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국내 육성 벼 품종의 출수일수 및 간장 변이 분석

이창민1, 권영호2, 박향미3, 정지웅1, 박현수1, 백만기1, 하수경1, 모영준1,*

Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments

Chang-Min Lee1, Young-Ho Kwon2, Hyang-Mi Park3, Ji-Ung Jeung1, Hyun-Su Park1, Man-Kee Baek1, Su-Kyung Ha1, Youngjun Mo1,*
Korean Journal of Breeding Science 2020;52(4):389-397.
Published online: December 1, 2020

1농촌진흥청 국립식량과학원 작물육종과

2농촌진흥청 국립식량과학원 남부작물부 논이용작물과

3농촌진흥청 국립식량과학원 중부작물부 중부작물과

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*Corresponding Author (E-mail: moyj82@korea.kr, Tel: +82-63-238-5232, Fax: +82-63-238-5205)
• Received: September 28, 2020   • Revised: October 6, 2020   • Accepted: October 22, 2020

Copyright © 2020 by the Korean Society of Breeding Science

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  • Recent advances in high-throughput sequencing technologies have enabled large-scale cost-effective genotypic analyses, and consequently, obtaining reliable phenotypic data has now become a major bottleneck in data-driven plant breeding. In order to construct a phenotype database for commercial rice varieties released by the National Institute of Crop Science, Rural Development Administration, we initiated a systematic phenotype evaluation project, with the aim of investigating the major agronomic traits of Korean rice varieties released during the period between 1979 and 2017. Despite the narrow genetic background, we found that the days to heading (DTH) and culm length (CL) of the 297 Korean rice varieties assessed exhibited wide phenotypic variation under different environments. Under normal planting cultivation in 2018, the DTH ranged from 48 to 104 days in Suwon, 46 to 111 days in Wanju, and 39 to 97 days in Miryang, with CL values ranging from 59 to 134 cm, 55 to 122 cm, and 57 to 106 cm, respectively. During early planting cultivation in 2019, the DTH ranged from 56 to 113 days (Suwon), 58 to 109 days (Wanju), and 58 to 100 days (Miryang), with corresponding CL values ranging from 63 to 119 cm, 55 to 93 cm, and 51 to 115 cm. Despite the difference of one month in planting dates in 2018 and 2019, DTH in the different years and regions showed highly significant positive correlations (r=0.90-0.98), whereas CL showed positive but weaker correlations (r=0.45-0.82). Furthermore, we detected a weak, although significant, correlation between DTH and CL in each environment (r=-0.18-0.35). Analyses of additive main effects and multiplicative interaction (AMMI) were conducted for DTH and CL to identify rice varieties with stable phenotypes under different environments. We anticipate that the findings of this study will provide a useful rice phenotype database to facilitate genotype-phenotype association studies and data-driven rice breeding.
벼(Oryza sativa L.)는 우리나라의 주식 작물로 농업생산액의 40%를 담당하고 있어 경제적 산업적으로 중요한 가치를 지닌다(Yoon et al. 2014). 최근 식생활 변화로 국내 쌀 산업이 생산량과 소비량 감소로 위축되고있지만 쌀 가공식품을 이용한 가정 간편식(Home Meal Replacement; HMR) 시장은 확대되고 있으며(Lee 2017, Shin et al. 2015), FTA 쌀 시장 개방으로 다양한 수입쌀이 유입되면서 소비자들은 가격과 기호에 따라 선택할 수 있는 폭이 넓어지게 되었다(Kim et al. 2019). 이러한 시장변화 속에서 소비자들의 다양한 요구를 신속하게 충족하기 위해서는 뚜렷한 육종목표 설정과 육종효율 증진이 매우 중요하다.
우리나라 벼 품종 육성의 방향은 1960년대까지는 재래종과 도입종 등을 활용하여 수량성과 재배안정성을 높이는 것을 목표로 연구를 해왔으며 1970년대는 기존 자포니카의 생산성 한계를 극복하고자 인디카 형질을 도입한 통일형 벼 품종 개발로 주곡 자급 달성의 성과를 이루었다. 이후 1980대부터는 통일형 품종의 재배안정성을 향상시키는 한편 경제수준 향상으로 변화하는 소비자 요구에 부응하여 밥맛이 좋은 고품질 벼 품종 개발을 위해 노력하였다. 2000년대부터는 국제 경쟁력 향상을 위해 최고품질 쌀 개발과 쌀 가공산업 활성화를 위한 가공용 품종개발, 건강기능성 쌀 개발을 목표로 육종 방향이 변화해왔다(Cho et al. 2020).
벼 육종방향 설정에 있어서 출수기와 간장은 필수적으로 다뤄야할 농업형질이다. 출수기는 일장과 온도 등의 환경 요인에 반응하여 변이를 보이기 때문에 육성 품종의 재배가능지역과 재배시기를 결정하는 요인으로 작용한다(Fujino 2020). 예를 들어 감온성이 큰 조생종 벼는 생육기간이 짧기 때문에 주로 중산간지와 북부평야지에서 조기재배로 재배되며, 감광성이 큰 중만생종은 남부평야지에서 보통기재배로 주로 재배된다. 최근에는 남부평야지에서도 추석 전 햅쌀 출하를 위한 조기재배, 또는 맥류 후작 이모작을 위한 만기재배 등 다양한 시기에 조생종 벼 재배가 증가하고있으며(Park et al. 2020), 다원화된 작부체계에 맞는 품종 개발을 위해서는 출수기를 유전적으로 정밀하게 조절(fine-tuning)할 수 있어야 한다(Hori et al. 2016). 또한 간장도 재배안정성과 품질, 수량에 매우 밀접하게 관련이 있는 형질이다(Nomura et al. 2019). 가을철 태풍이 잦은 우리나라에서는 도복에 의한 피해가 많이 발생되어왔다(Kim et al. 2000). 벼가 성숙되면서 하위 절간의 좌절, 줄기의 만곡현상과 뿌리도복 등의 발생은 벼 수량 감소와 등숙불량, 수발아 발생, 수확의 어려움 등을 유발하기 때문에 반왜성 유전자인 semidwarf 1(sd1)을 이용하여 내도복성과 재배안정성이 뛰어난 벼 품종이 많이 개발되었다(Baek et al. 2019, Chang et al. 1999). 하지만 획일화된 단간 내도복 품종은 상대적으로 바이오매스 생산이 적고 과비로 인한 품질 저하와 병해충 발생 등의 문제점을 일으킬 수 있기 때문에 다양화된 재배 여건에 따라 최적화된 간장을 지니는 품종을 빠르게 육성할 수 있는 육종 수단을 마련해야 한다(Bai et al. 2018, Nagai et al. 2018)
급속도로 변하는 쌀 산업 여건과 기후변화에 대응하여 다양한 소비자 요구를 만족시키고 재배안정성이 뛰어난 품종을 신속하게 개발하기 위해서는 육종 효율을 높여야 한다(Cho et al. 2020). 이를 위해서는 국내 품종의 유전자형과 표현형을 정밀하게 평가하고 주요 농업형질을 지배하는 유전자위 및 대립인자 효과를 파악하는 것이 중요하다(Mo et al. 2020). 유전자형 평가의 경우 염기서열 분석기술이 빠르게 발달하여 비용과 시간 소요가 예전에 비하여 크게 줄어들었지만 표현형의 경우 여전히 평가에 많은 비용과 시간이 소요되며, 유전자형과는 달리 환경의 영향을 크게 받기 때문에 장기간 반복적으로 데이터를 축적하는 것이 필수적이다. 현재 국내에서 300개 이상의 벼 품종이 개발되어 국가직무육성 품종목록에 등재되었지만 각각의 품종이 개발된 시기가 달라 동일한 지역과 시기에 일괄적으로 재배되어 체계적으로 평가된 적이 드물고 표현형 데이터베이스(DB)화도 미진하여 활용에 한계가 있다. 또한 1920년부터 현재까지 진행되고 있는 지역적응시험도 전국 28개 지역에서 수행되고 있으나 평가되는 계통과 품종이 매년 변하기 때문에 표준품종과 환경변이를 고려한 정밀한 표준화가 없이는 기존에 축적된 데이터를 활용하기가 어려운 실정이다. 따라서 농촌진흥청 국립식량과학원에서는 국내 육성 벼 300여 품종의 고품질 표현형 DB 구축을 위하여 수원(중부작물부), 완주(국립식량과학원 본원), 밀양(남부작물부) 3개 지역에서 2018~2021 4개년에 걸쳐 다양한 농업형질을 동시에 평가하고 있다. 본 논문에서는 수원, 완주, 밀양에서 보통기재배(2018년) 및 조기재배(2019년)된 국내 육성 벼 품종들의 출수기와 간장 변이에 대한 연구결과를 보고하고자 한다.
시험재료 및 재배방법
농촌진흥청 국립식량과학원에서 1979~2017년에 육성된 벼 297품종을 국립식량과학원 수원, 완주, 밀양 3개 시험포장에서 2018년 보통기재배, 2019년 조기재배로 재배하였다(Supplementary Table 1). 2018년에는 수원, 완주, 밀양에서 각각 4월 25일, 5월 9일, 5월 4일에 파종하여 5월 25일, 6월 1일, 6월 5일에 이앙하였고, 2019년에는 수원, 완주, 밀양 모두 4월 10일에 파종하여 수원은 5월 10일, 완주와 밀양은 5월 9일에 이앙하였다. 재식거리는 30 cm×15 cm로 주당 1본씩 4조로, 조당 26주를 이앙하였다. 시비량은 N-P2O5-K2O를 90-45-57 kg/ha으로 질소는 기비 : 분얼비 : 수비를 50 : 20 : 30 비율로 분시하였고, 인산은 전량 기비로, 칼륨은 기비 : 수비를 70 : 30 비율로 분시하였다. 기타 재배관리는 농촌진흥청 표준 재배법에 준하여 실시하였다.
표현형 조사
국내 육성 품종 297개의 출수기와 간장은 농촌진흥청 연구조사분석기준을 참고하여 조사하였다(RDA 2012). 출수기는 총 경수의 약 40%가 출수하였을 때를 기준으로 결정하였으며 출수일수는 이앙일부터 출수기까지의 소요일수를 나타내었다. 간장은 출수 후 30 일경 이삭이 모두 추출되었을 때 생육이 일정한 10 개체를 대상으로 지면에서 이삭목까지의 길이를 측정하였다.
기상자료
기상자료는 기상청 기상자료 개방포털(http://data.kma.go.kr)의 수원, 전주, 밀양 지점 정보를 이용하였다. 조기재배 이앙시기인 5월 초부터 출수기까지의 일평균기온을 이용하였으며 이앙기부터 출수기까지의 적산온도를 품종별로 분석하였다.
통계분석
통계분석은 R (Version 3.6.3, The R Foundation for Statistical Computing Platform)을 이용하였다. 삼원분산분석을 통해 품종, 재배시기, 지역과 각 요인들의 상호작용을 분석하여 출수일수와 간장 변이에 미치는 영향을 분석하였다. 형질들의 상관분석은 PerformanceAnalytics 패키지를 이용하였다. 형질의 평균 등 기술통계와 유전형(genotype; G)과 재배환경(environment; E) 간 상호작용(G×E interaction)에 대한 AMMI (Additive Main Effects and Multiplicative Interaction) 분석을 agricolae 패키지를 이용하여 수행하였다.
재배시기 및 지역별 출수일수와 간장 변이
수원, 완주, 밀양 3개 시험지에서 이앙기부터 전 품종이 출수를 완료한 시점까지의 일평균온도는 2018년 보통기재배 시 수원 26.2℃, 완주 26.6℃, 밀양 26.2℃, 2019년 조기재배 시 수원 23.2℃, 완주 23.3℃, 밀양 23.5℃이었다(Fig. 1). 보통기재배에서 공시품종의 출수일수는 수원, 완주, 밀양 시험지 각각 평균 77일, 71일, 67일로, 각각 48~104일, 46~111일, 39~97일 사이에 분포하여 수원 > 완주 > 밀양 순으로 출수일수가 길었다. 조기재배에서 공시품종의 출수일수는 수원, 완주, 밀양 각각 평균 87일, 86일, 82일로, 각각 56~113일, 58~109일, 58~100일 사이에 분포하여 보통기재배 보다 10~15일 가량 출수일수가 길어지고 지역별로는 보통기재배와 마찬가지로 수원 > 완주 > 밀양 순으로 출수일수가 길었다(Fig. 2). 지역 간 품종별 이앙기부터 출수기까지의 적산온도는 보통기재배에서 수원, 완주, 밀양 시험지 각각 평균 1971℃, 1897℃, 1770℃로 1112~2695℃, 1137~2899℃, 945~2538℃ 범위에 분포하였고, 조기재배에서는 수원, 완주, 밀양의 적산온도 평균이 각각 2050℃, 1984℃, 1895℃로 1216~2726℃, 1238~2598℃, 1283~2411℃ 범위에 분포하였다.
보통기재배에서 공시품종의 간장은 수원, 완주, 밀양 시험지에서 각각 평균 78 cm, 73 cm, 79 cm, 범위는 59~134 cm, 55~122 cm, 57~106 cm로 밀양에서 간장이 가장 크고 완주에서 가장 작았다. 조기재배에서 공시품종의 간장은 수원, 완주, 밀양 시험지에서 각각 평균 82 cm, 71 cm, 80 cm, 범위는 63~119 cm, 55~93 cm, 51~115 cm로 수원에서 가장 크고 완주에서 가장 작았다.
출수일수 및 간장 변이에 대한 상관분석
3개 시험지의 보통기 및 조기재배에서 조사된 출수일수와 간장을 각각 상관분석한 결과는 Fig. 3와 같다. 각 시험지에서 보통기재배-조기재배 간의 출수일수의 상관계수는 수원 0.98, 완주 0.95, 밀양 0.90로 보통기재배와 조기재배의 이앙시기가 한 달 가량 차이가 남에도 불구하고 3개 시험지에서 모두 높은 정상관을 보였다. 시험지 간 출수일수의 상관계수는 보통기재배 시 수원-완주 간 0.94, 완주-밀양 간 0.95, 수원-밀양 간 0.93으로 매우 높은 정상관을 보였고, 조기재배 시에도 수원-완주 간 0.98, 완주-밀양 간 0.95, 수원-밀양 간 0.94로 마찬가지로 매우 높은 정상관을 보였다.
3개 시험지에서 보통기재배-조기재배 간의 간장의 상관계수는 수원 0.71, 완주 0.71, 밀양 0.82로 출수일수에 비해서는 다소 떨어지지만 여전히 높은 수준의 정상관을 보였다. 시험지 간 간장의 상관계수는 보통기재배 시 수원-완주 간 0.78, 완주-밀양 간 0.58, 수원-밀양 간 0.45, 조기재배 시 수원-완주 간 0.67, 완주-밀양 간 0.61, 수원-밀양 간 0.59로 수원-완주 간의 간장이 상대적으로 높은 수준의 정상관을 보였다.
출수일수와 간장의 상관계수는 수원의 보통기 및 조기재배에서 각각 0.35, 0.24, 완주의 보통기 및 조기재배에서 각각 0.30, 0.25로 낮은 수준의 정상관을 보였다(Fig. 4). 밀양의 보통기 및 조기재배에서 출수일수와 간장의 상관계수는 모두 -0.18로 매우 낮아 무상관으로 판단되었다.
출수일수에 대한 분산분석 및 AMMI 분석
각 시험지에서 조사된 공시품종의 출수일수에 대한 품종, 재배시기, 시험지역의 주효과 및 이들의 상호작용에 대한 삼원분산분석을 수행하였다(Table 1). 품종, 재배시기, 시험지역의 주효과가 모두 출수일수 변이에 유의하게 영향을 미쳤으며 3가지 요인 중 품종(57.1%) > 재배시기(31.1%) > 시험지역(7.4%)의 순으로 출수일수 변이를 설명하였다. 출수일수에 대한 각 요인들 간 2가 상호작용은 모두 유의하였으나 출수일수 변이를 설명하는 비율은 품종-재배시기 0.9%, 품종-시험지역 1.9%, 재배시기-시험지역 0.8%로 낮은 수준이었다.
육성 당시 출수 생태형을 기준으로 공시품종을 조생종(87품종), 중생종(96품종), 중만생종(114품종) 3개 그룹으로 나누어 AMMI biplot을 이용하여 출수일수 변이에 대한 유전형(G)과 재배지역(E)의 상가적 주효과 및 상호작용(G×E)을 시각적으로 제시하였다(Fig. 5). AMMI biplot에서 Interaction Principal Component 1(IPC1) 값이 0에 가까울수록 유전형과 환경의 상호작용 효과(G×E)가 낮아 해당 형질에 대한 품종의 유전적 안정성이 높은 것으로 해석할 수 있다(Misra et al. 2009). 조생종, 중생종, 중만생종 모든 그룹에서 완주에서의 G×E 상호작용 효과(IPC1: -0.91~1.62)는 수원(IPC1: -3.71~3.67)과 밀양(IPC1: -2.76~2.85)보다 낮았다. 조생종 품종 중에서 조령벼(G14), 흑진주벼(G20), 조광(G45), 화왕(G63) 등이 출수일수에 대한 유전형의 효과가 상대적으로 높아 안정성이 비교적 높은 품종이었으며 소백벼(G1), 금오벼(G18), 흑광(G32), 금영(G50), 적진주찰(G55), 통일형인 태백벼(G263) 등은 상대적으로 환경조건에 영향을 많이 받았다(Supplementary Table 1). 중생종 품종 중에서는 팔공벼(G91), 화영벼(G96), 해품(G155), 보석흑찰(G138), 적진주2호(G163) 등이 출수일수에 대한 유전형의 효과가 높은 품종이었으며, 통일형인 삼강벼(G265), 용문벼(G267), 한아름(G272) 등과 자포니카인 소비(G109), 청안(G133), 도담쌀(G151) 등이 환경조건에 영향을 비교적 많이 받았다. 중만생 품종은 백진주(G192), 호평(G197), 칠보(G213), 미호(G254) 등이 출수일수에 대한 유전형의 효과가 높았고, 자포니카 품종인 고아미2호(G194), 드래찬(G221) 등과 통일형인 장성벼(G285), 향미벼1호(G286), 세계진미(G288) 등이 환경에 의한 영향을 상대적으로 많이 받았다.
간장에 대한 분산분석 및 AMMI 분석
각 시험지에서 조사된 공시품종의 간장에 대한 품종, 재배시기, 시험지역의 주효과 및 이들의 상호작용에 대한 삼원분산분석을 수행하였다(Table 1). 품종, 재배시기, 시험지역의 주효과가 모두 간장변이에 유의하게 영향을 미쳤으며 3가지 요인 중 품종(55.5%) > 시험지역(17.8%) > 재배시기(0.3%)의 순으로 간장 변이를 설명하였다. 재배시기의 경우 출수일수 변이의 31.1%를 설명한 것과 달리 간장 변이의 0.3%만을 설명하였고, 반면 시험지역은 간장 변이의 17.8%를 설명하였으나 출수일수 변이는 7.4%만을 설명하였다. 간장에 대한 각 요인들 간 2가 상호작용은 품종-시험지역, 재배시기-시험지역이 유의하였으며 간장 변이를 각각 14.1%, 2.0% 설명하였고, 품종-재배시기 간의 상호작용은 유의하지 않았다.
AMMI biplot을 이용하여 간장의 변이를 분석한 결과 조생종과 중생종 그룹에서 완주 시험지의 G×E 상호작용 효과(IPC1: -0.28~1.86)는 수원(IPC1: -3.66~2.95)과 밀양(IPC1: -4.82~3.95)보다 낮았고 중만생종 그룹에서는 밀양 시험지의 G×E 상호작용 효과(IPC1: -1.16)가 완주(IPC1: -3.69), 수원(IPC1: 4.86) 보다 낮았다(Fig. 6). 조생종 품종 중에서 금오벼(G18), 월백(G52), 조평(G54), 아세미(G65) 등이 환경에 대한 간장 변이의 안정성이 상대적으로 높은 품종이었으며 흑진주벼(G20), 삼천벼(G16), 백일미(G69), 신평(G83) 등은 환경조건에 영향을 상대적으로 많이 받았다(Supplementary Table 1). 중생종 품종 중에서는 화영(G96), 보석흑찰(G138), 미광(G139), 영보(G157) 등이 환경에 대한 간장 변이의 안정성이 상대적으로 높았으며, 자포니카인 화중벼(G99), 농안벼(G101), 풍미(G121) 등과 통일형인 삼강벼(G265), 한아름2호(G277), 새미면(G280) 등은 환경조건에 영향을 비교적 많이 받았다. 중만생 품종 중에서는 황금노들(G214), 새고아미(G241), 안백(G247), 새칠보(G253)이 환경에 대한 간장 변이의 안정성이 상대적으로 높았으며, 자포니카인 대안벼(G171), 화신벼(G173), 온누리(G204) 등과 통일형인 향미벼1호(G286), 금강1호(G291), 목우(G292) 등이 환경에 의한 영향을 비교적 많이 받는 것으로 분석되었다.
기후변화에 대비하여 우리 주식인 벼의 재배안정성을 보장하면서 다양한 소비자 요구에도 부응하기 위해서는 맞춤형 품종을 신속하게 개발할 수 있도록 육종 효율을 증진시켜야 한다. 이를 위하여 도열병, 흰잎마름병, 벼멸구 등 주요 병해충저항성과 내침수성, 내염성, 고온 및 저온 저항성 등의 내재해성, 아밀로스 함량이나 호화온도 등의 품질 특성을 비롯한 다양한 형질을 분자표지(molecular marker)를 이용하여 효율적으로 선발하는 MAS (marker assisted selection) 육종이 벼에서 활발히 적용되고 있다(Jena and Mackill 2008). 또한 최근에는 개별 분자표지를 활용하는 MAS 육종에서 한단계 더 나아가 전유전체 상의 고밀도 유전자형 정보를 활용하여 육종집단의 성능을 예측하고 계통을 선발하는 GS (genomics selection) 육종도 시도되고 있다(Spindel et al. 2015).
유전체 분석 기술의 빠른 발달로 대용량 유전자형 데이터를 비교적 용이하게 얻을 수 있는 오늘날에는 표현형 데이터 확보가 MAS 및 GS를 활용한 육종 효율 증진의 제한요인으로 작용한다(Varshney et al. 2016). 특히 대부분의 농업형질은 기상조건을 비롯한 다양한 환경요인에 크게 영향을 받으므로 데이터 기반 육종의 정밀도 향상을 위해서는 표현형의 지속적이고 반복적인 평가가 필수적이다. 이러한 맥락에서 농촌진흥청 국립식량과학원에서는 국내 육성 벼 품종의 주요 농업형질을 연차 간, 지역 간 반복 평가하여 국내 벼 고품질 표현형 DB 구축을 진행중이다. 본 연구는 이의 일환으로 수원, 완주, 밀양에서 2018년과 2019년에 걸쳐 평가한 국내 육성 벼 297 품종의 출수일수와 간장 변이를 분석하였다.
국내 벼 품종은 상품성 유지를 위하여 서로 유사한 유전적 배경을 가진 우수 품종들 간의 교배를 통해 육성된 사례가 많으므로 유전적 다양성이 매우 협소한 것으로 평가된다(Mo et al. 2020). 그럼에도 불구하고 표현형 측면에서는 상당한 수준의 변이 폭을 보이는데, 본 연구에서는 평가된 지역과 연차에 따라 출수일수는 39~97일(2018년 밀양)에서 56~113일(2019년 수원), 간장은 51~115 cm (2019년 밀양)에서 59~134 cm (2018년 수원)의 광범위한 품종 간 변이를 보였다. 출수기와 간장은 모두 벼의 생산성과 품질을 결정하는 핵심적인 형질로 전세계적으로 많은 연구가 이루어지고 있다. 두 형질에 영향을 미치는 수백 개의 양적유전자좌(quantitative trait locus; QTL)가 보고되었으며, 최소 14개의 출수기 지배 유전자와 30개의 간장 지배 유전자가 동정된 바 있다(Bai et al. 2018, Hori et al. 2016). 반면 아직까지 국내 벼 품종 집단의 출수기와 간장을 지배하는 주요 유전자위와 대립인자 분포 및 표현형에 미치는 효과에 대한 정보는 체계적으로 정리되지 못한 상황이다.
따라서 국내 벼 품종 집단 내에서 출수기와 간장을 결정하는 주요 유전자 및 대립인자 효과를 파악한다면 유전적 거리가 먼 외래 유전자원을 동원하지 않더라도 이미 상품성이 검증된 국내 품종군 간의 교배를 통하여 원하는 효과를 지닌 대립인자를 적절히 조합하여 출수기와 간장을 정밀하게 조절(fine-tuning)하는 것이 가능할 것으로 판단된다. 본 연구에서 분석한 국내 벼 품종의 출수일수와 간장 데이터는 유전자형 데이터와의 연관분석을 통하여 각 형질에 영향을 미치는 주요 유전자위를 탐지하고 대립인자 효과를 평가하는데 활용되고 있으며, 데이터 기반 육종 플랫폼 구축에 유용한 자료가 될 것으로 기대된다.
염기서열 분석기술의 발달로 유전자형 데이터의 대량 확보는 보다 용이해진 반면 표현형 평가에는 여전히 많은 노력과 비용이 소요된다. 따라서 데이터 기반 육종의 정밀도 향상을 위해서는 신뢰도 높은 표현형 DB의 구축이 매우 중요하다. 농촌진흥청 국립식량과학원에서는 국내 벼 고품질 표현형 DB를 구축하고 있으며, 본 연구는 이의 일환으로 수원, 완주, 밀양 시험포장에서 2개년(2018~2019년) 간 평가한 국내육성 벼 297품종의 출수일수와 간장 변이를 분석하였다. 국내 벼 품종은 유전적 다양성이 매우 협소함에도 불구하고 상당한 수준의 표현형 변이를 보였다. 2018년 보통기재배에서 출수일수는 수원, 완주, 밀양에서 각각 48~104일, 46~111일, 39~97일의 범위를, 간장은 각각 59~134 cm, 55~122 cm, 57~106 cm의 범위를 보였다. 2019년 조기재배에서는 수원, 완주, 밀양에서 출수일수 범위가 각각 56~113일, 58~109일, 58~100일, 간장 범위는 각각 63~119 cm, 55~93 cm, 51~115 cm이었다. 2018년과 2019년의 이앙시기가 한달 가량 차이남에도 불구하고 연차 및 지역 간 시험품종의 출수일수는 고도로 유의한 정상관(r=0.90~0.98)을 보였고, 간장은 출수일수에 비하여 낮은 수준의 정상관을 보였다(r=0.45~0.82). 한편 출수일수와 간장 간의 상관계수는 유의하지만 매우 낮은 수준(r=-0.18~0.35)이었다. 서로 다른 환경에서 조사된 품종들의 형질 변이에 대한 AMMI (additive main effects and multiplicative interaction) 분석을 통하여 각 품종의 환경 안정성을 평가할 수 있었다. 본 연구에서 분석한 국내 육성 벼 품종의 표현형 데이터는 유전자형 데이터와의 연관분석을 통하여 형질 지배 주요 유전자위를 탐색하고 대립인자 효과를 분석하는데 활용되고 있으며, 육종 효율 증진을 위한 데이터 기반 육종 플랫폼 구축에 기여할 것으로 기대한다.
본문의 Supplementary Table 1은 한국육종학회지 홈페이지에서 확인할 수 있습니다.
KJBS-52-389_Supple.pdf
본 연구는 농촌진흥청(과제번호: PJ01357202)의 지원에 의해 이루어진 것입니다.
Fig. 1
Daily mean temperature in ordinary (2018) and early (2019) cultivations at three regions. SW: Suwon, WJ: Wanju, MY: Miryang.
KJBS-52-389-f1.jpg
Fig. 2
Days to heading, cumulative temperature from transplanting to heading, and culm length of Korean rice varieties in different environments. O: ordinary cultivation (2018), E: early cultivation (2019), SW: Suwon, WJ: Wanju, MY: Miryang.
KJBS-52-389-f2.jpg
Fig. 3
Correlation analysis of days to heading and culm length in different environments. DTH: days to heading, CL: culm length, O: ordinary cultivation (2018), E: early cultivation (2019), SW: Suwon, WJ: Wanju, MY: Miryang. ***: Significant at the 0.1% probability level.
KJBS-52-389-f3.jpg
Fig. 4
Correlation analysis between days to heading and culm length in six different environments. SW: Suwon, WJ: Wanju, MY: Miryang. **: Significant at the 1.0% probability level.
KJBS-52-389-f4.jpg
Fig. 5
AMMI (additive main effects and multiplicative interaction) biplots showing the mean (main effect) vs. stability (PC1) view of 297 rice genotypes and three environments on days to heading. SW: Suwon, WJ: Wanju, MY: Miryang.
KJBS-52-389-f5.jpg
Fig. 6
AMMI (Additive Main Effects and Multiplicative Interaction) biplots showing the mean (main effect) vs. stability (PC1) view of 297 rice genotypes and three environments on culm length. SW: Suwon, WJ: Wanju, MY: Miryang.
KJBS-52-389-f6.jpg
Table 1
Effects of variety, cultivation time, location, and their two-way interactions on days to heading and culm length from ANOVAz.
Table 1
Source of variance df DTH PVE CL PVE
SS F-value SS F-value
Variety (V) 296 143267 156.1** 0.571 76275 15.8** 0.555
Cultivation time (C) 1 77944 25130.1** 0.311 432 26.5** 0.003
Location (L) 2 18601 2998.5** 0.074 24454 751.2** 0.178
V×C 296 2311 2.5** 0.009 4542 0.9ns 0.033
V×L 592 4821 2.6** 0.019 19449 2.0** 0.141
C×L 2 2060 332.1** 0.008 2702 83.0** 0.020

zdf: degree of freedom, SS: sum of squares, DTH: days to heading, CL: culm length, PVE: proportion of variance explained

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Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments
Korean. J. Breed. Sci.. 2020;52(4):389-397.   Published online December 1, 2020
DOI: https://doi.org/10.9787/KJBS.2020.52.4.389
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Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments
Korean. J. Breed. Sci.. 2020;52(4):389-397.   Published online December 1, 2020
DOI: https://doi.org/10.9787/KJBS.2020.52.4.389
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Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments
Image Image Image Image Image Image
Fig. 1 Daily mean temperature in ordinary (2018) and early (2019) cultivations at three regions. SW: Suwon, WJ: Wanju, MY: Miryang.
Fig. 2 Days to heading, cumulative temperature from transplanting to heading, and culm length of Korean rice varieties in different environments. O: ordinary cultivation (2018), E: early cultivation (2019), SW: Suwon, WJ: Wanju, MY: Miryang.
Fig. 3 Correlation analysis of days to heading and culm length in different environments. DTH: days to heading, CL: culm length, O: ordinary cultivation (2018), E: early cultivation (2019), SW: Suwon, WJ: Wanju, MY: Miryang. ***: Significant at the 0.1% probability level.
Fig. 4 Correlation analysis between days to heading and culm length in six different environments. SW: Suwon, WJ: Wanju, MY: Miryang. **: Significant at the 1.0% probability level.
Fig. 5 AMMI (additive main effects and multiplicative interaction) biplots showing the mean (main effect) vs. stability (PC1) view of 297 rice genotypes and three environments on days to heading. SW: Suwon, WJ: Wanju, MY: Miryang.
Fig. 6 AMMI (Additive Main Effects and Multiplicative Interaction) biplots showing the mean (main effect) vs. stability (PC1) view of 297 rice genotypes and three environments on culm length. SW: Suwon, WJ: Wanju, MY: Miryang.

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Fig. 5

Fig. 6

Days to Heading and Culm Length Variation of Korean Rice Varieties in Different Environments

Effects of variety, cultivation time, location, and their two-way interactions on days to heading and culm length from ANOVAz.

Source of variance df DTH PVE CL PVE
SS F-value SS F-value
Variety (V) 296 143267 156.1** 0.571 76275 15.8** 0.555
Cultivation time (C) 1 77944 25130.1** 0.311 432 26.5** 0.003
Location (L) 2 18601 2998.5** 0.074 24454 751.2** 0.178
V×C 296 2311 2.5** 0.009 4542 0.9ns 0.033
V×L 592 4821 2.6** 0.019 19449 2.0** 0.141
C×L 2 2060 332.1** 0.008 2702 83.0** 0.020
Table 1 Effects of variety, cultivation time, location, and their two-way interactions on days to heading and culm length from ANOVAz.

zdf: degree of freedom, SS: sum of squares, DTH: days to heading, CL: culm length, PVE: proportion of variance explained

Table 1

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