 결과는 다음과 같다. 첫째, 보조 정보가 가진 범주의 수와 범주 별 가중치 산출 방식을 모두 포괄하는 다중-클래스 대시메트릭 매핑의 일반식이 도출되었다. 둘째, 기존의 연구들에서 사용된 가중치 부여 방식을 정리하여‘인구 비중법’,‘ 표준 밀도법’,‘ 회귀분석법’으로 체계화하였다. 셋째, GIS 환경 하에서 대시메트릭 기법이 실행되는 프로그램이 제작되었다. 넷째, 서울의 2000년 522개 동 별 인구 수를 기본 데이터로 하고 벡터-기반의 토지이용현황도를 보조 정보로 하여 세 종류의 대시메트릭 매핑을 실시한 결과 코로플레스 매핑 보다는 기저의 통계적 밀도면을 보다 잘 반영하는 인구밀도 분포도가 제작되었다.

Root-knot nematode, Meloidogyne incognita is a virulent pest of solanaceaous crops worldwide. The M. incognita resistance gene Me7 derived from Capsicum annuum CM334, is located on chromosome 9. In the present study, an F2 population derived from a cross between ECW03R and CM334 was used to locate the Me7 gene. An F2 population was inoculated using approximately 1,000 second-stage juveniles per individual plant. Phenotype screening was done 45 days after inoculation by using gall index system. The phenotype study of 503 F2 individual showed 391 resistant and 112 susceptible plants. The 3:1 phenotypic ratio confirmed that resistance phenotype is controlled by a single dominant gene. Previously reported two markers were tested to reveal the linkage of markers to phenotype. Two markers, CAPS_F4R4 and SCAR_PM6a were located at 4.3 and 2.7 cM from the resistance gene, respectively. Additional SNP markers were developed using CM334 reference genome information to narrow down the position of the gene, but no closer markers could be developed due to errors of DNA sequence assembly. The closest marker was positioned on telomere of the chromosome 9 long arm, where tens of other NB-LRR genes are clustered. NB-LRR genes are being used as candidates to identify the Me7 gene.

Assessing genetic diversity, population structure, and linkage disequilibrium is important in identifying potential parental lines for breeding programs. In this study, we assessed the genetic and phenotypic variation of 174 normal maize (Zea mays) inbred lines and made association analyses with respect to nine agronomical traits, using 150 simple sequence repeats (SSR). From population structure analysis, the lines were divided into three groups. Association analysis was done with a mixed linear model and a general linear model. Twenty one marker-trait associations involving 19 SSR markers were observed using the mixed model, with a significance level of P<0.01. All of these associations, as well as 120 additional marker-trait associations involving 77 SSR markers, were observed with the general model. Two significant marker-trait associations (SMTAs) were detected at P ≤ 0.0001. In the mixed linear model, one locus was associated with water content, two loci were associated with 100-kernel weight, setted ear length, ear thickness and stem thickness; three loci were associated with ear height, four loci were associated with total kernel weight and five loci were associated with plant height. These results should prove useful to breeders in the selection of parental lines and markers.

Shoot-fresh-weight (SFW) is one of the parameters, used to estimate the total plant biomass yield in soybean. Understanding the genetic and molecular basis of SFW could help increase the total biomass production. In this particular study, we identified QTLs associated with SFW in a Recombinant Inbred Line (RIL) population derived from interspecific cross of PI483463 and Hutcheson. A total of 551 (535 SNP and 16 SSR) markers, were found to be polymorphic between the parental lines and were used to screen the RILs to develop the genetic map. Linkage analysis and QTL mapping were performed using with the software QTL IciMapping version 4.0, with the minimum LOD score of 3.0 and estimating the likelihood of a QTL and its corresponding effects at every 1cM. QTLs with LOD value > threshold LOD, as determined by 1000 permutation tests at p > 0.05 were considered as significant QTLs. The analysis identified a total of 5 QTLs associated with shoot fresh weight over two environments, with the phenotypic variation (PV) ranging from 6.34 to 21.32%, and the additive effect from -0.54 to 0.33. Among these QTLs, qFW1314_19_1 had the largest LOD scores, with PV of 21.32%. Interestingly, three QTLs, qFW2013_19_1, qFW2014_19_1, and qFW1314_19_1 identified on chromosome 19(L), showed negative additive effects, indicating the contribution from the wild parent PI483463. The QTLs identified in this study can be the targets to identify the candidate genes for the SFW and can help in developing cultivars with increased biomass potential.

Seed weight (SW), often expressed as 100-seed weight (HSW), is an important yield component in soybean and has been found to show positive correlation with seed yield. It is shown to behave as a quantitative trait controlled by many loci that are largely unclear. In this study, we represent the identification of chromosomal regions controlling the seed weight in soybean. We used a Recombinant Inbred Line (RIL) population, consisting of 188 lines derived from a cross of a wild soybean PI483463 (HSW: 0.85g) and a cultivated soybean cultivar Hutcheson (HSW: 14.05g) to identify the chromosomal regions controlling the SW trait. The population, along with parental samples and check, William82 (HSW: 21.2g) was grown for four years and phenotype data was recorded postharvest. A total of 535 SNP and 16 SSR markers, polymorphic between the parents were employed to genotype the RILs using Golden gate assay to develop the linkage map. Whole genome QTL scanning identified a total of 17 QTLs, spanning 10 chromosomes for the 100-seed weight. All these QTLs explained phenotypic variation (PV) in the range of 3.77 to 12.33%. Of the 17 QTLs, 2 QTLs qSWA1-1 and qSWD2-1, found to be the consistent QTLs, expressing in all the four environments. The QTL qSWD2-1 explained highest contribution to the total PV with 10.04 -12.23 %. The remaining 15 QTLs were identified in at least one environment with PV ranging up to 10.39%. The findings from this study will provide useful information to understand the genetic and molecular basis of SW and facilitate further genomic research leading to the yield improvements in soybean.

The seed shattering played a key role in the crucial step of rice domestication. Because it has been important to increase the yield human had to select the rice varieties and species with low shattering degree. The shattering habit of rice is considered to be under the relatively simple genetic control compared with other characteristics related to domestication. Several recessive genes associated with the formation of an abscission layer, sh2, sh4 and sh-h on chromosomes 1, 3 and 7, have been reported. In addition, the grain shattering of rice is considered to be caused by seed abscission. The morphology of the abscission layer can differ in many different rice varieties that show varying degrees of shattering. Accordingly, it is important to elucidate the molecular mechanism to determine why some varieties do not have abscission layers and have an easy-shattering trait. In this study, analysis of QTL for grain shattering was performed to determine the location of QTLs on the whole chromosomes of rice. Also, we tried to construct a physical map for qPs6

In this study, 80 F7:8 recombinant inbred lines (RIL), derived from a cross between dent corn and waxy corn, were evaluated for 10 grain yield and eating-related traits over a two-year period. A total of 39 quantitative trait loci (QTLs) and 74 epistatic interactions were confirmed in 2011 and 2012. All QTLs detected in 2011 and 2012, qAC9 (amylose content), qEH4 (ear height), qSEL6 (setted ear length), and q100KW10 (fresh 100 kernel weight) had higher phenotypic variance and were observed in both years; therefore, they may be considered major QTLs. We reported that the QE interaction affects (QTLs and environmental changes) for qEH4, qSEL6, and q100KW10 in discussion. Some new QTLs identified in this study were located on different loci compared with other studies. The genetic region (bin 4.08) strongly controls plant height and ear height, and results from pleiotropy and/or tight linkage. qST3 (including stem thickness) and qEH3 were co-located within two common adjacent simple sequence repeat (SSR) markers (umc2275 and umc1273), whereas qEL6 (ear length) and qSEL6 were co-located within two common adjacent SSR markers (umc2309 and bnlg238). Thus, these SSR markers are a useful selection tool for screening grain yield and yield component traits.

본 연구는 강원도 농업기술원 옥수수연구소에서 튀김옥 수수 품종개발을 위하여 육성한 79개의 자식계통들에 대하 여 대표적인 분자마커인 SSR마커를 이용하여 집단구조 및 association mapping 분석을 실시하였다. 집단구조에 대한 분 석 결과에서 79개의 튀김옥수수 자식계통들은 groups I, II, III, IV, admixed group으로 구분되었다. 4개의 옥수수 자식 계통은 group I에 포함되었고, Group II는 총 17개의 자식계 통들이 포함되었다. 그리고 6개의 자식계통들은 Group III에 포함되었으며, 22개의 자식계통들은 Group IV에 포함되었 다. 그리고 admixed group에는 30개 옥수수 자식계통들이 포함되었다. 튀김옥수수 자식계통들에 대하여 50개 SSR 마 커와 10개의 양적 형질 사이에서 association mapping 분석 을 하였다. Q GLM 분석에서는 0.01의 유의수준에서 92개의 marker-trait association을 확인하였으며, 반면에 Q+K MLM 분석에서는 0.01의 유의수준에서 6개의 marker-trait association 을 확인되었다. 본 연구에서 79개의 튀김옥수수 자식계통들 에 대한 집단구조 및 association mapping 분석의 결과는 앞 으로 강원도농업기술원 옥수수연구소에서 튀김옥수수 품종개 발을 위한 계통 육성 및 교배조합 구성 등에 유용한 정보를 제공할 것으로 기대한다.

Our study is performed to confirm the level of genetic diversity and population structure with 80 maize inbred lines (40 waxy inbred lines and 40 flint inbred lines) and to explain the genetic basis of agronomic traits using an association mapping. The 200 SSR loci are confirmed a total of 1,610 alleles in total 80 maize inbred lines. The average number of alleles per locus was 8.05. The average GD was 0.72. The average PIC value was 0.68. The average MAF was 0.40. Population structure was revealed for K=2. Total 80 maize inbred lines were divided by groups I, II and admixed group. The 14 waxy inbred lines were assigned to group I. The 45 inbred lines include 5 waxy inbred lines and 40 flint inbred lines were contained to group II. The 21 waxy inbred lines were contained in the admixed group with lower than membership threshold 0.8. Association mapping between 200 SSR markers and 10 phenotypic traits of waxy/flint maize inbred lines were performed by Q GLM and Q+K MLM. In significant level at 0.01, 72 SSR markers were associated with 10 phenotypic traits using Q GLM. The 4 marker-trait association were detected in Q+K MLM. The results derived from this study will be used for designing efficient new maize breeding programs.

In this study, we were conducted the construction of the framework map using SSR markers in the F2 population derived from a cross between waxy corn inbred line (02S6140) and sweet corn inbred line (KSS22), and also identifying of QTLs associated with eating quality traits by employing genetic linkage map of F2:3 population. The linkage map was constructed using 295 SSR markers on the 158 F2 individuals derived from a cross of 02S6140 and KSS22. The map comprised a total genomic length of 2,626.5cM in ten linkage groups and an average distance between markers of 8.9cM. Chi-square test revealed that 254 markers (86.1%) associating with all ten chromosomes exhibited a segregation of 1:2:1 Mendelian ratio. A total of 10 QTLs each for pericarp thickness (PER), amylose content (AMY), dextrose content (DEX), and sucrose content (SUC) were detected in the 158 F2 families. The number of QTL per each trait was ranged from 2 to 4, and also phenotypic variance was ranged from 4.26 to 30.71%. For PER, 4 QTLs were found to be controlled by four genomic regions at locations chromosomes 4, 5, 8, and 9 contributing 10.43, 6.71, 6.74, and 7.79% of phenotypic variance, respectively. While 2 QTLs for AMY, DEX, SUC traits, were found to be controlled by two genomic regions at locations chromosomes 4, 6, 8, and 9 contributing between 4.26 and 30.71% of phenotypic variance, respectively. Among them, 4 QTLs, such as qAMY4 (10.43%), qAMY9 (19.33%), qDEX4 (21.31%), and qSUC4 (30.71%), may be considered as a major QTLs, while the remaining six QTLs might be regarded as minor QTLs. In our study, qAMY9 for amylase content was detected on chromosome 9 in marker intervals phi027-umc1634, which was the same locus as encoding wx1 gene. Thus qAMY9 may be thought very useful molecular marker for selecting amylase content trait. The other QTLs may be thought very useful molecular marker for eating quality traits. The resulting genetic map will be useful in dissection of quantitative traits and the identification of superior QTLs from the waxy hybrid corn, and also this study may provide valuable information for the further identification and characterization of genes responsible for eating quality-related traits in waxy corn and sweet corn.

The objectives of this study were to identify QTLs for agronomic traits using introgression lines from a cross between a japonica weedy rice and a Tongil-type rice. A total of 75 introgression lines developed in the Tongil-type rice were characterized. A total of 368 introgressed segments including 285 homozygous and 83 heterozygous loci were detected on 12 chromosomes based on the genotypes of 136 SSR markers. Each of 75 introgression lines contained 0-9 homozygous and 0-8 heterozygous introgressed segments with an average of 5.8 segments per line. A total of 31 quantitative and 2 qualitative loci were identified for 14 agronomic traits and each QTL explained 4.1% to 76.6% of the phenotypic variance. Some QTLs were clustered in a few chromosomal regions. A first cluster was located near RM315 and RM472 on chromosome 1 with QTLs for 1,000 grain weight, culm length, grain width and thickness. Another cluster was detected with four QTLs for 1,000 grain weight, grain length, grain width and grain length/width ratio near the SSR marker RM249 on chromosome 5. Among the 31 QTLs, 9 (28.1%) Hapcheonaengmi3 alleles were beneficial in the Milyang23 background. ILs would be useful to confirm QTLs putatively detected in a primary mapping population for complex traits and serve as a starting point for map-based cloning of the QTLs. Additional backcrosses are being made to purify nearly isogenic lines (NILs) harboring a few favorable Hapcheonaengmi3 alleles in Milyang23 background.

We conducted a QTL analysis of grain quality traits using 117 BC3F4 and BC3F5 lines developed from a cross between Ilpumbyeo and Moroberekan. Genotypes of 117 BC3F5 lines were determined using 134 simple sequence repeat (SSR) markers. A linkage map constructed using 134 SSR markers was employed to characterize quantitative trait loci (QTL). The 117 BC3F4 and BC3F5 lines were evaluated for eleven grain quality traits in 2005 and 2006. A total of 18 QTLs were identified for eleven traits, and the phenotypic variance explained by each QTL ranged from 9.9% to 35.2%. Moroberekan alleles contributed positive effects in the Ilpumbyeo background at two QTL loci for 1,000 grain weight. Four QTLs, two for chalky rice and one each for 1,000 grain weight and head rice were consistently detected in two consecutive years indicating that these QTLs are stable. Clusters of QTLs were observed in three chromosome regions. One cluster harboring five QTLs including head rice and brown rice ratio near SSR markers RM190 and RM314 was detected on chromosome 6. Another cluster harboring grain weight and white belly was detected on chromosome 2. Increase in white belly at this locus might be due to the increase in grain weight due to the presence of the Moroberekan allele. The Moroberekan alleles at two QTL loci, gw3 and gw4 associated with increased grain weight might be useful in breeding programs to develop high-yielding cultivars.

We conducted a QTL analysis of agronomic traits using 117 BC3F5 and BC3F6 lines developed from a cross between Ilpumbyeo and Moroberekan. Genotypes of 117 BC3F5 lines were determined using 134 simple sequence repeat (SSR) markers. A total of 832 Moroberekan chromosome segments with 410 homozygous and 422 heterozygous, respectively, were detected, and the genetic distance of introgression segments ranged from 0.5 cm to 112.1 cm. A linkage map constructed using 134 SSR markers was employed to characterize quantitative trait loci (QTL). The 117 BC3F5 and BC3F6 lines were evaluated for seven agronomic traits at two locations in 2006 and 2007 and at one location in 2007. A total of 26 QTLs were identified for seven traits including days to heading, and the phenotypic variance explained by each QTL ranged from 9.2% to 24.2%. Moroberekan alleles contributed positive effects in the Ilpumbyeo background at eleven QTL loci including panicle length and spikelets per panicle. Five QTLs, two for days to heading and one each for culm length, panicle length and spikelets per panicle were consistently detected in every occasions indicating that these QTLs are stable. Among them, two QTLs, spp6 for spikelets per panicle and pl6 for paniclel length were localized in the similar region. Increase in spikelets per panicle at this locus might be due to the increase in panicle length, because both traits were associated with increase in spikelets per panicle and panicle length due to the presence of the Moroberekan allele. These Moroberekan QTLs might be useful in breeding programs to develop high-yielding cultivars.

In the previous study, 141 BC3F2 lines from a cross between the Oryza sativa cv. Milyang 23 and O. glaberrima were used to identify favorable wild QTL alleles for yield component traits. In this study, we carried out QTL analysis of four grain morphology as well as four yield component traits using 141 BC3F5 lines from the same cross and compared QTLs detected in two different generations. The mean number of O. glaberrima segments in the 141 BC3F5 lines ranged from 1 to 13 with 2.69 and 5.71 of the average means of homozygous and heterozygous segments, respectively. There was a three-fold difference in the number of QTLs detected for four traits commonly evaluated in two generations (seven QTLs in the BC3F5 vs 21 in the BC3F2 population). The percentages of the phenotypic variance explained by QTLs in the BC3F5 population were similar to or less than those in the BC3F2 population. This is probably due to the difference in the genetic composition of two populations and the environmental effects. The locations of the QTLs commonly detected in both generations were in good agreement except for one QTL for spikelets per panicle. The yield QTL, yd3 was colocalized with the spikelets per panicle, spp3. Yield increase at this locus is due to the increase in spikelets per panicle, because both traits were associated with increase in spikelets per panicle and yield due to the presence of an O. glaberrima allele. Clusters of QTLs for grain morphology traits were observed in two chromosome regions. One cluster harboring five QTLs near SSR markers RM106 and RM263 was detected on chromosome 2. This population would serve as a foundation for development of the introgression line population from a cross between Milyang 23 and O. glaberrima.